Paligo’s new Markdown Import feature solves these challenges. The result is a migration feature that enables doc teams to automatically convert Markdown files into structured content in a single operation, without losing any existing structure or metadata.

Let’s dig into this new feature.

What is Markdown?

Markdown is a widely used format for creating technical content. Initially used by developers and engineering teams, its use is also growing rapidly among other teams in organizations, especially those focused on AI initiatives.

Markdown is a lightweight markup language that lets writers apply formatting with plain-text symbols. Due to its simplicity, Markdown files are rendered consistently across devices and platforms and are a natural starting point for many documentation teams.

If you’ve ever installed software, you should remember the README files that explained how to install it. That’s Markdown. Markdown is also used by developers to store code documentation (like API documentation) directly in the source repository.

The most common form of Markdown is GitHub Flavored Markdown (GFM), but many organizations have created extensions or customizations to the base specification (called flavors) to support granular formatting requirements.

Another reason Markdown is popular is that it’s easily consumed by AI tools. Anyone who writes prompts for LLMs like ChatGPT or Claude will recognize the Markdown format, as it’s used to provide additional context to the AI. Many AI tools also produce content in Markdown format.

The Challenges of Working With Markdown in Paligo - Before

Documentation teams that support Markdown and use Paligo faced challenges bringing this content into the CCMS. It was a completely manual, error-prone process in which writers had to copy and paste Markdown into a topic. Once pasted, they had to reformat the content and rebuild the metadata.

For example, a documentation manager at a SaaS company wants to migrate 300 pages of GitBook documentation, written in Markdown, to Paligo. The manager asks her team for an estimate, and they respond with six-plus weeks, which will involve copying and pasting the content into Paligo, manually restructuring it, and cleaning up the metadata. All that work has to happen before a single page is published. The manager deprioritizes the project.

Because of the effort involved with migrating Markdown and the risks associated with copy/pasting and making changes, Paligo was not widely adopted for Markdown documentation. Instead, teams work with multiple tools, each handling a different type of content. Those who continued to work to bring Markdown into Paligo’s CCMS invested significant time and effort and had to work around errors.

Now, consider this example: The same documentation manager uploads her GitBook export as a Markdown archive to Paligo. The import runs, and the content is automatically in Paligo’s editor, structure intact, ready for her technical writers to review and enrich. A migration that would have taken six weeks now takes an afternoon.

This is what Paligo’s new Markdown import feature does. It removes the barrier to migrating Markdown quickly and easily into Paligo’s component content management system (CCMS).

Introducing Markdown Import - How It Works

The markdown import feature supports both single-file and multi-file GitHub Flavored Markdown (GFM) in one operation. It’s not a simple file reader like some other import tools. Instead, it converts Markdown into Paligo’s native Docbook structure, mapping content to Docbook elements rather than storing it as raw text. The import process preserves semantic meaning wherever the two formats overlap and retains any available metadata, reducing post-import cleanup.

Initially, the Markdown Import function supports GFM, but the team expects to add new flavors and customizations as part of Paligo’s roadmap. The GFM import also supports most other flavors out of the box, with only minor gaps where a flavor has added significant features and the content uses those features.

With Markdown imported into Paligo, content can be reused, conditionalized, version-controlled, and published to multiple outputs, including HTML, PDF, XML, and Markdown (Paligo supports Markdown as an output as well).

Why You Want to Migrate Markdown to Paligo

Markdown is a popular format for technical content, but it has its drawbacks. It is inherently less semantically rich than Docbook, so you don’t have the semantic tagging you need for search or for AI tools. When you migrate Markdown into Paligo using this new Import feature, you preserve any structure and metadata that exists in the content, and you gain the possibility to add much more metadata than Markdown allows.

There are several reasons why migrating Markdown files to Paligo is useful. Customers who use AI to generate draft content can quickly import it into Paligo to leverage structured content.

For technical writers, it eliminates the blank-page problem. In the past, a tech writer would get a Markdown file, create a set of topics in a Paligo publication, and either copy and paste the Markdown content into the topics or write the content directly, reading from the Markdown file.

Now, thanks to the new import feature, writers get a clean foundation of content imported directly into the CCMS, with topics created automatically. The writer then opens the topics and makes any necessary changes, saving hours of manual reformatting.

It also enables developers to become a more integrated part of the documentation pipeline without having to learn to read or write structured content. They simply provide Markdown files to the doc team, and when they need to review content in Paligo, they can easily view it using Paligo’s Next-Gen Editor. No need to learn how to read or write structured content in Docbook XML.

Who else will find this feature helpful?

• Documentation Teams: Teams migrating from markdown-native tools have a clean, structured starting point without needing a big migration project.
• Documentation Managers: Managers who need to support Markdown as an input or output can now leverage the same tool (Paligo) that they use to manage all other documentation.

With the Markdown Import feature, writers and publishers increase efficiency, while documentation managers and team leads get a clear migration path that won’t cost a fortune and get teams up and running quickly. Not only does the import feature reduce migration effort, it also accelerates the onboarding of Markdown content into the content pipeline and speeds content publication.

The Benefits of Importing Markdown into Paligo

There are several important benefits of this capability that documentation teams will appreciate.

A One-Step Import

The import happens with a single step. There is no manual reformatting prior to import, and no existing structure is lost. This makes importing Markdown faster and more efficient, giving technical writers time to focus on more important aspects of creating and managing documentation.

Developers Have a Frictionless Way to Contribute

With more engineering teams starting to write documentation in code repositories using Markdown, this import feature ensures developers can continue working the way they want while also contributing to the documentation teams' work. Developers become part of the team that creates highly structured content and makes it available across more channels and outputs.

No Big Migration Projects

Finally, documentation managers don’t have to plan a big migration project; instead, they make migration part of their regular workflows. This is not only a cost-saving measure but also a productivity improvement. Markdown content gets into Paligo faster, so technical writers can work on it, and it gets published faster. In industries where technical content must change quickly to keep pace with product changes, this is a major win for doc teams.

Accelerate AI-Assisted Content Workflows

Many AI tools generate Markdown content by default, and many documentation teams are now using AI assistants to help them create initial draft content. Now, instead of having to copy, reformat, and restructure that content in Paligo CCMS, teams can migrate it into Paligo, where it’s converted into structured content and becomes part of a governed, structured content workflow.

Bringing Markdown into a Structured Content Workflow

Markdown remains a widely used format for technical documentation, but managing it at scale can create challenges around reuse, governance, and multi-channel publishing.

With Paligo’s new Markdown Import feature, those barriers are removed. The feature automatically converts Markdown into structured DocBook content, where it can then be versioned, reused, and published to multiple channels and outputs (including Markdown).

This feature not only supports technical writers who regularly work with Markdown content, but also enables closer collaboration with developers and engineering teams and saves documentation managers time and budget on large migration projects.

You can find out more about the new Markdown Import feature here.

Paligo’s Next-Gen Editor changes that.

Structured Authoring Without the Learning Curve

The Next-Gen Editor bridges the gap between technical writing and expert knowledge. The editor strips away the technical controls that confuse most non-technical users and replaces them with a familiar, intuitive, and easy-to-use user interface. If you’ve used word processing tools like Word or Google Docs, then this editor will feel familiar.

Imagine a collaborator or reviewer who struggles to view content, make changes, or provide feedback. Frustration often led to exporting a Word version of the publication, marking it up with edits and notes, and then sending the marked-up copy to the writer via email or Slack. The writer would then go into the content, make the required changes, and have them reviewed again. It was a continuous loop of frustration, increasing the time and effort for even the smallest changes.

The Next-Gen Editor eliminates this feedback loop by giving everyone access to the same content view, allowing them to add, review, or comment on content anywhere in the publication.

It’s a Familiar Interface

One of the biggest challenges for collaborators and reviewers is being able to quickly understand the context of the content they are adding or reviewing in a publication. The learning curve for navigating the old editor and knowing where to go made it harder to get started and stay focused on the actual work.

The new Next-Gen Editor for Contributors and Reviewers changes this immediately. The interface feels familiar from the start, making it easy to orient yourself and know what to do without a steep learning curve.

With the Next-Gen Editor, the entire publication is visible as a single continuous document, giving users the full context for where their edits are. Open a publication, add content, fix a typo, or add a comment. This new editor user interface was designed to make the work of collaborators and reviewers more efficient: no duplication, faster reviews, and improved consistency across large publications, while maintaining the structured content model, reuse strategy, workflows, and governance controls that organizations require.

Keep Conversations with the Content

Currently, in Paligo, content reviewers have their own editor for reviewing content. They open a topic or snippet, review it, and then add a comment to provide feedback to the writer. The writer then needs to open the topic and review the comments. It’s not difficult, but it is time-consuming because every topic has to be opened individually to see the comments.

The Next-Gen Editor includes threaded conversations, which enable reviewers to view the entire publication and add comments or @mentions anywhere in it. Paligo completely rebuilt its commenting engine to provide this capability because it understands the need to keep comments with the content for a complete understanding of a publication.

Threaded conversations maintain context and resolutions in one place and are directly connected to the source content.

3 Benefits The Next-Gen Editor Delivers

The Next-Gen Editor delivers three key benefits to organizations:

1. It enables direct editing without the complexity of traditional XML editors. Now, everyone in the organization can quickly and easily contribute content without a technical background, while maintaining the capabilities needed to manage enterprise documentation effectively. Subject matter experts feel comfortable contributing directly in the editor, eliminating the time-consuming, tedious back-and-forth that frustrated them to the point of not wanting to participate.
2. Modern threaded conversations make users feel as if they are working in a familiar word-processing tool, driving faster collaboration and review cycles. Easy to use, easy to collaborate, faster to get content created, reviewed, and published.
3. By showing the full publication in the editor, users have the full context of the content they are working on, ensuring accuracy across the entire publication. No more jumping between topics, trying to understand context. No more duplicating content because they didn’t know something was already covered. A single, contextual view shared by everyone.

Why This Editor is Important Now

Paligo is the only structured authoring tool that maintains the integrity of CCMS content while offering the lightweight feel of a modern web editor for contributors and reviewers.

This is an important distinction.

Today, many non-technical people are working hard to solve problems that structured authoring solves, but they lack the skills to use the tools that create structured content, so they don’t use them. With this new editor, Paligo has changed the story, empowering everyone in an organization to leverage structured authoring to create their content while also providing the seamless, contextual feedback loop they are used to with consumer-grade applications.

This democratization of structured authoring is critical for organizations that want to scale their enterprise knowledge and prepare their enterprise content for agentic AI. It also improves governance and drives operational efficiency.

Most modern editors simplify collaboration by abstracting away structure, but they haven’t gone as far as Paligo. Paligo took a different approach by rebuilding the core editor to make collaboration and review easier for non-technical users while preserving the integrity and scalability of structured content.

As organizations start scaling AI initiatives, structured content becomes even more valuable, but only if the people closest to the knowledge can contribute effectively. The Next-Gen Editor transforms Paligo from a siloed tool used only by technical writers to a company-wide collaboration hub that creates governed, reusable, AI-ready content at scale for the entire enterprise.

More Than a UI Redesign

The Next-Gen Editor represents more than a UI redesign. It expands structured authoring beyond the technical documentation team, turning enterprise content creation into a true collaborative operation across the organization.

Paligo is redefining how organizations create, manage, and scale trusted content across the organization at a time when structured content has become foundational to enterprise operations and AI initiatives.

A CCMS changes that, but only when the underlying content operations is designed intentionally. Structured content, governance, taxonomy, reuse, and publishing workflows aren’t administrative overhead. They’re the foundation for scalable, trusted knowledge operations.

Successful CCMS implementations require content operations that include best practices to help you understand your content and how best to create, manage, and publish it. At a recent KMWorld London event, Mark Pepper, former Information Architect and Project Manager at Paligo, shared these 8 best practices for content teams to follow when managing formal knowledge in a CCMS, and we’re sharing them here with you.

Why a CCMS for Formal Knowledge?

You use a CCMS to create, manage, and publish structured content. It follows a structured content authoring model that is more rigorous than what you would expect in a typical knowledge-sharing environment. This makes a CCMS more suitable for formal knowledge such as policies, standard operating procedures (SOPs), or official facts.

Yes, there are other tools, such as SharePoint, Zendesk, and ServiceNow, that can store and manage knowledge, but they don’t follow a structured content model. They also have repositories that are low to moderate in complexity, and for some companies, they are sufficient. But if you’re looking for an option that gives you the features and functionality needed to manage your formal knowledge in a way that makes it consistent, accurate across publishing channels, compliant, and well-governed, you need a CCMS.

Here’s a quick look at the best practices:

Let's look at each piece in more detail.

1. Conduct a Content Audit

The first step in building your strategy for managing knowledge is to conduct a content audit of what you already have. An audit helps you understand content structure and reusability, and makes it much easier to create other best practices (such as the Content Plan or Taxonomy Design).

Create a spreadsheet that includes the following columns:

• Publication Name
• Description
• Where it lives
• Who owns it
• Last Updated
• Duplicates/Variations (Are there copies of this publication? Note what they are, where they reside, and who owns them)
• Primary Source? (Is this the primary source of this content?)
• About your content: model, taxonomy, metadata
• Should this content be included in your plans?

The benefits of the content audit are multiple. It helps you identify all the content you need to include in your Content Plan, provides elements you use in your Content Model, and includes keywords for Taxonomy Design and Metadata.

Without a content audit to work from, you’ll spend a lot of time designing a content model that doesn’t work for the actual content you have.

Use AI to Do It Faster: AI can help you perform a content audit much faster and more efficiently, especially when you have large content sets. Point the AI at your internal knowledge repositories (e.g., network drive, Google Drive, SharePoint portal), and ask it to review each publication and complete the spreadsheet above. Direct the AI to perform the same work on any other repositories you have. Include in your prompt to note any duplicate content by adding an additional column called “Duplicate?” If the AI determines that the content is a duplicate of another item already listed, it can cross-reference the rows and flag them for the Information Architect to review. Having AI perform your content audit can reduce the time required from weeks to minutes or hours.

2. Outline the Publishing Model

The Content Audit feeds the Publishing Model, providing the list of publications you will manage in your CCMS.

The publishing model ensures every publication follows consistent structure, branding, formatting, and delivery rules across channels. The model defines all the elements needed for a publication, whether it’s a document, an HTML site, or something else. Each element is connected to a content element defined in the Content Model.

The Publishing Model tells you where each element should appear and where that information is managed. For example, the title page will have a title, a subtitle, a copyright notice, a byline, a logo, and a background image.

Along with defining the formatting and layout specifications of a publication, the Publishing Model maps all formatting rules to the Content Model elements. It also maps publishing types to include in the Taxonomy Design. Finally, publications are organized into a Content Plan.

Use AI to Do It Faster: Point your AI at a publication and have it scan and define all its elements, along with design parameters.

3. Define the Content Model

The Content Model defines how to use structured authoring elements as defined in an XML schema. It should be included in any templates you use. The model describes the content types to include in the Taxonomy Design, as well as the elements to include in the Reuse Model and the Content Standards.

Content teams with a strong content model can quickly create quality, structured content that follows your defined standards. When authors understand these rules, they work more efficiently and ensure the end user benefits from the design.

Use AI to Do It Faster: Use AI to map the elements defined in the Publishing Model and the Content Audit to the elements in the Content Model and Taxonomy Design. The AI can quickly review the contents of these models and transfer the required information to the next set.

4. Develop the Taxonomy Design

The Taxonomy Design blueprints the categories you plan to use in the CCMS. This is a hierarchical set of categories and the criteria for using each. A taxonomy starts with a top-level category (also called the root category), which provides high-level criteria for when to apply the category or one of its child categories (typically, there are up to four child categories). Each child is a more refined version of its parent.

Common categories include Audience, Product, System/Tool, and Information Type, and can get very large and complex for large publication sets. The Taxonomy Design also tells you the attributes and values to use in the Metadata.

Use AI to Do It Faster: Point your AI at your Content Audit to get a starting Taxonomy. Then have it scan the contents of your publications to recommend additional elements to include in the design.

5. Implement Content Management

You store and manage your publications in a content management system, in this case, your CCMS. The content team works with the CCMS and needs to understand its structure. In some cases, this will be a folder structure. In others, it’s a flat structure that uses metadata (or a filter/sort option) using established values.

Taxonomy and metadata are closely related and should share many of the same names. The best way to think of the two is that taxonomy is the blueprint, and metadata is its practical application.

Your Content Model and Taxonomy Design tell you the metadata values to apply to each content item.

Use AI to Do It Faster: Use AI to apply metadata based on the criteria you define in your taxonomy design. Your CCMS should include AI capabilities for adding metadata to content items as they are added or updated.

6. Build the Reuse Model

The Reuse Model defines the rules and guidelines for reusing content. Reuse is common when following a structured content model and using a CCMS. For example, you can use a piece of content in multiple publications, use a component (a combination of content elements) in multiple publications, or you can apply profiling and variable sets.

Reuse rules are important because reuse requires consistency to avoid content duplication. They also ensure the content team can effectively manage reused content sets.

Use AI to Do It Faster: Use AI to review your Content Audit, Publishing Model, Content Model, and Taxonomy Design, pulling the information together to establish reuse rules for your CCMS.

7. Create the Content Plan

Everything you have created so far leads to the second last best practice: a Content Plan. Your Content Planner is responsible for creating a Content Plan that identifies the audience (who the content is for), content goals (for each audience), content requirements (what content is needed to achieve the content goals), and public requirements for new and updated content.

The Content Plan references the content in the CCMS that should appear in each publication, and a content workflow defines who will create the content, review it for adherence to writing standards and technical accuracy, and any final review needed before the publication is released.

Use AI to Do It Faster: You can use AI to help you build out content plans. Point the AI at the content already in your CCMS and in your Content Audit, and have it build a plan. You can also use AI to automate the assignment and management of content in your workflow.

8. Document the Content Standards

The final best practice is to create content standards. Take every best practice you have completed so far and put them all together to define the rules authors, editors, and reviewers need to follow to create good content.

Consider this a living document that gets updated regularly as your organization’s documentation needs evolve. Make it a requirement to review the content standards every quarter so the information is always fresh in your content team’s mind. Also consider including checklists for each best practice and for key use cases to help writers and editors work more efficiently.

Use AI to Do It Faster: AI is very good at compiling and collating information. Give it access to all your Best Practice Models and have it create a document that pulls everything together in an easy-to-understand set of rules and guidelines.

Don't Forget the Governance Model

Underlying all the best practices we’ll discuss is the concept of governance. A governance model helps you map out who is involved in developing and supporting each best practice. It is necessary to ensure the best practices continue to meet the needs of your organization.

As your organization’s needs change, you’ll need to review your best practices and make any changes needed. You should also periodically review how the content team manages content. You can offer suggestions to improve how they work or identify improvements to the CCMSis configuration and administration.

To help you create this map of roles and responsibilities, create a RACI (responsible, accountable, consulted, informed) table like the following:

Note that each role may be supported by one or more people, and several roles could be owned by the same people. You may also call these roles by different names, so adjust the table as required.

Final Thoughts

We’ve covered the 8 best practices you need to follow to manage your content right in a CCMS. These practices help you design and implement a successful environment for managing all your formal knowledge. Can you operate a CCMS without the fine-tuning we discuss here? Yes, but to achieve maximum value, it’s important to configure it to meet your specific needs.

A well-planned and implemented CCMS helps reduce content duplication, lower translation costs, speed up migration to the CCMS, and get your publications to market faster. The AI suggestions can help you work faster and more efficiently, but remember to always review AI output and adjust where necessary.

Also, while the best practices we’ve shared here apply to using a CCMS, they can also be used to help you optimize the other tools mentioned above to some degree.

If you feel overwhelmed by the amount of work required to implement these best practices, consider taking a phased approach and start with one or two, such as the Content Audit and Content Model. Then build out to incorporate additional best practices.

The short answer Single source of truth (SSOT) is the practice of maintaining one authoritative version of every content component — a safety warning, a specification, a procedure — and reusing it everywhere it’s needed. When the source changes, every output updates automatically. In technical documentation, SSOT eliminates the “save as” workflow that causes version drift, duplicate translations, and release-day chaos. A component content management system (CCMS) makes this possible by shifting from document-based authoring to modular, reusable components authored once and published to PDF, HTML, mobile, and localized formats from a single source.

What is Single Source of Truth (SSOT)?

Let’s start with a real scenario. At Beijer Electronics, a global manufacturer of industrial automation solutions, the technical writer managed documentation for an entire portfolio — user guides, reference manuals, installation guides, release notes — across multiple product versions. All of it lived in large monolithic XML files built with Arbortext. Collaboration happened through email. The only way to publish was PDF via SharePoint.

When engineering found a specification that needed fixing, the question was always the same: which file is actually the right one? That question is the reason single source of truth exists.

DEFINITION Single source of truth (SSOT) means maintaining one authoritative version of critical content and reusing that single component everywhere it’s needed — rather than copying it into separate documents. When the source component changes, every output that references it updates automatically. No hunting. No version conflicts. No wondering which copy is correct.

Think of it like a database record. Your CRM has one customer profile that powers email campaigns, invoicing, and reporting. SSOT works the same way for content: one component powers multiple deliverables. Update it once, and every dependent output reflects the change.

For technical writers, this is the antidote to “save as.” Instead of duplicating entire documents across product manuals, you create modular content components that are authored once and published to whatever outputs you need.

The Problem: Content Fragmentation in Technical Documentation

Nobody sets out to fragment their content. It happens gradually, through necessity and the limitations of file-based tools.

You finish a product manual. Next quarter, a new variant launches. Rather than starting from scratch, you do what any sensible person would: create a copy, edit for the variant, move on. Then another variant. Another copy. Six months later, five versions of the same safety warning sit in five different files — and nobody’s sure which one was updated last.

Then engineering discovers a flaw in a critical specification. The panic sets in two hours before the deadline when someone finds the warning was updated in three manuals but not two others.

Which version is authoritative? How do we make sure all copies get updated before the release deadline? — The question every documentation team dreads hearing at 4 PM on a Friday

The localization team faces an even worse version of this. They’re translating the same content in dozens of separate files. Multiplied costs, multiplied review burden, plenty of opportunity for terminology to drift between products.

And underneath all of this: nobody actually knows which version is authoritative. Writers work in silos. A subject matter expert might approve a specification in one manual, completely unaware it’s been copied into twenty others.

WHY THIS MATTERS BEYOND EFFICIENCY Content fragmentation isn’t just a time problem — it’s a compliance risk. In regulated industries like industrial automation, medical devices, and aerospace, inconsistent safety information across documents can create genuine liability. SSOT eliminates this by ensuring every instance of critical content is inherently synchronized.

How Single Source of Truth Works in a CCMS

SSOT isn’t just a nice idea — it’s a practice that requires specific tooling. A component content management system (CCMS) makes it possible by shifting from document-based authoring to component-based authoring.

Instead of writing in monolithic documents, you author modular reusable components. Each one is versioned, stored centrally, and — critically — referenced by documents that need it. Not copied. Referenced.

THE KEY DISTINCTION True SSOT uses reference-based reuse, not copy-based reuse. Documents don’t contain copies of components — they point to them. The reference is resolved when you publish. Update the source, and every document reflects the change automatically. That’s the difference between SSOT and “save as.”

Here’s how it works: you create a component — “Thermal Specifications for Product X.” When Product Y launches with identical specs, you don’t copy the component — you reuse it. Update once, both manuals reflect the change.

Structured Authoring and Content Reuse — Deep dive into component-based architecture

A CCMS also gives you version control (like Git for content), variant management for conditional content, and multi-channel publishing to PDF, HTML5, mobile, and eLearning — all automatically.

Single Source Publishing — Multi-channel output strategies

Real-World Example: How Beijer Electronics Made It Work

CUSTOMER STORY How Beijer Electronics achieved 73% topic reuse across 463 publications — from fragile monolithic XML files to structured content with 53 active contributors.

73% Topic reuse

463 Publications

18% Image reuse

53 Active Reviewers

Where They Started

Beijer Electronics makes industrial automation and connectivity solutions. Their documentation was managed by a single technical writer using Arbortext for file-based authoring. Content lived in massive XML files with unreliable profiling. Email was the only collaboration channel. PDF via SharePoint the only delivery format.

What Wasn’t Working

The files were fragile. Even minor updates were time-consuming and risky. One XML error could break an entire publication.

Collaboration was a dead end. Engineers who spotted errors had to email the writer and wait. The process was so slow people stopped reporting issues.

One person held all the knowledge. A single writer with years of domain expertise — massive ramp-up risk if they left.

Nothing was reused, so nothing was consistent. Copy-paste meant branding, voice, and terminology drifted. Customers noticed.

What They Changed

Beijer moved from Arbortext to structured, topic-based content in a CCMS. They launched an HTML5 documentation portal alongside PDF, integrated single sign-on, and connected translation workflows for eight languages.

Most importantly, they brought product owners and engineers into the content workflow as active contributors. Technical writers shifted from sole authors to editors and quality gatekeepers.

Product owners take visible pride in their documentation. Engineers and reviewers contribute through inline comments instead of email chains. — Petter Bergeling, Beijer Electronics

The Results

73% Topic reuse

463 Publications

18% Image reuse

8 Languages

Multi-channel publishing from one source — PDF and HTML5 portal from the same content. Same-day turnaround on reviews. Translation consolidated across eight languages.

THE PART NUMBERS DON’T CAPTURE Writers gained confidence updating content without fear of breaking files. The support team shared direct links to documentation sections instead of emailing PDFs. Documentation went from afterthought to a product asset people were proud of. 53 reviewers actively contributing — compared to near-zero before.

Benefits of Single Source of Truth

What the Organization Gets

Faster Publishing Cycles — Modular content means assembling manuals from components. A variant that took 6 weeks can be done in 2. Beijer publishes 463 publications from a single source.

Reduced Translation Costs — 73% topic reuse means translating each component once, not per product. Across eight languages, the savings compound significantly.

Consistent, Accurate Outputs — One source, no drift. Critical for safety-critical information where inconsistency creates liability.

Improved Compliance — Full audit trail by default. Compliance reviews become faster and more reliable.

What Writers and Teams Actually Feel

Confidence Before Every Release — The “did I miss updating one copy?” panic goes away.

Shared Ownership — Product owners writing their own content, 53 reviewers engaged. Documentation stops being one person’s burden.

Less Busywork, More Craft — Version-conflict friction removed. More time for writing clear, useful content.

Documentation Earns Respect — Support sends direct links. Product managers reference it in sales conversations.

When SSOT Makes Sense (and When It Doesn’t)

We’d be doing you a disservice if we pretended SSOT is always the answer. Here’s an honest assessment.

Worth the Investment When:

• Multiple products share specs, procedures, or safety info
• Regulated industries need traceability
• Frequent releases mean constant updates
• 3+ languages make duplication expensive
• Long support lifecycles extend maintenance over years

Probably Overkill When:

• Single product, single manual — no reuse opportunity
• Narrative/creative content needing unique voice
• Very simple documentation (5-page quick-start guide)
• No localization requirements
• Team not ready for CCMS adoption

SSOT vs. Copy-Paste Content Reuse

This distinction matters more than most people realize — and it’s where teams fool themselves into thinking they already have SSOT when they don’t.

This was the heart of Beijer’s transformation. Old workflow in Arbortext: copy-paste. New workflow: reference-based SSOT. That shift enabled 73% topic reuse across 463 publications.

Implementing SSOT: Key Considerations

Content Architecture First

Before choosing a CCMS, figure out your component taxonomy. Which content types should be reusable? Map the dependencies. This audit is often as valuable as the implementation itself.

Choose Tools That Connect

A CCMS needs to integrate with your localization vendor, publishing pipeline, and review workflows. A cloud-native CCMS like Paligo provides authoring, version control, conditional publishing, and multi-channel output in one system.

What is a CCMS? — Understanding component content management systems

Invest in People, Not Just Software

Beijer succeeded by lowering the barrier to contribute, not raising it. 53 reviewers engaged because participation became easy, not mandatory.

Single Source Publishing — Team adoption strategies

Start Small, Prove It, Expand

Pick your highest-reuse product line. Implement SSOT there, measure, then expand. A pilot gives proof points and surfaces challenges early.

THREE THINGS YOU NEED Every successful SSOT implementation has: (1) a clear content architecture and component taxonomy, (2) tooling that supports component management and multi-channel publishing, and (3) genuine investment in team training and workflow change. Skip any one, and the project stalls.

SSOT and Localization: Translation Cost Impact

Localization is where the financial case for SSOT becomes hardest to argue against.

A 100,000-word documentation suite at €0.10/word costs €10,000 per language. If 40% is duplicated, only 60,000 words are unique. That’s €4,000 saved per language, per release. For eight languages with quarterly releases: €128,000 in annual savings.

40% Typical duplication

€128K Annual savings (8 langs)

1× Each component translated once

Beyond cost: SSOT dramatically improves translation memory efficiency and eliminates terminology inconsistency. Beijer consolidated eight languages in one system.

Content Reuse Strategy — Comprehensive guide to building a reuse framework

Getting Started with Single Source of Truth

If you recognize your own situation in this article, here’s a practical path forward.

Step 1: Audit Your Duplication

How much content is duplicated? What does it cost in translation, maintenance, and rework? If duplication exceeds 30% and you localize into multiple languages, the math favors SSOT.

Step 2: Map Your Component Taxonomy

Which products share which content? Where do they diverge? This clarifies your roadmap and gives you numbers for the business case.

Step 3: Evaluate CCMS Options

Look for component versioning, multi-channel publishing, localization integration, and ease of use for writers — not just administrators.

Step 4: Run a Pilot

Pick one product family. Implement SSOT. Measure reuse rates, time saved, and translation impact. Use those numbers to justify expanding.

Step 5: Train the Team — Then Lower the Barriers

Make it easy for people to participate. Beijer’s success came from reducing friction for contributors — not adding process.

Frequently Asked Questions

What’s the actual difference between SSOT and single-sourcing?

They’re closely related. Single-sourcing means authoring once, publishing to multiple outputs. SSOT emphasizes one authoritative version everything references. In practice, if you’re doing single-sourcing in a CCMS, you’re doing SSOT.

Can’t I just do this in Word or Google Docs?

You can try, but you’ll end up with copy-paste reuse, not true SSOT. Word and Docs don’t have component referencing or multi-channel publishing. The “save as” problem will come back as soon as you scale.

How long before we actually see results?

Pilot: 2–3 months. Full rollout: 6–12 months in phases. The content audit and taxonomy design — the part that makes everything else work — typically takes 4–8 weeks.

What if our team doesn’t want to change how they work?

This is the hardest part — harder than the technology. Success comes from making the new workflow easier, not harder. Beijer got 53 reviewers engaged by lowering barriers: SSO, inline commenting, and giving people ownership.

We’re in a regulated industry. Does this actually help with compliance?

This is where SSOT is strongest. Any industry needing version history, approval trails, and provably consistent safety information. A CCMS-based SSOT provides audit capabilities by default.

What about content that’s almost the same but needs small tweaks?

That’s what conditional content and variants handle. One component with rules: “Show this for EU markets,” “Use this spec for products after Q3 2024.” One source, multiple controlled variations.

Do we have to migrate everything at once?

No — and you shouldn’t. Start with your highest-reuse content. Prove the value. Then expand. Gradual rollout reduces risk and lets the team build confidence.

Is this just a way to sell us a CCMS?

SSOT is a methodology, not a product. You need a CCMS to implement it properly, but the principle — one authoritative source, reused everywhere — is what matters. We wrote this because we’ve seen the difference it makes. If our CCMS is the right fit, great. If another tool works better, that’s fine too.

About This Guide

Paligo Content Team — Technical Documentation & Content Strategy This guide was written by Paligo’s content team, drawing on direct experience helping organizations like Beijer Electronics transition from file-based authoring to structured content management. Paligo is a cloud-native CCMS used by documentation teams across industrial, medical, and technology sectors.

Fact-checked and verified: All statistics come from Paligo’s published customer case studies. Last reviewed: March 4, 2026.

Ready to explore content reuse? Book a demo to see how Paligo handles SSOT in practice.

Content Reuse Hub — Complete guide to content reuse, single-sourcing, and CCMS

As AI platforms become more widely used, the potential scale and costs of them malfunctioning also grows. But are certain platforms more associated with failures and harmful incidents? And are particular groups more affected?

For this research, we analyzed more than 1,400 incidents recorded on the AI Incident Database, and categorized them by the most common sectors, platforms and affected groups that appear in the incidents. These include a mixture of incidents.

Key findings:

• Information and communications is the dominant sector for harmful AI incidents (25%), with the four most common harm types being physical safety (20%), financial loss (16%), psychological harm (14%) and civil liberties violations (13%), which cut across all sectors.
• More than a quarter of all harmful AI incidents affect ordinary members of the public with no specific group targeted. However, when AI does harm one group more than another, race or ethnicity is the most likely differentiator for who gets targeted.
• Among named organizations, OpenAI (5%), Google (4%) and Facebook (4%) appear most frequently in documented harmful incidents, largely reflecting the scale of their deployment in AI.
• Nearly half of all documented harmful AI incidents (49%) involve software-only systems, including cases where AI-generated content errors in publishing and documentation caused direct financial and legal consequences.
• Social media platforms collectively appear in 19% of incidents where a system was implicated, with Facebook the most frequently implicated platform (4%) and ChatGPT the most frequently named LLM (4%).

IT, Transport, and Arts and Entertainment are the sectors most impacted by harmful AI incidents

Information and communications is the most dominant sector where harmful AI incidents take place, which spans social media platforms, search, advertising and content systems. One in five incidents recorded were within IT, which have included Facebook's On This Day feature surfacing painful memories including deaths and breakups to users uninvited and Amazon's recommendation algorithm found to be boosting its own products over more relevant results.

While many platforms and IT systems cause Information and Communications to rank as the most common sector for harmful AI incidents, many of these issues extend into money, health and civil freedom. Across all sectors, the four most common types of harm reported involved physical safety (20% of incidents), financial loss (16%), psychological harm (14%) and civil liberty violations (13%).

Other common sectors with heavy reporting of harmful AI incidents are Transportation and Storage (13% of incidents), and Arts, Entertainment and Recreation (11%) and Public Administration and Defense (11%).

The general public are targeted most often by harmful AI

More than one in four (26%) reported incidents of AI harm are non-discriminatorily targeting the general public, with incidents including Google's AI Overviews confidently providing incorrect and potentially harmful medical and geographic information to millions of users.

At least 13% of harmful incidents clearly target or put users of social media platforms at risk. One notable example in 2023 involved reports of Facebook's ad delivery algorithm showing job advertisements disproportionately to men, regardless of the advertiser's stated targeting preferences.

Many AI incidents reported carry implications for financial risk or losses among the general public, including a 2024 scam that exposed the use of AI-generated deepfake videos of billionaire Andrew Forrest to promote a fraudulent cryptocurrency investment platform on social media.

Many incidents of AI harm affect more than one group or demographic. Among incidents where a particular group is more heavily impacted, race is the most common differentiating factor, appearing in 16% of all documented incidents. Examples include Detroit Police wrongfully arresting a Black man based on a faulty facial recognition match, and a long-used kidney function testing algorithm found to systematically underestimate health risk in Black patients.

OpenAI, Google and Facebook are most frequently identified as deployers in reported harmful AI incidents

Among named organisations most frequently identified as deployers in documented harm incidents. Deployers are the party putting AI into use. OpenAI (5%), Google (4%), Facebook (4%) and Tesla (3%) appear most often. This largely reflects the scale at which their systems are deployed rather than confirmed liability for harm, and in each case the organisation was named in incident records rather than found legally responsible.

Tesla's incidents are distinct from the others, rather than platform or content failures, its appearances are concentrated in autonomous vehicle incidents where physical injury is the primary documented consequence.

A further 15% of incidents could not be attributed to any named organisation, with cases predominantly involving deepfake voice cloning and investment fraud where the party using the AI remained unidentified.

Software accounts for almost twice as many harmful incidents as all physical systems combined

The most prevalent form of harmful AI requires no hardware or physical object, with nearly half of all documented incidents (49%) involving software-only systems.

For companies managing structured content and technical documentation at scale, this presents operational risks. The AI embedded in the systems used to create content carries the same potential for harm as any other software..

One famous example of this from recent years was a $439,000 Deloitte report submitted to the Australian government found to contain fabricated academic citations attributed to AI use.

A further 15% of harmful incidents involve vehicles or mobile robots, which is the category most associated with physical injury and death. These include Tesla Autopilot fatalities and Waymo collisions.

Consumer devices account for 9% of incidents, with previous cases including Amazon's Alexa recommending a dangerous electrical challenge to a ten-year-old girl and Apple's Face ID failing to distinguish between identical twins.

Which platforms are implicated in harmful AI reports most often?

Among named systems where platforms were directly implicated in incidents, social media platforms collectively appear in 19% of incidents, which is more than any other system category.

Facebook is the most frequently implicated individual platform at 4% of cases, with incidents including its AI chatbots entering online grief and mental health support communities uninvited and responding as though human.

TikTok appears in 1.4% of incidents, including one where a lawsuit filed by seven French families alleging its recommendation algorithm systematically exposed minors to content promoting self-harm, eating disorders and suicide.

ChatGPT is the most frequently named LLM at 4.3% of all incidents. One case involved a child protection worker in Victoria, Australia that used it to draft a court report that introduced inaccuracies and downplayed risks in a live child safeguarding case.

Among other LLM incidents, Air Canada's customer service chatbot appears in the analysis after providing a passenger with incorrect bereavement fare information. A Canadian tribunal ordered the airline to pay damages, making it one of the first cases of a company being held legally liable for its chatbot's output.

5 ways to ensure your content accurately feeds into an AI system

1. Structure your content before it reaches AI AI systems process what they're given, so if your content is inconsistently formatted, poorly labeled or buried in unstructured documents, the output will reflect that. Structured authoring with consistent headings, metadata and topic-based organization gives AI systems the clearest possible input to work from.
2. Use single sourcing to eliminate version conflicts One of the most common causes of AI-generated errors is contradictory source material. If the same information exists in multiple versions across different documents, an AI system has no reliable way to determine which is current. Single sourcing, which means maintaining one authoritative version of each content component, will remove that ambiguity at the point of ingestion.
3. Maintain a clear taxonomy and metadata schema AI systems rely on context to interpret content correctly. A well-maintained taxonomy tells the system what a piece of content is, who it is for, and where it sits in relation to other content. Without it, the system is making inferences that may be wrong. In YMYL contexts, wrong inferences carry real consequences that can lead to the types of harmful incidents that our analysis explored in this article..
4. Review and validate AI outputs against source content AI-generated content should always be traced back to the source material it drew from. If an output cannot be verified against a specific content component, it should be treated as unreliable. Building a review step that explicitly checks AI outputs against structured source content is the most direct safeguard against the kind of hallucination and fabrication documented in the incident database.
5. Keep content current and retire outdated material Unsurprisingly, AI systems trained or prompted on outdated content will produce outdated outputs! In areas like medical guidance, legal information or financial advice, outdated outputs cause documented harm. A content lifecycle policy that retires superseded material is an essential risk management measure to avoid future harms occurring.

Methodology

Researchers at Paligo analyzed 1,406 unique incidents recorded in the AI Incident Database (correct as of March 2026). Incidents were categorized and ranked by the most common sector, harm type, harmed party, deploying organization and associated system using pre-classified taxonomies from the AI Incident Database, and supplemented by a keyword analysis of incident records where taxonomy annotations were absent.

All figures represent the percentage of incidents in which a given category, entity or system was identified — not the percentage of total harm caused, and not a measure of confirmed legal liability. Where organisations or platforms are named, this reflects how they were recorded in incident reports at the time of documentation rather than any finding of responsibility. Counts across some categories are non-exclusive, meaning a single incident may appear under more than one classification.

That delay matters. When documentation lags behind product releases, global customers wait for answers.

Paligo’s new AI Translations capability changes that process entirely. Instead of sending content outside the platform, documentation teams can translate topics directly inside their CCMS workflow and publish in multiple languages almost instantly.

What used to take weeks can now happen in minutes.

Why Translation Has Always Slowed Documentation

Many organizations that support a global customer base want to provide technical documentation in multiple languages, but it’s easier said than done. For most documentation teams, translation still depends on external vendors, file exports, and slow feedback loops.

First, you have to select a translation vendor and go through the process of implementing their platform into your documentation processes. Depending on how you manage your documentation today, the implementation process takes different forms.

If you use unstructured tools like MS Word or Google Docs, the translation process is completely manual. You send your documents to the vendor, and they return a translated version. If there were issues with the translation, you have to send the entire document back and request changes. If there is an update to the content, you have to send the entire document back and wait for the updated translation.

If you use a component content management system (CCMS), the integration might be seamless, or it may still require manual effort. Most organizations export their documentation as XLIFF, send it to the vendor for translation, and then import the translations back into the CCMS. This export/import process takes time, and errors often happen, such as broken XML structure, lost metadata, and versioning mistakes.

All of these challenges result in delays in properly translating the content and getting the translations into your CCMS. They also increase translation costs, not only from the translation vendor but also from the extra work your authors or translation managers do to ensure the translations are properly entered into the system and reviewed.

Paligo AI Translations: A Modern Approach

Paligo’s AI Translations turns translation into a native step in the documentation workflow. The new AI Translation enables you to translate content within your Paligo documentation workflows and release it simultaneously in multiple languages. No more copy and paste. No more exporting and importing XLIFF files.

For technical writers and collaborators, this means less work managing the translation process and faster, more efficient translations. For translation reviewers, it’s easier to review translations and make changes. And for documentation team managers, it means cost savings and a faster time to market for your documentation.

AI Translations enable a smooth, fast translation experience, allowing organizations to scale globally without the traditional bottlenecks of working with translation vendors or relying on manual translations. It’s about cost savings, productivity improvements, and faster time to market for your products with multi-lingual documentation.

Let’s take a look at how Paligo’s new AI Translations works.

How AI Translations Works Inside the Paligo Workflow

With AI Translations, Paligo makes translation native to the documentation workflow and is designed to be easy to use. There is nothing you need to do. Using an industry-leading third-party AI translation service, Paligo automatically translates content within your workflow. All you have to do is select the content to translate and indicate the languages you want it translated into. Currently, Paligo supports over 30 languages, with more on the way.

When you select the content to translate and the languages, Paligo analyzes the content size and provides a cost estimate before the translation is performed. If you approve the cost, you select to do the translation, and the content is translated and added.

Built-in Control With Glossary and Human Review

The translation facilitates machine translation post-editing (MTPE) by allowing “human-in-the-loop” review and approval directly within Paligo. The AI Translation service translates the selected content, and an author or contributor can then review the translation and make any necessary changes to ensure it’s accurate and on-brand. External specialists can also be invited to review translated content (e.g., translation vendor). Once approved, the translated content is now ready to publish.

Paligo creates an audit trail for all translated content, and you can download a translation usage .csv report from the Translations Dashboard to view in Excel.

Another key feature of AI Translations is the Glossary. The Glossary is designed to enable consistent memory used by translation providers in that it provides specific rules for translating words or phrases based on industry or organization-specific terminology.

We’ve looked at how AI Translations work. Now, let’s talk about the costs.

What This Changes for Documentation Teams

We talked about how AI Translations work compared to traditional translation vendors. Here's why Paligo’s approach is better.

Manual Friction is Eliminated

When documentation teams have to manage translations through copying and pasting, it’s easy to make mistakes or miss things. For those with a proper translation process set up in Paligo, they often export XML or XLIFF files, send them to a translation provider, and then re-import the translations. This process is still very manual, and it’s where version control usually breaks down.

With AI Translations, all this manual copy/paste, export/import is eliminated. Translation occurs within the content-creation workflow in Paligo. Authors don’t have to touch the content to send it for translation. Versioning is carefully controlled by Paligo, and complete audit trails show documentation teams what content is translated and when.

Modular Efficiency Means Faster Translations

Efficient content processes are something every documentation team wants. They need to publish content within critical timelines, either for new product releases, critical product updates, or to meet service-level agreements with customers. This is why structured authoring and content reuse are so important, including for translation management.

When you have to send all your content to a translation provider at once, wait for it all to come back translated, and then properly get that translation back into the system, significant time can pass.

With AI Translations, the process of translating content changes. Because Paligo uses structured, topic-based content, translations can happen incrementally. Teams no longer have to send entire documents for translation. Individual topics can be translated as soon as they are approved.

Have a topic written and approved? Send it for translation. Have a couple more ready? Send those for translation. As each translated topic comes back, a translation reviewer (an author or contributor) can review it, edit and approve it. This iterative review approach leads to faster translation reviews and overall translation. It also ensures version control is properly applied.

Cost Comparison: AI vs Traditional Translation

Traditional translation providers charge between $0.05 and 0.30 per word as a baseline. Translations are typically billed per word with three levels of rates: Exact matches (free to a few cents per word) Fuzzy matches (0.05 - .015 per word No match/new word matches (0.25-.30 per word)

When you use AI Translations with a human reviewer, you can reduce translation costs by 30-80% compared to working with a traditional translation provider. If you have no human review (i.e., use raw AI translations), those savings can exceed 95%.

If you opt for AI Translation without MTPE, rates can be lower depending on the nature of the content and alignment with the Glossary. Why would you not do a human review? For some companies, speed to market is prioritized over complete accuracy in translations. The idea is that they can publish documentation faster while still fixing mistakes with user feedback.

Paligo AI Translations uses a token-based model (1 token = 1 character). Customers purchase prepaid token packages (e.g., a small package is $50 per 1 million characters) that scale to support different translation requirements. New customers receive a free trial of 1 million characters.

Let’s put this into perspective.

An administration guide typically ranges from 160,000 to 170,000 words, or 400-600 pages (assuming 325 words per page). If we assume six characters per word for technical content, then 1 million characters equate to 166,000 words. So, using AI Translations, you would pay roughly $50 for an Admin guide using this model. Technical documentation tends to skew towards higher characters per word due to product names, command syntax, longer compound terms, and URLs/file paths.

Whether you use pure AI Translation or AI Translation with review, the costs are still significantly lower than using a translation vendor.

Here are those translation costs again: Workflow Type Estimated Cost (Per Word) Potential Savings Best For… Traditional (100% Human) $0.15 - $0.30 0% (Baseline) High-stakes legal, creative marketing, literature. Full Post-Editing (MTPE) $0.08 - $0.15 30% - 50% Customer-facing web content, technical manuals. Light Post-Editing $0.03 - $0.08 60% - 80% Internal docs, FAQs, high-volume help centers. Raw AI (No Human) <$0.01 >95% Low-risk content, user reviews, instant chat. Other factors that can increase cost savings include:

• Language pairs. Common language pairs (eg, English to Spanish) have higher AI translation accuracy, while rare pairs (e.g., English to Icelandic) require more human editing. Using the Glossary to match languages can help improve translation accuracy for less-common language pairs.
• KISS rule. KISS, or “keep it simple, silly,” means you write content in simple words and sentences. This isn’t always possible with technical documentation, but it is a common recommendation in all writing to ensure the content is easy for users to understand.

AI translation savings will never be 100% when there is a human-in-the-loop, because human review isn’t just a quick look. It’s a person who takes the time to ensure the content is accurate and on brand, which means you get:

• Contextual accuracy. AI often misses nuances or hallucinates terminology that doesn’t fit the industry
• On brand content. AI can be “bland” - humans inject brand tone of voice
• Responsible translation. For legal or medical content, humans take responsibility for accuracy and therefore take the time to review the translations properly.

Paligo’s AI Translations Compared to Other CCMS Vendors

Other CCMS providers offer AI translation capabilities, but what sets Paligo apart is how these capabilities are implemented in the platform. AI translations are tightly integrated into the documentation workflow; there’s no need to create a separate process to manage translations or go outside the platform.

Cost estimates are another important difference. Instead of finding out how much a translation costs after it’s completed, Paligo provides authors with an estimate up front so they know how much they are spending and can make appropriate decisions. And because translations are applied using tokens, documentation managers can more effectively track token usage and budget appropriately.

For customers who worry that AI translations are risky due to hallucinations and accuracy issues, both the Glossary and MTPE features provide more fine-tuned control. The Glossary helps the AI translate keywords and terms properly, while MTPE enables a human-in-the-loop by allowing humans to review and edit machine translations directly in Paligo to correct mistakes or ensure brand voice is applied.

When you are looking at AI translations from a CCMS provider, it’s important to understand exactly how the process works and if it provides the control you need to have confidence in the translation capability.

The CCMS Becomes a Global Distribution Engine

For many documentation teams, translation has always been a bottleneck. Content is written quickly, but global publishing slows down while teams wait for external vendors, manage XLIFF files, and resolve formatting issues.

AI Translations inside Paligo CCMS change that dynamic. Instead of sending documentation off-platform, translation becomes part of the content workflow itself. Topics are translated as they are written, reviewed by human editors when needed, and published in multiple languages without breaking structure or version control.

The result is faster documentation releases, lower translation costs, and a workflow that scales as products and global audiences grow.

This is where the modern CCMS goes next. Not just managing documentation, but powering global content distribution.

Complex interfaces and fragmented review workflows push subject matter experts (SMEs) back to PDFs and email threads.

Paligo’s new editor closes that gap: a modern, intuitive editing experience which keeps the full power of structured content.

A Story: When Documentation Collaboration Breaks Down

Max is a technical writer updating documentation for his company’s next product release. Jennifer is a Contributor with subject matter expertise. Together, the two need to get the documentation updated before launch, and the timeline is tight.

Max has made a number of changes to the documentation in the CCMS (component content management system). He now needs Jennifer to review and make any edits necessary. Plus, she has some additional sections to add, but isn’t sure where they should go.

Jennifer logs into the CCMS and tries to make her changes, but the editor feels nothing like the writing tools she uses every day. Frustrated, she gives up, exports a copy of the documentation as a PDF, marks it up with changes, noting where the new sections should go, and gives Max the new content to add.

Max now needs to review the PDF, identify the components to update in the CCMS, add the new sections, and enter the new content manually. When he copies and pastes Jennifer’s content - a list and an image - into a new component, the formatting breaks. The entire process takes twice as long as it should, and the risk of missed updates or broken content is real.

This story plays out on documentation teams everywhere. CCMSs were built to enable structured authoring and better collaboration – but the editing experience hasn’t kept pace.

Paligo is changing that.

The CCMS Promise and the Time-to-Value Gap

Technical documentation powers AI applications like support chatbots and product knowledge bases - and the quality of that documentation directly shapes the quality of the answers. To keep up, teams are turning to a CCMS. The promise is clear: structured content, managed at scale, ready for every channel. But there's a gap between that promise and how quickly teams can actually deliver.

A CCMS enables structured authoring and content reuse, reducing what writers need to create and manage while giving AI a reliable source of truth. Knowledge becomes a database of single-sourced components, not a scattered file system. That foundation supports translations, multi-channel publishing, and the grounding layer for trustworthy AI.

But the editors in most CCMS platforms are still too complex for writers new to structured content - and even harder for SMEs who simply need to review and approve.

A CCMS solves the scale problem, but it has left everyday authors, contributors, and reviewers behind.

Why The Editor Became the Bottleneck

Before building the new editor, Paligo listened closely to what customers were struggling with, and rethought the experience from the ground up.

Many customers are new to structured authoring, or XML, and find the editor poses a steep learning curve. For SMEs and other non-technical authors who also want to create or review content, the editor lacks the familiar, intuitive design of the word processing tools they use regularly.

The existing editor made onboarding difficult and pushed even experienced users into clunky workarounds. The new editor needed to feel familiar from the first login - powerful, but never requiring specialized knowledge to get started.

Copy-pasting content - formatted text, images, lists - meant hours of manual cleanup. The new editor needed to handle rich content cleanly, so writers could bring in work from other tools without breaking it.

And the review process itself was fragmented: suggestions and comments lived in separate workflows, forcing teams out of the editor and into email threads or marked-up PDFs. The fix: a single interface where authors, contributors, and reviewers all work together.

Paligo didn’t patch the old editor. It redesigned the entire editing experience, modeled after the tools teams already use every day.

Redefining What “Modern Editing” Means for Structured Content

Structured content can't stay locked behind XML expertise. As more teams depend on documentation - for onboarding, for AI, for scale - the people creating and reviewing that content need an experience that works for them, not just for specialists.

A modern editor for technical content starts by lowering the barriers to entry. Writers and reviewers should be productive from day one without needing to understand XML or content components.

But the editor isn’t just where writing happens. It’s where SMEs review and comment. It’s where non-technical contributors add content without breaking structure. And it’s where content becomes AI-ready.

The editor has to make structured content feel natural. Reading, editing, suggesting changes, incorporating feedback – none of it should require hours of training to use a complex platform effectively.

Paligo didn’t set out to build a better XML editor. It wanted to build a better content experience for every user.

But the editor isn’t just where writing happens. It’s where SMEs review and comment. It’s where non-technical contributors add content without breaking structure. And it’s where content becomes AI-ready.

The editor has to make structured content feel natural. Reading, editing, suggesting changes, incorporating feedback – none of it should require hours of training to use a complex platform effectively.

Paligo didn’t set out to build a better XML editor. It wanted to build a better content experience for every user.

Designing an Editor for How Teams Work

The next-generation editor in Paligo CCMS is designed to be efficient and reliable, removing the steep learning curves users faced around structured authoring and reviewing. It fundamentally improves how teams collaborate.

One view, full context

Authors and Contributors can now view the entire publication in a single view and select components to edit. Reviewers continue to see the full publication and can add comments.

The experience is modeled on familiar word processing tools everyone already knows. The content stays fully structured underneath, but reviewers never need to think about that.

Inline suggestions and comments without context switching

Previously, Authors had to open a separate editor to select and modify a component. So, they would see comments in one view and need to switch to another view to make the required changes. Now, they work within the full publication in the new editor, viewing comments and making changes without ever switching views.

Suggestions appear directly in the content - additions, edits, and deletions are all visible in context. Authors accept or reject each suggestion, and threaded comments keep the conversation alongside the content.

Eliminate the copy/paste nightmare

The new editor can handle the most complex rich content, speeding up the time it takes an Author to create and update content. Authors and Contributors can paste new content exactly where it belongs - with the formatting intact.

Back to Jennifer and Max: collaboration without friction

So how does this change things for Max and Jennifer?

Jennifer receives a link, opens the publication, and sees the entire document in a single view. She scrolls through the content, finding exactly what she needs to review and edit.

When she sees something that needs to be changed, she can use the suggestion tool to update the text. In some cases, she only wants to add a comment, which she can easily do from the same interface. Max can also now use the suggestion and comments tools within the editor.

As for the new sections she wants to add, both Jennifer and Max can now move to the location they want to a section and add them via a button in the toolbar.

Jennifer and Max now work in the same interface - creating, reviewing, and publishing from one place. It’s a remarkably faster and more reliable way of working.

Beyond a Better Editor: Building the Right Foundation

The new editor solves one of the biggest trade-offs in the component content management industry: powerful structured content versus modern collaboration.

Everyday writing and productivity tools are very good for collaboration, enabling multiple people to easily work on new content. But they do it at the expense of structure. These productivity tools generate unstructured content that can’t be reused without copying and pasting, leading to duplicate content that is hard to manage and govern.

Then there are tools, like some CCMS or XML Authoring tools that offer robust, XML-based structured authoring, but at the expense of collaboration. In some tools, there are completely separate interfaces for creating and reviewing content, resulting in what’s known as the “two UI problem.” Some use desktop tools to create content and send it for review in PDF format.

Paligo’s new editor delivers it all: structured authoring with built-in review and collaboration, while building the core knowledge infrastructure for the future.

Structured Content as Infrastructure for AI

Most CCMS providers bolt AI features onto old architecture. Paligo took a different path: rebuild the foundation first, then build intelligence on top.

You can’t build the future of content on yesterday’s authoring experience. - Rahul Yadav, CEO at Paligo

This new editor isn’t just a better XML authoring tool. And it’s not another bolt-on AI writing assistant.

Paligo is building the infrastructure first: a single, intuitive experience for every type of user, designed to integrate AI features seamlessly and securely.

They are building a platform that enables writers to create content faster and empowers SMEs to participate consistently. Documentation is created, reviewed, approved, and published to multiple channels faster, without compromising content quality or accuracy.

Customers depend on documentation to use products, answer questions, and troubleshoot problems. That’s the baseline.

But documentation now serves a second purpose: training data for AI applications and agents. Structured, accurate content gives AI reliable knowledge about how products work. Without it, hallucinations and inaccurate answers follow - a risk no organization can afford, especially in regulated industries.

What Comes Next

The new editor in Paligo CCMS isn’t just an interface update. It’s a deliberate shift in how structured content is created and sustained over time.

By unifying authoring, reviewing, and collaboration in a single, intuitive experience, Paligo removes one of the biggest barriers to effective documentation: friction between people, tools, and structure. Writers work faster. Reviewers stay engaged.

For content leaders, the impact goes beyond a workflow. A familiar editing experience means shorter onboarding cycles and less time spent on training. When contributors and reviewers can work confidently from day one, adoption stays high - and teams spend their energy on content quality, not platform friction.

And this foundation matters. High-quality, easy-to-use structured documentation isn’t only critical for customers - it’s essential for training reliable AI systems and agents. Without it, speed comes at the cost of accuracy and trust.

Improving the editor is just the beginning. Paligo is building an AI-native infrastructure that allows documentation to scale, adapt, and power what comes next.

Since the launch of ChatGPT, over 200 million organizations worldwide have adopted generative AI for their daily work, yet most still struggle to measure tangible returns.

Although Large Language Models (LLMs) are increasingly becoming embedded in everyday work processes, companies still struggle with where to deploy them effectively, how to communicate their value, and how to ensure those investments translate into meaningful returns.

MIT research suggests 95% of companies have seen zero return on their AI investments so far. A recent Financial Times analysis of S&P 500 company filings shows that major US firms struggle to describe any concrete benefits of AI within their organizations, even after building up hype around AI solutions in their earnings reports.

The success of adoption varies widely from one organisation to the next, but it’s clear that workers are actively experimenting with AI in hopes of unlocking further time-saving and productivity gains.

And while anyone can type a prompt into an LLM interface, the surge in ready-made prompts and automation templates that appear on sites such as AIPRM shows that people aren’t only using LLMs at a surface level; they’re also trying to systematize and scale their workflows in ways that go beyond ad-hoc prompting.

But which work-related tasks are we trying to automate the most?

Our research

To discover which work-related tasks are being automated the most, the team at Paligo analysed usage data by category from tens of millions of interactions on AIPRM’s library of public Prompts and GPTs. The patterns in this dataset reveal genuine user intentions through LLMs, showing what people are deploying generative AI to support in specific parts of their workload.

Key findings

• Article and blog writing is the most in-demand task for people to automate on LLMs, with more than 35 million uses of these prompts on AIPRM.
• Social media tasks (6 million uses) and marketing tasks (4 million) are the third and fourth most in-demand categories of work for automation.
• Midjourney is the most popular image generation tool that people want to automate, with over 2.7 million uses via AIPRM’s prompts.
• The vast majority of automation demand falls into eight broad categories: writing & communication, marketing & advertising, information & research, visual & creative generation, data & analysis, planning & business strategy, software & development, and customer interaction.

35 Million uses

Article writing (47.4%) dominates workplace automation, 5x more than any other task

What type of work do people want to automate the most with LLMs?

Large Language Models, such as ChatGPT, can provide answers within seconds for simple queries and prompts that require writing or grammar-related responses.

The ease with which they can produce articles, which are suitable for a range of mediums and goals, helps to explain why writing and communication-related automation tasks are by far the most in-demand.

Ranking first for automation demand is article and blog writing (35.3 million uses). Uses of prompts and GPTs for this task are five times more common than the second most common type of task: social media automation.

While article generation is clearly the most commonly automated task, the systemic consequences of this reliance are measurable and accelerating. A recent survey by Terzo and Viz Capitalist of more than 1,700 businesses found ‘inaccuracy’ to be the top risk identified above others such as ‘job displacement’ and ‘cybersecurity’. Alongside this, companies are already rehiring positions that were previously cut in order to fix major errors in content production that AI has caused.

The most popular writing prompt within our analysis, with 1.9 million uses, is described as a humanized, SEO-optimized, long-form article writer. Overall, 760 of the 5860 prompts and GPTs in our research sample mention writing as part of their output.

The temptation for businesses to assume LLMs can produce writing at scale with just a few prompts will have quietly fueled the growing ecosystem of prompts and GPTs utilized for writing needs.

But regardless of the level of ‘humanised’ content LLMs will claim to deliver, there is a severely understated and considerably wide gap in quality, accuracy and originality between the type of writing that LLMs can produce on any scale compared to a human writer.

AI writing often amplifies biases and stereotypes that humans can recognize and avoid. It frequently produces false or misleading information because it generates text without verifying facts. LLMs don't think or judge the way humans do, but they can apply context and follow ethical guidelines based on their training.

Writing-related tasks appear more than once in the top 10 most in-demand prompt automations, with creative & scriptwriting, as well as marketing copy, each being accessed more than 1.4 million times.

Creative and scriptwriting prompts support users in developing storylines, dialogue, and character profiles. Marketing copy is being generated to support the creation of optimised messaging across multiple marketing channels.

The most automated writing tasks with LLMs

Beyond the top three most commonly automated writing tasks that also appear in the most popular prompt list across all categories, we can see more specific use cases for writing that attract millions of users.

Call-to-action (CTA) prompts (1.2 million uses) are the third most popular for writing automation, which involve the production of short, persuasive pieces of copy designed to drive clicks, conversions, or user response on web pages and applications.

Editing and proofreading prompts see more than 360,000 users looking for a greater automation solution. This lower use case likely stems from the ease with which users of LLMs can paste existing copy straight into a chat window and request fixes directly, making specialized templates feel unnecessary.

But where LLMs struggle with longer, more complex documents, structured prompts and GPTs have more appeal under the assumption they can handle edits more consistently at scale. However, there is no evidence that the rate of hallucinations from LLMs can be eradicated regardless of the quality of a user’s prompt.

Although it only sees 24,106 uses, academic writing prompts are the tenth most popular writing task for automation. According to research from the Higher Education Policy Institute, the proportion of higher education students using generative AI to create material for assessed work increased from 54% in 2024 to 88% this year.

Issues surrounding accuracy in generative AI output do not escape the academic world either. With mounting evidence of LLMs providing advice that endangers users, OpenAI revised its policy in October 2025 to limit ChatGPT’s ability to give licensed professional advice, which includes medical or legal guidance.

A UNESCO survey of HE institutions also shows that enthusiasm is tempered by uncertainty, with more than half of respondents admitting they feel hesitant about using AI effectively in teaching or research, and one in four reporting that their institutions have already faced ethical issues, from student overreliance to authorship disputes and biased outputs.

Similarly, a 2024 study has shown that an overreliance on LLMs for students is linked to a weakening of their critical thinking, decision-making, and analytical abilities.

Social media and marketing-related tasks

Written content is the new factory line. Prompts for social media content creation were used over 6 million times. The sheer volume reflects how workplaces are under increasing pressure to produce more social-friendly assets and copy, with LLMs supporting their process.

AI-generated content has already surpassed human output in terms of volume. A study released by Graphite in October 2025 revealed 50% of new written content on the internet is likely produced by AI.

Among marketers, SurveyMonkey reveals that more than half in the sector use AI to optimize content, and over 40% rely on it to create material, brainstorm ideas, support social media strategies, and analyze data.

Further down the ranking, tasks such as link-building & outreach (372,350 uses), audience segmentation (163,215), and media channel selection (69,612) collectively paint a picture of people trying to outsource the more strategic aspects of marketing.

These activities are being used hundreds of thousands of times within our dataset. They are tasks that traditionally rely on judgment, nuance, and experimentation, yet automation is being asked to shortcut that process. Whether these tools are enhancing strategy or quietly flattening it into something generic remains uncertain. Still, the volume of usage suggests many workers are betting that AI can shoulder a significant amount of the marketing workload.

Midjourney prompts are the most popular generative media tool for automating design tasks

A large share of activity in our research shows people using LLMs as a prompt-creation engine for other generative media systems. The most common of these is Midjourney, with more than 2.7 million uses of Midjourney-focused prompt templates in our research.

2.7 Million uses

Midjourney (3.6%) is the most automated image generation tool, workers use LLMs to craft better prompts

This reflects both the popularity of the model and shows that users rely on LLMs to craft more precise or stylised instructions before pasting them into Midjourney.

One of the most widely used Midjourney helpers, DishPrompt, has over 4,700 uses and generates visually rich food image prompts from recipe descriptions. It’s estimated that over 15 billion images have been created through generative AI since 2022, and major brands now incorporate AI media generation into their major campaigns, such as Coca-Cola’s most recent Christmas TV commercials in 2024 and 2025.

Stable Diffusion (439,545 uses) and Leonardo AI (372,762) rank second and third for prompt usage. One widely used Stable Diffusion template has been run more than 50,000 times and offers five stylistic variations from a single keyword.

The decrease in popularity for platforms like Sora, Flux, and Firefly suggests that newer or more specialized systems haven’t yet built the same level of everyday utility or community momentum, even if their underlying performance is comparable to more popular models.

Audio-first tools, such as AudioCraft (40,681), rank lower in terms of usage, with generative audio use cases being more niche compared to the broader appetite for visual creation.

Other notable work tasks with high automation demand

Prompts that assist with data and analysis have been used more than 2.6 million times on AIPRM. These tasks range from modelled testing to spreadsheet cleaning and data engineering.

Tasks that require support with customer interaction, such as draft responses and CRM strategy, have collectively seen more than 1.3 million uses.

The automation of business planning and strategy has also seen over 2.3 million uses, which includes prompts and GPTs to help with startup ideas, product naming, and descriptions, as well as scaled pricing recommendations.

Generation needs are evolving, but is it improving?

The results of our analysis show that many workers aren’t just experimenting with AI anymore; they’re reorganising core tasks around it. From writing and marketing to design, research, and strategy, the demand signals mapped across AIPRM show where generative-ai is already supporting day-to-day needs.

Even while this shift accelerates, it remains crucial that generative-ai is inclusive and maintains standards of quality, ensuring it augments human work rather than replacing it with a cheaper, blander version of itself. While our research indicates demand for automation in many areas of work, the quality at which AI can complete those tasks requires more regular evaluation.

Looking ahead, the rise of agentic AI, where systems that can plan, take actions, and complete multi-step tasks autonomously signal an even greater evolution from prompting tools to true work automation. This is where AI won’t only assist our workflows but will increasingly be able to run them end-to-end.

74.3 Million uses analyzed

Spanning 56 work categories and 5,000+ prompts

Why technical documentation cannot afford to rely on LLMs

We set out to produce this research in order to raise awareness of how millions of people are relying on inaccurate, error-strewn AI models to produce or assist them with work-related tasks under the impression that the output will be comparable to that of a professionally trained human. Unfortunately for those buying into the prompt and GPT marketplace, that isn’t the case.

Like many roles, technical documentation writers demand quality and accuracy. Their responsibilities have shifted toward gathering, collating, and organizing factual, accurate information. This is ingrained in their role, and there's a high level of trust that they deliver on that promise.

Here are more detailed reasons why LLMs can’t deliver on the needs of technical documentation writers:

• Accuracy is essential: Documentation errors impact product usage, customer success, and organizational liability. Unlike marketing copy, where approximation can suffice at times, technical content errors cascade into customer integrations, compliance violations, and support overhead. OpenAI's October policy change, banning medical advice due to liability concerns, demonstrates that even AI providers recognize certain content domains cannot tolerate the error rates inherent in automated generation.
• Updates must cascade systematically: A single software update, regulatory change, or product modification often requires coordinated updates across hundreds of help articles, user guides, and API references. Content generated via standalone prompts lacks mechanisms to track where information is used or to ensure updates propagate correctly.
• Version control and audit trails are required: Regulated industries demand documentation showing who changed what when. Teams need to revert changes when updates introduce issues. Content generated by external AI tools lacks inherent versioning or change history. Accountability often points towards hallucinations.
• Content must be structured for reuse and AI consumption. Technical documentation follows single-source principles. The same component appears in web help, PDF manuals, in-app tooltips, and increasingly, as structured data consumed by AI agents like kapa.ai to answer customer questions. Unstructured generation creates redundancy, inconsistency, and content that AI systems cannot reliably parse.

Research methodology

Researchers at Paligo set out to discover what work tasks users of LLMs are trying to automate the most. They analyzed more than 5,000 of the most commonly used prompts and GPTs posted to AIPRM.com, a site where users share prompt templates for millions of others to use and integrate into their own ChatGPT and other generative AI workflows. They manually assigned these prompts and GPTs by their title and description into a maximum of three separate categories before summing together the usage of each category. The final analysis compares 74.3 million uses among 56 categories of work tasks. Data current as of November 2025.

For a full glossary of our AI prompt categories, click here.

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Our research reveals a critical gap: 35 million use-cases automate article writing through prompts. However professional documentation requires more than speed; it needs structure, quality control and governance.

What Zendesk calls contextual intelligence is the same foundation that structured, technical documentation has been built on all along.

Zendesk is talking about AI for customer experience, but the same holds true for other AI-powered use cases. If AI needs unified, contextual knowledge, then structured content is the only viable foundation for modern AI and multi-channel publishing.

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AI Exposes the Limits of Unstructured Documentation

The days of writing technical documentation as static, chunky, downloadable Word documents or PDFs are over (although yes, some companies still want those PDFs, too). Unstructured content created with MS Word, Google Docs, Adobe InDesign, Confluence, and Zendesk Knowledge, cannot support the kind of agentic AI that vendors are promising. Companies need their documentation to be easily ingestible by large language models (LLMs), and PDFs and other static, unstructured content creation tools are not the answer.

Unstructured content mixes content and formatting, is not reusable, and doesn’t scale. If you’re managing multiple copies of a document to support different product versions or different audiences, then you run the risk of having out-of-sync versions of your documentation, inconsistent information across versions, and a lot of content duplication. These challenges have an impact on how your documentation is ingested by LLMs.

LLMs can ingest unstructured content, but when you choose this route, you face a number of challenges:

• AI systems work inefficiently. When information is messy or inconsistent, models require significantly more computational effort to interpret, often leading to uneven analysis and weaker data extraction.
• Trouble understanding relationships. Without standardized formats, AI can’t reliably recognize how different pieces of information relate across documents (or even document sections).
• Ambiguity increases error rates. Unstructured content lacks metadata and often doesn’t follow predictable patterns or structures. This leads to inaccurate inferences or misinterpretations that only get worse as more content is processed.
• The chunking problem gets worse. Because large language models must break data into smaller sections to process it, inconsistent or disorganized content often gets split in ways that separate essential context, reducing accuracy and reliability.

With unstructured content, there are challenges dealing with all the different information formats, such as images, tables, and scanned documentation, that also don’t contain metadata.

The key point here is that unstructured content does not provide the context AI needs to truly understand it. But structured content does.

What Structured Content Actually Enables (That AI Depends On)

Structured content separates content from formatting, uses topic-based authoring, can be reused across formats and channels, and scales. What has become a technical documentation best practice is also a foundation of AI.

So, how does structured content improve AI?

Structured content allows AI to learn patterns more quickly and accurately. By breaking information into self-contained, consistently formatted topics, structured authoring gives models predictable signals using clear headings, standardized sections, and logical organization. Instead of wasting processing power trying to understand the layout, the AI can focus entirely on understanding meaning.

Taxonomy and metadata make context explicit. Controlled vocabularies, tags, and metadata clarify concepts, entities, and relationships, improving tasks such as categorization, topic modeling, and pattern recognition.

Relationships stay intact during processing. When LLMs break information into chunks, structured content naturally preserves hierarchy and context. Sections, subsections, and headings keep related ideas together, reducing the risk of misinterpretation.

Processing becomes far more efficient. Formats like XML or JSON provide models with a defined structure for parsing, making entities, attributes, and relationships easier to identify. This improves accuracy while reducing computational load.

Outputs become consistently reliable. Because structured content limits ambiguity, models generate more precise responses, which is critical for regulated industries like healthcare, finance, and legal, where accuracy is non-negotiable.

“All the things we do to make content better for humans – componentize it, structure it, clean up the grammar, tag it with metadata – make content better for machines as well.” Val Swisher

Structured content also integrates with external databases, ontologies, and RAG (Retrieval Augmented Generation) workflows. This enrichment strengthens the model’s knowledge base and provides users with more context-aware, accurate information.

AI Doesn’t Need More Content. It Needs A Unified Knowledge Framework

Building on Zendesk’s vision of agentic AI, we need to understand that agentic systems do more than retrieve information. AI isn’t magical, but it also doesn’t simply index content and return keyword-based responses. AI has to understand the meaning of content, evaluate relationships among content topics, make decisions based on content rules (such as filtering and conditional text), and personalize outputs.

If you’re forcing AI to work with unstructured content like a Word or Google document, or an HTML knowledge base article, it will struggle to do these things.

What you want instead is to build a unified knowledge framework that maps out how to create, manage, and store technical documentation. The framework includes a shared structure and language, taxonomies and metadata, consistent terminology, and content formats.

To implement this framework, companies are turning to component content management systems (CCMS) that support structured authoring, reuse, metadata, relationships, and multi-channel publishing, the things that help AI understand content better.

A CCMS provides the technical environment needed for the contextual intelligence that Zendesk talks about. From a documentation perspective, we can take Zendesk’s four pillars of contextual intelligence and break them down this way:

• Structured data: A CCMS provides topic-based authoring, metadata, and clear content models.
• Historical context: A CCMS provides a single source of truth for all documentation, ensuring you have the historical context needed. It also provides version control and auditing to track changes to documentation over time.
• Dynamic signals: With the right CCMS, you can define metadata, use variables, and apply dynamic filtering via profiles and conditional content to support the right person and context.
• Policies and governance: With a CCMS, you can apply review and approval workflows, controlled vocabularies, and reuse rules, all at the topic level.

AI Has Made Multi-Channel Publishing Non-Negotiable

Multi-channel publishing and AI readiness aren’t separate outcomes. They share the same foundation.

Today, technical documentation needs to live everywhere: online help centers, in-app help, PDFs, customer portals and knowledge bases, chatbots, AI assistants and agents, and more. AI may be at the top of the conversation list, but it’s still one of many channels.

When you work with unstructured content, you are rewriting your documentation over and over for each channel. Or you’re cutting and pasting and dealing with manual updates. The process is time-consuming, error-prone, and can be expensive over time.

Structured content is the most efficient and effective approach for multi-channel publishing. Even if you are only publishing to one channel today, by following a structured content model, you are future-proofing your content for any new channels that come.

This includes multiple AI-related channels. Remember, AI cannot generate channel-specific responses (e.g., search, chatbot, assistant, agent) if the content isn’t inherently channel-agnostic and well-structured for reuse.

Why Governance Matters More Than Ever

It’s worth taking a minute to talk about the importance of governance in the AI era. AI applications and agents amplify your content framework for better or worse. So, if your content is unstructured and messy, then your AI will be messy at scale. But if your content is well-governed, your AI will be much more reliable at scale and will be considered more trustworthy.

What governance capabilities are important? An up-to-date content model that truly reflects your business is paramount. You also need clear roles and responsibilities for managing your content, along with content standards and style guides. There are more capabilities that you can read about here.

In terms of AI-specific governance capabilities, the most important thing is providing the right documentation set for training the AI. If you train your AI on outdated, inaccurate, or inconsistent content, the responses provided will not be correct.

In addition, because generative AI creates new content based on your documentation, you’ll want to train it on your brand and editorial style guides to ensure responses follow those standards.

A CCMS provides a number of governance capabilities that ensure your content is AI-ready, including:

• A single repository for all technical content
• Review and approval workflows
• Versioning
• Auditing
• Taxonomy and metadata

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How Paligo Complements Zendesk’s Vision for Contextual Intelligence

If contextual intelligence and AI-driven customer support are becoming essential (and they are), then Zendesk is only part of the solution.

Zendesk is a customer support platform that includes a knowledge base. But the knowledge base is not built on a structured content model. It’s designed for simple articles with limited structure, metadata, and taxonomy. Each article is a standalone document. There is some limited reuse through content blocks, but they don’t provide the true reuse that a CCMS like Paligo does.

Because the knowledge base is unstructured, AI systems will struggle to understand relationships, reuse information, or maintain context across versions, languages, or product lines.

Paligo CCMS can fill these gaps by providing content that is modular, structured, taxonomy-driven, and enriched with metadata. When content is clearly structured, AI can reliably interpret relationships, maintain context during retrieval, and produce more accurate answers in customer-facing experiences.

Paligo also optimizes versioning, variant management, and multi-channel publishing, giving teams a single source of truth that, when integrated, feeds Zendesk clean, consistent, well-organized content.

With this partnership, Paligo becomes the authoritative content engine behind Zendesk, while Zendesk becomes the delivery layer that incorporates AI assistants and agents.

Structured Content Isn’t Just the Best Path. It’s the Only Sustainable One.

AI has raised the stakes for technical documentation. What used to be “nice to have” things like consistency, structure, metadata, and reuse are now the backbone of every AI-driven experience. Multi-channel demands have multiplied, customer expectations have hardened, and Zendesk’s own message confirms what the industry has known for years: without structured content, modern documentation simply can’t keep up.

Unstructured articles and isolated knowledge bases won’t give AI the contextual intelligence it requires. Only structured, topic-based content provides the stability, clarity, and scalability needed for reliable AI performance across every channel, whether it’s human or machine.

The teams that embrace structured content now aren’t just preparing for AI; they're embracing it. They’re building a knowledge foundation that grows with the business, supports every new channel, and strengthens customer trust over time.

We invite you to learn how you can bring together the strengths of Paligo CCMS and Zendesk to create the customer experiences your customers demand.

FAQs

Q1: Why is unstructured content such a problem for AI?

Unstructured content makes AI work much harder than it should. When information is inconsistent, mixed with formatting, or spread across multiple versions, AI systems waste processing power trying to interpret the structure instead of understanding the meaning. This leads to weaker accuracy, higher error rates, and broken relationships between concepts once the content is chunked into smaller sections. AI can still ingest unstructured docs, but you pay for it in inefficiency, inconsistency, and degraded output quality.

Q2: What does structured content actually unlock for AI?

Structured content gives AI what it needs most: clarity, consistency, and context. Topic-based authoring, predictable structure, and rich metadata help AI models understand relationships, hierarchies, and intent. This reduces ambiguity, preserves meaning during chunking, and dramatically improves accuracy. Instead of deciphering messy layouts, the model can focus on interpreting information—making every downstream task faster, more precise, and more reliable.

Q3: How does a CCMS like Paligo support contextual intelligence?

A CCMS provides the unified knowledge framework AI depends on. It brings structure, metadata, versioning, relationships, and governance into one place. When mapped to Zendesk’s pillars of contextual intelligence, a CCMS delivers:

• Structured data through topic-based authoring and metadata
• Historical context through version control and a single source of truth
• Dynamic signals through profiling, variables, and conditional content
• Policies and governance through workflows, controlled vocabularies, and reuse rules Together, these capabilities give AI the stable, contextual foundation it needs to deliver accurate, personalized responses at scale.

Q4: Why does structured content matter for multi-channel publishing—and how is AI changing the stakes?

Multi-channel publishing used to be a convenience. AI has turned it into a requirement. Structured content allows teams to create once and reuse everywhere—HTML, PDFs, portals, chatbots, assistants, and AI agents—without rewriting or duplicating information. Unstructured content can’t scale this way without constant manual effort. Because AI relies on channel-agnostic, consistent content, structured content becomes the only sustainable way to support today’s mix of human and AI-driven experiences.

But sometimes the desire to streamline tools results in a loss of critical capabilities for one or more teams. This can be the case when a company selects Zendesk to manage technical content.

In this blog, we’ll examine Zendesk’s capabilities for creating and managing technical documentation, where it falls short, and when to know you need a tool purpose-built for technical content.

Let’s get started.

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Zendesk: Built for Customer Support, not Technical Documentation

If you are looking for customer support software, Zendesk is typically at the top of the list. Zendesk is a customer support and engagement platform that helps companies manage and streamline customer interactions across multiple support channels, including messaging, live chat, voice support, and more, consolidating them into a unified experience. The ticketing system consolidates requests across multiple channels, providing support agents with a single place to track, manage, and resolve issues.

Zendesk also includes self-service capabilities, such as a Help Center (knowledge base), FAQs, and community forums. It’s the knowledge base where some companies plan to manage their technical documentation.

Can Zendesk Double as a Documentation Tool?

The Zendesk Knowledge Base can be used to create and manage technical documentation, and for some companies, it’s a good fit. It’s available starting with the Zendesk Suite plan and above, but many advanced features are only available in the Enterprise plan, so if it is on your list to evaluate for technical documentation, make sure you know what plan provides the features you need.

Article-Based Content Creation: A Limiting Approach for Tech Docs

The knowledge base has a basic content architecture. You define categories and, within each category, you create sections. Neither categories nor sections have content; they are simply organizational structures. It’s within a section that you begin to create content in the form of articles.

Articles are page-based (similar to Microsoft Word or Google Docs), and include a mix of content and styling. You can create articles directly in Zendesk using the WYSIWYG editor. In some cases, teams will create the content in an external authoring tool such as Word, Google Docs, or rich text and then cut and paste it into the Zendesk article editor.

You can create a site-wide theme for styling your articles, but you can also edit an article’s HTML source directly. There is, however, a limit on the HTML tags you can apply. If you choose to make style changes to an individual article, you’ll have to manually track those changes.

When you want to work with others on your content, you need workflow capabilities. There is a review and approval workflow in the Enterprise Plan, along with the ability to schedule articles to go live.

You can track changes made to your articles using version history, as well as all activities performed in relation to an article. You can restore a previous version of an article, but not individual revisions to it. If your article included one or more content blocks, those blocks become unlinked, and their content is placed inline with the rest of the article. Versioning is supported in Professional and Enterprise plans.

Metadata: Helpful, But Not Built for Docs

To apply tags and labels to your content, you will need a Pro or Enterprise plan. Tags are used to support the “related to” widgets in the Zendesk Help Center, while labels are keywords or phrases used by search, chatbots, or to create lists.

Limited Content Reuse: Where Zendesk Falls Behind

Zendesk’s Knowledge Base does provide some content reuse capabilities in the Enterprise plan. You can create content blocks, which can be a paragraph, section heading, table, bulleted list, code block, or a subsection (even an entire article). These content blocks are stored in a dedicated section, where you can select them and insert them into your articles.

You can view where a content block is used across your categories, and if you update a content block, it should automatically update everywhere it’s used (although if you read the forums, this may not always happen).

A tricky part is that you can apply formatting to your content blocks, so if you are reusing them, you need to consider how much formatting to apply.

One important capability for technical documentation teams is the ability to create variants or apply filters to content, enabling them to create a single version of their documentation and adjust it for different audiences. For example, you want to create a set of documentation that supports users of all subscription levels of a SaaS product, but some features are only available at certain levels.

Another example: your product is sold in multiple countries, but you had to give it a different name in a few countries. You need your documentation to support each name. Filtering or profiling, along with variants, allows you to do that. Unfortunately, the knowledge base does not provide either of these capabilities.

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Translating Articles: Options Without Efficiency

Zendesk integrates with a number of third-party translation providers, or you can build your own integration using its API. If you purchased the Copilot add-on, you can also translate articles using the AI translation for articles tool. Copilot will create a new version of an article for each language you want. And, of course, you can also choose to manually handle translations using a spreadsheet and copying and pasting. Zendesk does track translations to the source article.

Depending on your approach to translations, the process for managing translations varies. In a manual approach, you must manually flag a translation as outdated. With the other options, it depends on the provider and how you have things set up. Some translation providers only work with content that is already published live. If you want to translate content that is updated but not yet live, the process may be slightly different.

Publishing Limitations: One Primary Channel, Few Alternative Options

The primary channel for Zendesk articles is its Help Center. Which makes sense, because you can easily link knowledge base articles to support tickets. You can also publish individual articles as a PDF, but there is no option to publish an entire knowledge base or multiple categories as a PDF.

How To Know If Zendesk Is The Right Documentation Tool

While Zendesk does provide some capabilities that support technical documentation teams through its knowledge base product, it’s best suited for companies whose primary publishing channel is its Zendesk Help Center.

Zendesk is a customer support solution; it’s not a component content management system (CCMS), and as a result, it does not provide the depth of capabilities that a CCMS offers for managing technical documentation.

Where does it fall short? There are several important areas:

Advanced Reuse: Missing Variants and Filters

Although Zendesk's knowledge base does provide reusable content blocks, these are limited. You can’t create variables to replace words within content or set filters to include or exclude content for different publications. When creating a large amount of similar documentation, content reuse is a critical capability.

In comparison, a CCMS enables documentation teams to create content following a topic-based (or component-based), reusable content model. Instead of creating one complete article or guide or manual, you create a series of topics that are then put together to create a specific publication. Topics can be reused across publications and if you update a topic, it’s automatically updated everywhere it’s used.

Collaboration: One Author at a Time

Most technical documentation teams collaborate with subject matter experts to ensure the accuracy of the content they create. This requires a robust review and approval workflow capability that allows different people to work on documentation. Zendesk does provide review and approval workflow capabilities at the Enterprise plan level, but each person involved in the process must have an agent license, even subject matter experts. There are roles that can comment on an article, but only after it’s been published.

When you break documentation down into topics, you can have more than one person working on the content at once, speeding up the time it takes to create content. Because Zendesk creates complete articles, only one person can work on the content at a time, slowing down the entire process. The exception here is when using content blocks, but the bulk of article creation happens by one person in one article or page.

Translations: Inefficient Translation Processes

Both Zendesk and CCMSs integrate with third-party translation providers, some with direct integrations, while others would require custom integration. Some CCMSs also provide internal translation capabilities.

The key difference between how you translate content in a CCMS versus Zendesk is that you can send topics for translation. You don’t have to send the entire publication for translation every time there is a change to the content. This not only saves time getting translations back, but it also reduces translation costs. Consider a topic that is reused across five different publications. It only needs to be translated once and it’s automatically applied across every publication that uses it.

If you are thinking the Zendesk AI translation tool is a good option, make sure you can train it on your brand’s voice and tone guidelines as well as upload your company’s terminology. Otherwise, you may be translating content using a voice and terminology that is inconsistent with what you want to provide your customers.

Multi-Channel Publishing: No True Single-Sourcing

Most documentation teams have to publish technical documentation across multiple channels, including a Help Center, downloadable PDFs, context sensitive help, and more. What you don’t want is to create multiple versions of your documentation to do that, which is what you’ll have to do if you use Zendesk. Although it can publish an individual article as a PDF, you can’t publish multiple articles with a table of contents and fancy branded covers without involving a developer and creating a custom export process.

A solution that allows you to single-source your documentation, creating and managing it in one location, and then publishing it to the relevant channels is the best option and that’s what a CCMS enables.

Zendesk or a CCMS? Choosing the Right Tool

We could go further here and identify more reasons Zendesk’s knowledge base isn’t a best fit for most technical documentation teams, but these four key capabilities alone make the point.

The question you have to ask yourself is, what do you need? How much documentation are you creating, how much reuse exists between them, who needs to work on the content, and how? Also, what’s your budget?

You can’t purchase Zendesk’s knowledge base on its own; it comes as part of the package of one or more Suites or Support plans. So, if you don’t need the customer support features (which are the primary features of Zendesk), then it’s likely not the right technical documentation solution for you.

The best solution for most teams managing multiple publications across multiple channels is a CCMS. A component-based content management system provides the consistency and accuracy you need when you’re managing multiple publications across channels. It’s purpose-built to handle the unique requirements of tech docs teams and their business processes, from content reuse and collaboration to more efficient translations and publishing.

Of course, not all CCMSs are the same, so you still need to define your requirements clearly and take the time to evaluate the different options. When you do, make sure to put Paligo CCMS on your list; it might just be the perfect solution for you.

To understand the impact AI is having on the technical documentation industry, we’re sharing how Paligo and Kapa.ai are working together - and individually - to leverage AI to create value in technical documentation.

Reading the Technical Writer Room

A couple of years ago, Paligo surveyed its customers to understand how they felt about AI. The response was split. While many were interested in seeing AI features in the Paligo CCMS, 60% had concerns, especially around accuracy and security. Things have changed a lot since that survey, and although technical writers are still somewhat concerned, many are more positive and excited about how AI can help them do their jobs more efficiently.

Paligo’s VP of Strategy and Business Development, Rasmus Pettersson, said that while certain parts of the content creation process could be replaced by AI, it’s unlikely to fully replace humans. What is more likely is that we will see a hybrid workflow in which technical writers embrace AI.

Generative AI is a huge opportunity for both technical writers and the companies that embrace its capabilities in the systems they use. That’s why Paligo is building generative AI into its component content management system (CCMS).

An AI Assistant Designed for Technical Documentation

One way software companies (including Paligo) are leveraging generative AI is through AI assistants that appear as a sidebar or a chatbot within an application. Around three years ago, Emil Sorensen (CEO) and Finn Bauer (CTO), co-founders of Kapa.ai, were looking at ideas to start a business, and this one hit close to home.

Sorensen said they had both been in situations or knew people who worked at highly technical companies and spent a lot of time answering questions that could have been answered if the employee or customer had simply read the documentation. Around the same time, ChatGPT launched, so they tried using it and similar large language models (LLMs) to create an AI assistant for tech docs.

It didn’t work. The assistant would make things up, wouldn’t cite the documentation, and there was no way to provide technical writers with information on how the content was performing.

To resolve these challenges, they developed an agentic RAG chatbot assistant (capable of reasoning through sub-questions like a human assistant), focused on answering technical questions from technical documentation.

Kapa ingests documentation sources, converts the information to markdown, and stores it in an index. It then uses an LLM (Kapa works with multiple providers) to generate the final output in a user-friendly manner.

Kapa answers come with documentation citations, so a user can always go directly to the source content. It can also break down a vague or complex request into specific sub-questions to improve the quality of answers.

The AI assistant is trained to respond with “I don’t know” when a user asks a question that the documentation doesn’t answer. Sorensen said those “I don’t knows” are interesting signals to improve your documentation. Which leads to analytics.

Sorensen said analytics has become the second biggest value proposition for Kapa. Customers can easily find gaps in their documentation using dashboards and reports. Kapa helps understand usage, uncovers patterns with common questions, and can help documentation teams prioritize content creation based on real user intent.

That same challenge—bridging knowledge gaps through trustworthy AI—made Kapa a natural partner for Paligo.

How Kapa Integrates with Paligo (or any technical documentation)

There are two parts to the integration between Paligo CCMS and Kapa.ai. On the front-end, there’s the AI assistant in Paligo’s online technical documentation. A simple script deploys the Kapa AI assistant within the documentation site, allowing customers to open a chatbot and quickly and easily ask questions on how to perform different functions in Paligo.

A great example of how Kapa can help Paligo customers is helping a customer understand how to publish documentation stored in Paligo to Zendesk. This is a multi-step process detailed across different sections of the Paligo CCMS documentation (integration, configuration, publishing). To understand the full process, a customer would normally have to go through each section and piece it together. Now, they can simply ask the Kapa AI. Kapa will go into the documentation, find everything the customer needs to know, and return a complete set of instructions with full citations.

Customers can provide feedback on the information the assistant returns, helping Paligo understand the effectiveness of the content and where it needs work.

On the backend, Paligo team members have access to the Kapa administration site, where they identify documentation for Kapa to ingest. One of Paligo’s most experienced Customer Success team members used Kapa during a customer call to find answers to a question he didn’t have, and he said Kapa returned very good answers.

Paligo’s Roadmap for AI

The implementation of the Kapa AI assistant for technical documentation is just one of the AI projects Paligo is working on. Pettersson describes AI as a "catalyst" that enhances existing capabilities rather than creating them from scratch. The idea is to help documentation teams who don’t always get the recognition (or budget) they deserve to do more with less.

Paligo VP of Product, Thommie Bohlin, said AI has opened doors that didn’t exist before, and there are so many things Paligo could build. He’s talking about features that could provide huge efficiency gains but also open the door to expanding ecosystem tools into the core platform (rather than keeping them as separate tools).

Right now, Paligo is focusing its AI efforts on solving difficult, time-consuming, but essential tasks. Bohlin provides several examples, including:

• A sidebar AI Assistant: Paligo is developing an AI Assistant to support technical writers during the content creation process. An AI assistant for technical documentation has unique requirements compared to the same assistant for marketing or sales content, so there’s a lot of work to ensure it supports technical writers in the best way.
• Improving content reuse: Content reuse is challenging for many tech doc teams, especially if they come from an unstructured content model background or are not trained technical writers. From a post-process perspective, AI can analyze content in the environment and identify overlaps, presenting them to the writer, who can then use the content in whole or in part. The overlaps might not be identical, but would have the same meaning and describe the same things. That’s step one. The next step is more proactive, where the AI analyzes text as it's being written and suggests existing content with a similar intent, preventing duplication immediately and ensuring consistency.
• Streamlining translations: User trust in AI translation tools is growing, so integrating AI-powered translation directly into the authoring tool simplifies what can be a complex workflow. This option is a good alternative for less regulated industries who don't need a human in the loop for their translations or the resources to add another tool to their tech doc stack.
• Simplifying content migration: Using AI to help import and structure legacy content is a key goal for Paligo, as it's a major pain point for companies adopting a new system. This is not a simple capability to build, so it won’t happen overnight.

How does Paligo decide what’s strategic to build versus partner on? Bohlin said the benefits of building features are that they have full control over the direction of each feature and can focus on the specific requirements of Paligo’s ICP (ideal customer profile). However, there are areas where it makes sense to partner, such as the Kapa partnership for an AI assistant for product documentation.

Taken together, Paligo and Kapa’s work points to broader lessons about how AI creates value for technical documentation teams.

4 Key Insights on the Value of AI in Tech Docs

AI is proving to be a transformative partner for technical documentation teams. It doesn't make good writing obsolete; it makes it more critical than ever. AI sharpens the focus on creating high-quality, deeply structured content and elevates the writer's role from a simple producer to a strategic editor and information architect. The revolution isn't about replacing humans, but about empowering them with better tools and invaluable data.

Here are four key insights that demonstrate the value AI brings to technical documentation.

The Most Valuable Answer an AI Can Give Is "I Don't Know"

Kapa's "I don't know" responses are powerful signals for technical writers. Kapa’s platform clusters the most common questions that the AI fails to answer, revealing precise "coverage gaps" in the content. This provides a data-driven roadmap for documentation teams, enabling them to prioritize updates and new articles based on what users are actually searching for and what they are failing to find.

AI Doesn't Eliminate the Need for Structure. It Magnifies It

Well-structured content provides the guardrails that AI needs to deliver accurate, reliable answers.

“It's incredibly important to have it well structured. Because, despite all the headlines you see about LLMs getting better over time with larger context windows, the fact of the matter is, most LLMs are still very, very far from perfect, and they need a lot of help and guidance to understand quite complex products. And the best guidance you can give LLMs is lots of structure and hierarchy,” said Sorensen.

LLMs chunk up documentation to help with ingestion and understanding. Anything a company can do to chunk data before ingestion improves the accuracy and understanding of the LLM. Structured content is key to enabling Kapa.ai to provide the best answers.

Pettersson agreed, noting, “Content from structured authoring tools like Paligo, packed with metadata and a clear hierarchy, is incredibly readable for an AI and can supercharge its capabilities.”

AI Won't Replace Technical Writers; It Will Give Them a New Role

Pettersson argues that AI will not replace the need for a "human in the loop." Instead, it will accelerate a fundamental shift in the technical writer's role.

He describes a "hybrid workflow" in which subject matter experts, such as engineers and product managers, create initial drafts. The technical writer then evolves into a "technical editor." In this new role, their focus shifts to ensuring content is understandable, has the right tone of voice, and fits cohesively within the entire documentation set.

This change frees writers from time-consuming initial drafting and allows them to focus more on the outcomes of documentation. They can spend more time on what happens once the documentation is published.

This is where Kapa’s analytics can play a key role. Technical writers can understand how customers are using the documentation and if they are getting the answers they are looking for. Using this information, they can improve the content and fill in gaps more quickly.

Technical writers are interested in improving their productivity, but Product Managers, IT Managers, and marketers involved in selecting tools like Paligo CCMS want to know how Paligo can improve the customer experience.

By connecting their work to the ultimate goal of enabling the end user to use their product better, technical writers can directly impact core business metrics, such as Net Promoter Score (NPS). This elevates their strategic importance within the organization and proves the value of high-quality content.

According to Pettersson, this “new role” emerging for technical writers is not necessarily new; some of Paligo’s customers are already working in this way. AI, however, will drive change faster.

You're Not Just Writing for Humans Anymore

One of the biggest shifts on the horizon is the need for writers to consider a new audience: AI. Bohlin explained that a major future track for documentation is creating information specifically for AI consumption.

He illustrated this with an example: a tumble dryer displaying an error code. When a consumer searches for that code, they should receive simple instructions, like cleaning a filter or calling a technician. But when a service technician searches for the same code, they should receive a completely different set of instructions, like a five-step guide to replacing a specific part.

Audience-specific delivery is only possible if the content is packed with rich metadata that identifies product lines, target audiences, and markets. The AI uses this metadata to interpret the user's context and present the correct information to the right person. This elevates the importance of a structured, metadata-rich authoring approach even further. Can you do this today? Only with a lot of manual effort.

Paligo + Kapa Working Together

Kapa currently has over 200 customers, including OpenAI, Docker, Sentry, and now Paligo. Sorensen said the company is deeply focused on where the complexity of documentation lies. With the increased use of AI, it’s becoming easier to build products and software, but that also means products will get more complex, and people will rely on AI to help decode much of that complexity. This is where Kapa will focus its efforts, working with customers like Paligo to support their customer needs.

But they are also looking to make Kapa available to internal employees, such as solution engineers and customer success team members, who need to field product questions. In this use case, Kapa would ingest both internal and external sources to create a “super-powered’ AI assistant.

Kapa gets asked by customers they work with a lot about documentation platforms for various use cases. Sorensen said Paligo fits many strong documentation use cases, and they will recommend it where it makes sense. Paligo, for its part, will not only use Kapa on its documentation site but will also recommend Kapa to customers looking to implement their own AI assistants for documentation. It’s a partnership built on mutual respect for what the other is trying to achieve to improve technical documentation for better customer experiences.

FAQs: How AI Is Creating Value in Technical Documentation

How are Paligo and Kapa.ai using AI to improve technical documentation? Paligo and Kapa.ai are combining structured content and retrieval-augmented AI to make documentation more accessible, accurate, and data-driven. Kapa’s AI assistant can answer technical questions directly from documentation, cite its sources, and help identify gaps in content, while Paligo integrates AI into its CCMS to streamline authoring, reuse, translation, and migration.

What makes Kapa’s AI assistant different from generic chatbots like ChatGPT? Kapa uses a specialized retrieval-augmented generation (RAG) approach designed specifically for technical content. It only answers questions using the company’s own documentation, provides citations for every answer, and is trained to respond with “I don’t know” when it lacks the information—an important signal for improving documentation quality.

How is Paligo integrating AI into its own platform? Paligo’s AI roadmap focuses on enhancing—not replacing—the work of technical writers. Current and upcoming features include an AI writing assistant built into the authoring environment, tools for identifying and reusing similar content, automated translation support, and AI-assisted content migration.

Does AI threaten the role of technical writers? No. The article emphasizes that AI is transforming rather than replacing the writer’s role. Instead of producing first drafts, writers will act as technical editors and strategists, ensuring clarity, consistency, and usability. AI handles the repetitive tasks; humans handle judgment, structure, and voice.

Why is structured content essential for AI in documentation? Structured content provides the hierarchy and metadata AI systems need to interpret and deliver accurate answers. Tools like Paligo make this possible by organizing documentation into reusable, well-labeled components that AI can easily understand—improving both automation and human readability.

As we’ll show you, there are significant limitations for documentation teams that use RoboHelp and InDesign, including scalability, accuracy, translation, and collaboration, and that structured authoring and component content management (CCMS) solutions are a better fit for scaling and managing modern technical content.

User Expectations Have Changed

Traditionally, technical documentation was published as PDFs that end users could download or obtain as a paper copy. Although there are some situations where a PDF is still required, it is only one format needed today (and usually the least used one).

Today, users want easy access to technical documentation. They are typically trying to resolve issues or answer questions on their own as quickly as possible, and the idea of having to scroll through pages of a PDF to find what they need is frustrating.

Digital channels that share technical documentation, such as Resource Centers, Knowledge Bases, and Customer Portals, are growing in popularity. These channels provide unique features that make finding information much faster, including search and conversational interfaces.

Additionally, providing technical documentation in digital format enables its view on mobile devices, a channel that is increasingly in demand by mobile support teams, such as field technicians.

The question is, does managing technical documentation in RoboHelp or InDesign enable documentation teams to support these new publishing channels? The answer isn't a resounding yes or no; it's more nuanced than that. But there are key considerations that impact what either of these tools can realistically deliver.

A Brief Look at Adobe RoboHelp and InDesign

To understand the limitations of RoboHelp and InDesign, we first need to describe what these tools are and how they work at a high level.

Adobe InDesign is a Design Tool

Adobe InDesign is a desktop publishing application used by graphic designers to create visually appealing documents that can be published in both print and digital formats. InDesign is part of the Adobe Creative Suite and is available as a subscription service. Like some other applications in the Creative Suite, InDesign is an application you download and use on your desktop; cloud solutions are limited to file sharing.

The most popular use of InDesign is in marketing, where it is used to create brochures, e-books, magazines, and other marketing collateral for both digital and print purposes. You can even create really nice interactive documents with buttons, slideshows, animations, audio, and video.

Adobe RoboHelp is a HAT Tool

Adobe RoboHelp is a Help Authoring Tool (HAT), used to provide help content, typically to applications. Like InDesign, RoboHelp is also a desktop application that is purchased on a subscription basis within the Creative Cloud.

One popular use of RoboHelp is to integrate it with Adobe Experience Manager, providing in-application help and website help content. While it is still actively supported, you don’t hear as much about innovations and new capabilities in this application as you do with other newer Adobe applications.

Using RoboHelp and InDesign for Tech Docs

It’s easy to understand why RoboHelp is used by technical documentation teams, especially those with a strong Adobe software footprint. RoboHelp is designed to create and manage technical documentation, typically for use as help content published using the RoboHelp Server, Adobe Experience Manager, and into Knowledge base solutions like Zendesk, Salesforce, and ServiceNow.

InDesign, on the other hand, seems like a less likely tool for creating and managing technical documentation for a variety of reasons, but particularly because it only enables teams to create a single document at a time, mixing content and format (much like working with Word or Google Docs). Its use historically comes from the popularity of Adobe's creative tools in organizations.

However, as we’ll show, InDesign, and to some extent RoboHelp, are not designed for today’s multi-channel, collaborative, structured documentation needs.

Challenges with Adobe Tools in Technical Documentation

Let's look at several key aspects of managing and publishing technical documentation, and how they are (or aren't) supported by InDesign and RoboHelp.

Content Creation Limitations

You can manage technical content using both a structured and unstructured authoring model, but if you want to get the most out of your content, you’re better off using a structured authoring tool.

With InDesign

InDesign is an unstructured authoring tool. Like Microsoft Word or Google Docs, an InDesign file follows a what-you-see-is-what-you-get (WYSIWYG) model, combining content with formatting and styles. Content is written or copied into InDesign frames, which you then format and size to fit the page. The same principle applies to adding images to your document.

In most cases, technical writers create their content in a Word document and then copy and paste it into InDesign; it’s rarely used to create content from scratch. You can also “place” a Word document into an InDesign file, effectively linking the content. When you use the “place” approach, any changes made to the original Word document are updated in the InDesign File when you open and refresh the file. You will need to review your file, however, as changing the amount of text can impact the design of the content in the file.

Adobe has also introduced a new tool called InCopy that technical writers can use to create technical content that is synced with the content in the InDesign file. The InCopy tool effectively replaces using Word, and the same challenges still apply in terms of fixing formatting issues.

The key point to note is that although it is easier to keep the InDesign file up to date as you make changes to the Word document or InCopy content, you will still need to republish the content and place it in the appropriate publication channels, which means your changes are not real-time.

As the content is unstructured and is created on a document-by-document basis, there is no content reuse across documents. Each document stands on its own, leaving tech writers to duplicate content in each document they need to create. You do have the ability to create snippets of content, but these are saved as standalone files that you then "place" or embed into your documents. You will need to track where you use these snippets manually; the same process applies for publishing updates. You do not have the option to reuse content using variables, filtering, or profiling.

With RoboHelp

RoboHelp also follows an unstructured content authoring model, but you may find you have a bit more flexibility. With RoboHelp, you create a project with a collection of topics. Each topic includes a mix of content and formatting using a WYSIWYG editor. You can assign keywords or phrases to topics to help create an index and then, using templates, publish that content to different channels.

RoboHelp does provide more reuse capabilities than InDesign. For example, you can create reusable snippets of content. However, these snippets can only be used within the project in which they were created, and a project is typically a single document. You can also use tag groups to create conditional content to support filtering or profiling.

The Impact: If you are managing only one or two documents with little content reuse or need to use variables and filtering, then either of these tools may work well for you. However, when you start adding more documentation that needs to be personalized for different audiences and published across multiple channels (each one looking very different), then structured authoring tools will suit you much better and be more cost-effective in terms of both resources and tool costs.

Collaboration and Version Control

The ability to collaborate on technical documentation is essential because writers work closely with subject matter experts and editors to create the most accurate content possible. Because both InDesign and RoboHelp are desktop-centric, not cloud-native, collaboration is a bit more challenging, requiring extra steps you don’t need with a cloud-native tool.

With InDesign, there are three approaches to collaboration. The first is to export the content to PDF and share the document with team members. These team members would then annotate and comment in the document and send it back to the writer, who would then need to make the changes in the original InDesign file manually. You can also share the InDesign file directly with others, but this requires each reviewer to have an InDesign license and to be familiar with working in InDesign. Additionally, only one person can work in an InDesign file at a time, so you may face challenges in managing reviewers' work.

The third approach to collaborating on InDesign files is to upload the file to Adobe Creative Cloud as a cloud document and share it for review. Again, reviewers would annotate and comment, and the writer would then need to manually apply those changes to the file.

It’s a similar story with RoboHelp. You upload the content to cloud storage in the Creative Cloud and share a URL with reviewers. Reviewers need to use the RoboHelp Review Web App to access and collaborate on the content. As for version control, neither tool has it out of the box. With RoboHelp, you need to integrate a tool like Git to manage versions of your content. With InDesign, versions are maintained by publishing the file as a PDF or saving multiple copies of the InDesign file for each version you need.

The Impact: Again, the biggest impact is felt by tech doc teams that work with numerous reviewers and subject matter experts, as there are more people to manage when working on documentation and tracking changes made. Additionally, if your organization must comply with strict regulations, these tools may not be the right choice, as tracking versions and auditing are not built-in features.

Translation and Localization Management

Not every company needs to provide its technical documentation in multiple languages, but for those that do, InDesign and RoboHelp provide significant limitations.

To translate InDesign files, you export the content as IDML and then send the content for translation. When the translated content is returned, you typically create a copy of the InDesign file for each translation and import the translated content into the appropriate file. Now, you are managing multiple copies of your document, one for each language, and you'll need to track changes to the original language manually, as well as when those translations are updated. When you apply translated text to the file, you may need to do some formatting work, as translated content is typically larger in size than the original content (assuming your base language is English).

RoboHelp is a similar story. You manually translate content by exporting a translation file, sending it to a Translation Management Provider, and then importing the translated content. For each language, you create a new Project.

RoboHelp also offers a machine translation approach in newer versions of the tool. Using this approach involves selecting topics or an entire project and sending them for translation. The translated content is then returned and placed in a new language project that is connected to the source project. When the source content is updated, a notification appears indicating that the languages need to be updated.

The Impact: Translation is not inexpensive by any means, but when you have to build manual workflows that require translating the same content repeatedly, even with minor changes, your budget will take a hit. Additionally, manually managing multiple copies of a document and ensuring all translated versions are up to date is a headache that no technical documentation team wants to deal with.

Multi-Channel Publishing Gaps

We talked a bit already about publishing content from both RoboHelp and InDesign, but there are a few more things to consider.

First InDesign: The primary publishing format for InDesign is PDF, both for printed and digital use. You can also publish an InDesign file as an interactive PDF, HTML 5, XML, ePub, TXT, and RTF. The key point to remember with InDesign is that there is no way to automate publishing updates; it's a completely manual process.

Then there’s RoboHelp: The primary publishing channel is RoboHelp Server, which is a server-based Help solution. You can also publish a project to Adobe Experience Manager, SharePoint Online, and knowledge base solutions, such as those already mentioned. Within your project, you define output layouts, skins, or templates for different formats.

In terms of formats, RoboHelp content can be published as HTML5 (responsive and frameless), PDF, MS Word, ePub/Kindle, Mobile App, Knowledge Base, and content-only (XML).

The impact: While there are a number of options for publishing RoboHelp content through automated processes, InDesign does not offer the same capability. If you need your technical documentation published quickly and with minimal manual effort, InDesign may not be the best option for you.

Choosing the Right Path Forward

When selecting a tool for creating and managing technical documentation, it's essential to clearly understand your requirements before starting your search.

If you have only a few documents, little reuse, few publishing channels, and no stringent regulations or compliance requirements, then either of these tools might work for you. If you are an Adobe customer, it makes sense to examine these tools closely.

But the more documentation you need to manage, the more languages, publishing formats, and reuse, these tools have limitations that will restrict you and cause a strain on your resources - both time and money. Structured authoring tools make a lot more sense when you have these constraints; even more so if you have to deal with regulations and compliance requirements.

There is no one right tool for managing technical documentation that works for every organization. The right tool will meet most, if not all, of your organization's (and your tech doc team’s) specific needs.

To get you started, here's a quick checklist you can use to start documenting your requirements. Add any additional questions based on your situation:

1. How many products do we support?
2. How often is our content updated?
3. How much content do we reuse across our documentation?
4. Do we need to use variables in our content (e.g., different product names in different markets)? Or do we need to apply filters or profiles for different audiences or markets?
5. How many languages do we support?
6. What formats do we need to publish our content?
7. What channels are we publishing our content to?
8. How fast do changes to documentation need to be published?
9. How many team members do we have? How many are remote and will need cloud-native tools?

Future-Proofing Your Technical Documentation

We began this blog by stating that there are many different tools technical documentation teams can use to manage content. And there are. However, not all options are the right choice, and it’s essential to understand the limitations of each tool in relation to your specific requirements.

Adobe InDesign and Adobe RoboHelp are both practical tools for specific purposes. While we agree that RoboHelp can serve as a technical documentation tool, its limitations in terms of structured authoring pose challenges for organizations that need to scale their documentation. InDesign, on the other hand, is not purpose-built for technical documentation, unless your requirements are very light.

Structured authoring and a component content management solution (like Paligo CCMS) are the best approaches for most technical documentation teams because they provide the modern capabilities needed to manage highly reusable, scalable documentation.

Define your requirements and carefully review potential tools. Then pick the one that works best for your company. If you'd like to learn more about CCMS, drop us a note and we’ll show you how Paligo CCMS can help manage your documentation.

We're going to look at why relying on tools like MS Word, Google Docs, or Confluence feels easy now, but can lead to significant long-term challenges in terms of efficiency, scalability, and content quality. We'll also explain why structured authoring tools are the better option now and in the long term.

Unstructured vs Structured Content: Comparing Paligo with Confluence, MS Word, Google Docs, Zendesk Knowledge and Adobe InDesign

Discover the difference with structured content

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What "Unstructured" Really Means

Unstructured content refers to any content that is locked inside entire documents or pages, without metadata or modularity. It looks organized on the surface, but behind the scenes, it’s just text and formatting, making reuse and automation impossible, and using AI at scale problematic.

Unstructured authoring is the creation of content as single documents or wiki pages. You may have heard the term "blob content" in content management before. This is what unstructured content is - a single, linear document. It combines formatting and style with content, resulting in a specific publishing format with limited underlying metadata, semantic tagging, or modularity.

Examples of unstructured content tools include Microsoft Word or Google Docs (text files) and Confluence (wiki pages). Both of these types of authoring tools allow a writer to write their content in any manner they want. While these documents do typically have a structured format, it's for visual formatting (headings, bullet points, tables); they don't carry deeper meaning for reuse, automation, or AI..

Unstructured authoring tools appear efficient because any team member can jump in and start writing, but they lack essential capabilities for technical documentation, such as content reuse and multi-channel publishing.

We talk about the difference between structured and unstructured content all the time. Check out these links to go deep:

1. Structured Authoring: What It Is and Why It’s Important
2. Making the Business Case for Intelligent Enterprise Content

From Unstructured to Structured Content

We’ve explained what unstructured authoring is, but before we go further, let’s define structured authoring.

Structured authoring is the opposite of writing blob content. Instead of writing one big document, content is broken into modular, reusable topics (like procedures, concepts, or references). Each topic follows a consistent structure (e.g., headings, metadata, taxonomy), and the style or formatting is separated from the content itself.

A component content management system (CCMS) is a structured authoring tool that allows documentation teams to:

• Reuse content topics across products, regions, and formats
• Improve compliance through versioning and metadata
• Single-source publishing to multiple formats and channels, including PDF, HTML, portals, chats, knowledge bases, and more.
• Support improved synchronous collaboration on the same publication, including writing, editing, and subject matter expert review.

The Capabilities and Limitations of Word, Google Docs, and Confluence (Structured Authoring)

Let’s take a look at each tool to better understand its capabilities and limitations for writing technical documentation.

MS Word / Google Docs

We're going to group Word and Google Docs because they are the same type of unstructured authoring tool (just made by different companies).

Word and Google Docs are word processing software used for writing and reading content. Both are available as free or paid cloud-based tools, and Word is also available as a desktop application. Creating content in these tools is straightforward; you simply create a new document and start writing using a WYSIWYG (What You See Is What You Get) editor interface.

As you write, you apply formatting styles like headings, tables, page breaks, and bulleted lists. You can add images and insert other content such as drawings, charts, and esignatures (each tool may provide different options for additional content types). Documentation authors can spend up to 60% of their time formatting their document, and the final version can appear highly visual and well-written, yet still be a single format.

While simultaneous editing is possible online, there’s no way to lock sections or prevent conflicting edits. You can view a document’s history to see who made changes, but you cannot revert specific changes; only the full version is available. Also, reverting changes runs the risk of overwriting changes made by other writers. If you are using the desktop version of MS Word, review workflows are external. Track changes and comments occur in a returned file, which must then be reconciled manually.

What if you want to use the content you have written in other ways? What if you want to publish parts of the content as a set of FAQs, as HTML-based knowledge base articles in your support portal, or as HTML pages on your Resource site? Beyond saving as Word or PDF, other formats (HTML, Markdown, ePub) are often afterthoughts, with limited control and inconsistent output, especially when using Word Online.

Confluence

Atlassian Confluence is team collaboration and knowledge management software. Writing in Confluence is similar to writing in Word or Google Docs. You create a page and write content using a WYSIWYG editor, including text, images, headings at multiple levels, code, tables, and Figma files. Pages can be versioned and organized into a hierarchy, but they exist as a single flat-file format.

As it’s a collaboration tool, Confluence is good at enabling collaboration. Up to 11 writers can work on a single wiki page at a time, and changes are saved and synced automatically. However, managing large-scale content sets is difficult, and while there is some basic content reuse, it’s not comparable to content reuse in structured authoring tools.

Confluence is both a content creation and content delivery application, which means the primary publishing channel is Confluence itself. To make content available via public links for external customers, you must have a paid version. You can publish Confluence wiki pages in other formats, such as HTML, Word, and PDF, but these are still single-page content formats.

Now that we’ve seen the individual limitations of Word, Google Docs, and Confluence, let’s look at the bigger issue they all share: scalability.

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The Scalability Problem

MS Word, Google Docs, and Confluence all share the same key challenge: they are document-first authoring tools, not content-first. This key challenge is why scaling your documentation with unstructured authoring tools is so difficult.

If you are managing a small set of technical documents with a small team, unstructured authoring tools may be fine. But as your documentation grows, as you add more publishing channels, and as you support more languages, scaling that documentation gets hard really fast.

Publishing documentation across channels requires making copies of each document for each channel. Now you have to deal with duplicated content and find a way to keep track of each version and where it's published. If you want to change some of the content in those documents, you have to copy and paste your changes manually. Manual work typically leads to errors and inconsistencies.

What if you are creating technical documentation for different audiences, such as end users, administrators, and developers? There is a lot of the same content across these documents, so you would want to reuse as much as you can to prevent duplication of effort and maintain changes. With tools like Word and Confluence, this is not possible. You will have to create separate versions and copy/paste content between them. Maintaining changes to the content again requires going to each version and making the changes. As your documentation grows, keeping track of all the versions (and versions of the versions) becomes unmanageable.

Now, let's add in translations. If you are managing documentation for a global audience, with Word and Google Docs, you have to maintain separate copies of the documentation for each language. Each time the content changes, you have to send the whole document for translation. Since your documents are formatted, this process takes longer to translate the content and ensure the design is properly maintained.

Confluence is a little different. Although it does not offer out-of-the-box support for automated language translations of page content, it does have several partners offering translation capabilities through add-ons. It includes additional add-ons that provide browser-based translation leveraging services like Google Translate. Also, if you have Atlassian Intelligence (Atlassian's AI offering), you can translate sections of content or an entire page and paste the output into language macros or new pages. However, in almost all cases, there is manual work to track and manage translations.

The Hidden Costs of Unstructured Content Authoring

Getting cross-organizational buy-in for a modern technical documentation authoring and publishing system doesn’t have to be complicated. When you tailor the message to each stakeholder, including the Team Lead, Department Lead, Head of Product, CFO, and the C-Suite, you replace concerns about resources, effort, and costs with clear, measurable upside. Whether it’s a CCMS and CDP, or a CCMS alone, you can migrate in sprints, build reusable content, and publish richer, searchable documentation exactly when you need it.

And remember, when the numbers show that great documentation wins customers, approving your migration becomes the obvious business decision. Start with a small pilot, track the gains, and watch the buy-in follow.

Watch the complete webinar on-demand to hear more from Josh and Fabrice.

Search and Findability

When your technical documentation is in an unstructured content format, a search engine indexes the entire document and returns the entire document when someone conducts a search. This makes it hard for users to find the exact information they need. Instead, they may have to scroll through pages of information (even with a detailed table of contents).

Structured content allows fine-grained retrieval, which makes it perfect for modern capabilities such as chatbots and AI. It’s also easier to leverage your content in FAQs, snippets, and summaries.

Content Governance and Compliance

In regulated industries, proving what was published when, and to whom, is almost impossible with Word and Google Docs. Even Confluence doesn’t provide the detailed audit trails needed to show when changes were made.

Structured authoring tools provide clear audit trails, with full version history, including tracking translated versions, and where content is reused across documentation. This information is critical in highly regulated industries where even the smallest change can have a huge impact.

Knowledge and Decay

It's not unusual for documentation teams to create one version of a document and continue to update it for minor changes, and then create a new version when there are significant changes required. Suppose a new product feature is only available for a certain edition or subscription level. In that case, the documentation team will need to create a copy of the documentation that includes that new feature. Now, there are multiple copies of the documentation that need to be tracked and updated.

Over time, some of these documents are no longer used or required, but they still exist in locations where customers can access them. The result is outdated, conflicting content that confuses and frustrates customers. Even Confluence Spaces, which are collections of documents, often get outdated, but are still living content that customers have access to.

Structured content, on the other hand, enforces central management and consistency of information. Its single-source nature means technical writers are always working on the most recent version of the content, and as that content is updated, the changes are published everywhere the content lives.

AI Readiness

As companies rush to prepare their content for AI, they face challenges with unstructured content. While it is true that AI can ingest unstructured documentation, it is harder for AI to parse and repurpose it.

We're not going into the details of how generative AI tools, such as large language models (LLMs), work, but there are important aspects to understand regarding how LLMs work (go deeper here). Unlike search engines that index large amounts of content and return results based on keywords, LLMs generate new content in response to a prompt that provides the best answer. These LLMs are trained on source content, such as your technical documentation, and the structure of that content can impact its reliability.

Unstructured content is less reliable than structured content because it requires more resources to process, lacks a consistent format for the LLM to understand, lacks metadata or taxonomy, and mixes content types that require special processing. LLMs also handle a limited amount of data at one time, so unstructured content often needs to be chunked. If it's chunked through automated processing, key information may get separated, leading to less accurate processing.

Structured content improves the accuracy and reliability of LLM responses by providing consistency and context. Because structured authoring breaks content into self-contained topics with standardized formats and enriches that content with metadata and taxonomy, LLMs are better able to recognize patterns, relationships, and key entities.

In addition, structured content that follows defined formats like XML or JSON can provide more reliable output because it’s easier for an LLM to identify boundaries, categories, and relationships. It also integrates seamlessly with knowledge bases and domain-specific taxonomies, enhancing precision in industries such as healthcare, finance, and manufacturing. The result is faster learning, more consistent outputs, and greater reliability in applications like customer portals, self-service, and chatbots, where delivering accurate and context-aware information is critical.

Employee Experience and Onboarding

One aspect we often overlook when managing documentation with unstructured authoring tools is the technical writer's experience. When writing documentation, new writers spend a lot of time learning where documentation lives, how versioning is done, and which documents are the latest. Sometimes, it's a game of hide and seek, trying to learn everywhere a topic is used and update it properly. Writers may also develop carpal tunnel syndrome with all the copy/pasting required to make changes across documents (that's a bit of a joke, but has some truth to it).

Structured authoring tools reduce ramp-up because writers are working with a single tool and a single source of content. They don't have to guess which version is the most recent, or update multiple versions or documents, because updating one updates them all. The biggest challenge for new team members is learning a new structured authoring tool, but the right tool will have a user experience that is intuitive and easy to learn.

The Business Case for Structured Authoring - Why This All Matters

According to a Salesforce study, 61% of customers prefer to use self-service tools to resolve simple issues. Self-service tools require clear, concise, and accurate content. Documentation teams do not have the luxury to spend a lot of time creating content over and over again. They require tools and processes that make their workflows fast and efficient.

Unstructured authoring tools have hidden costs, including wasted translation costs and time spent creating, finding, and updating multiple documents with the same information. Companies also need to consider the increase in support calls resulting from inaccurate documentation.

Structured authoring reduces and, in some cases, eliminates these costs, improves time-to-market of new and updated content, and drives increased customer satisfaction because the most up-to-date information is always available.

When documentation teams are trying to convince their executive leaders that a structured authoring tool is the best choice, they only need to share the costs associated with working with MS Word, Google Docs, or Confluence, and the benefits of structured authoring.

Wrapping Up

Word, Google Docs, and Confluence will always have a place for small teams or simple projects. However, if your documentation is extensive, multilingual, and central to the customer experience, unstructured authoring can create hidden costs that you can't ignore.

Structured authoring is the only scalable path forward. The key is understanding your requirements and showing, through time and costs, why structured authoring and structured content are the best solution.

At LavaCon this year, talk about artificial intelligence (AI) was everywhere. At one point between speakers, host Jack Molisani said that AI had already been mentioned over 400 times that day on the main stage alone. We came away from all this with a number of takeaways that have us excited about the possibilities AI will bring to enterprise content management, as well as the renewed conviction that our componentized, structured approach to content management has our customers well-positioned to reap the benefits of recent advancements in AI.

New roles for content authors

Our first takeaway from the conference was that the role of content authors must pivot in the age of AI. The very first talk of the conference was by Caroline Roth, Vice President of Content Experience at Salesforce. She talked about how her documentation team was able to “change the story” about their role as content creators and prove their value amid the recent explosion of new generative AI tools. Copyeditors pivoted to becoming “AI quality specialists.” New roles were invented for prompt and conversation design. Far from running from AI, this company allowed employees to dedicate time to experiment with AI tools and share their findings across teams. Their documentation team pre-emptively embraced AI-enhanced workflows and then went out of their way to publicize their gains in productivity. Bonnie Swanson from Proscia echoed the sentiment about the new role of writers with AI; technical writers are uniquely equipped for prompt engineering because of their wordsmithing skills.

Stefan Gentz from Adobe used the phrase “Content Orchestrator” to describe how he sees the changing role of content authors. Increasingly, content will be assembled on demand, based on the user’s context and needs. Content will be delivered as pages cobbled out of modular topics written in a structured authoring framework. The workflow he suggests is that AI will draft content, but humans will design intent and limits, enforcing style, terminology, and safety policies. AI will suggest improvements and humans will approve, based on performance along metrics such as time-to-answer. Writers, he says, will go from “writers-of-pages” to “designers-of-systems.” Jason Kaufman, CEO of Zaon Labs, echoed this need for humans to stay in the loop, saying that their job will become more about curating, governing, and assuring quality and accuracy. “Human-verified is the new gold standard,” Kaufman said.

AI is changing how organizations deliver knowledge and support. But AI can only succeed if the content behind it is trustworthy. Disconnected files and inconsistent language create risk. Structured content removes those barriers, making knowledge reliable, scalable, and ready for AI adoption.

For decision-makers, this isn’t just about improving documentation. It’s about turning content into a growth driver that supports faster releases, lower risk, and better customer experiences.

Authenticity is key

No matter how quickly content is delivered or how personalized it seems to be, readers will not trust it unless it is seen as authentic. Authoritative source content, complete with audit trails and manual approval processes, is key to building trust. Rob Hanna, CEO of Precision Content, spoke of the “trust quotient” of content, deriving from factors such as transparency, consistency, clarity, accuracy, and presentation. He quoted OpenAI as stating that one of the biggest issues in training machine learning models is error in data and labels. This points to the need for humans to feed their AI systems with the right kind of content. Sarah O’Keefe, CEO of Scriptorium, noted how AI will expose all of the shortcuts you may have taken with your content: that is, all of the unaddressed inaccuracies, duplication, obsolescence, inconsistencies, and overall lack of informative, semantic metadata. This point was further developed in a humorous bit by Jason Kaufman, feigning surprise that he was being called up to the main stage at that moment, as an AI chatbot trained on an out-of-date PDF copy of the LavaCon agenda had confidently but inaccurately informed him that his speaking slot was later in the day. “Trust is everything,” said Megan Gilhooly from OneTrust, summarizing this core takeaway of the conference.

CCMSs are critical for enterprise content management

Finally, a number of speakers spoke about the tangible benefits of adopting a component content management system (CCMS) for effectively managing content at scale. Dawn Stevens, President of Comtech Services, emphasized that CCMS acquisition is a journey, not a quick fix. While it’s never “easy,” the “rewards are magical,” including productivity gains, increased author confidence, and serious return-on-investment coming from single-sourcing and content reuse. Bernard Aschwanden, head of CCMS Kickstart, cited a bevy of research statistics about the transformative impact of CCMSs on the way enterprises manage their content. For example, the market research company Forrester says that 80% of respondents to a survey of theirs reported that adopting a CCMS reduced risks associated with workforce and regulatory compliance. Furthermore, 3 in 5 respondents expected an ROI increase after CCMS adoption. This is because CCMSs can standardize processes and make quality more reliable. They can also, as noted by consultant Noz Urbina, readily connect with knowledge graphs to power retrieval-augmented generation-style AI chatbots and enable omnichannel delivery of content.

Our time at LavaCon confirmed what we at Paligo already know to be the best approach for technical authors who need to meet the challenges of today and the AI-powered demands of the near-future: to create structured, modular, semantically-rich content and manage it in an enterprise-ready platform with robust collaboration tools and delivery capabilities. We’re already looking forward to the insights and opportunities of next year’s conference!

That’s the focus of the Customer Insights Report 2025. Created in partnership with management consultancy firm Harper, this year’s report shows how structured content delivers measurable business value - and why it has become the foundation for AI that documentation teams can trust.

Highlights from the 2025 Report

• 94% of users report ROI with Paligo, proving the business case for structured content
• Teams save 43% of time on everyday tasks, creating space for faster delivery and innovation
• Updates are 24% fewer and 31% faster, thanks to governed single-sourcing and reuse
• Structured authoring makes content AI-ready, ensuring knowledge is machine-readable, consistent, and dependable for search, chat, and generative experiences

These results aren’t abstract - they’re felt every day by teams using Paligo. As one technical writer put it:

Our team is now able to more easily share content with one another ensuring consistency and saving time making updates in multiple locations. We’re also able to update and publish multiple resources easily and simultaneously. Building our online help from RoboHelp used to take up to 8 hours, but Paligo has significantly reduced publication time to just under an hour.

Another customer from the biotechnology industry highlighted how Paligo changed collaboration:

Simply by many departments being in the same environment has cut down on inconsistency and fostered better collaboration.

And in global content operations, the impact is clear:

The switch to Paligo has saved me many headaches and back-and-forward communications between vendors. It has also improved localization workflows as we used the API to connect Paligo with our internal Translation Management System.

These findings make one thing clear: when content is structured, governed, and reusable, it becomes more than documentation - it becomes a strategic asset.

AI is changing how organizations deliver knowledge and support. But AI can only succeed if the content behind it is trustworthy. Disconnected files and inconsistent language create risk. Structured content removes those barriers, making knowledge reliable, scalable, and ready for AI adoption.

For decision-makers, this isn’t just about improving documentation. It’s about turning content into a growth driver that supports faster releases, lower risk, and better customer experiences.

What the 2025 report indicates

The message from this year’s insights is clear: structured content isn’t just about making technical writing easier. It’s about building a foundation that supports the entire business - shortening release cycles, reducing compliance risk, enabling global scale, and ensuring AI delivers accurate results. Teams that once struggled with duplication, silos, or slow publishing are now delivering consistent, governed knowledge at speed. That shift doesn’t just improve documentation - it changes how organizations operate. With Paligo, content becomes a strategic asset: scalable, future-proof, and ready to support growth in the AI era.

We’re excited to announce that Rahul Yadav will join Paligo as Chief Executive Officer on December 1, 2025. Rahul brings more than two decades of leadership experience in guiding technology organizations through product-led innovation, AI transformation, and customer-centric growth.

A Proven Leader in Innovation and Growth

Rahul has built his career around turning customer needs into innovation:

• At Milestone Systems, he redefined video security platforms with product transformation, AI innovation, partnerships, and acquisitions.
• At TV 2 Danmark, he helped them double streaming subscribers while enhancing digital media experiences and scaling its streaming platform.
• Across every role, from enterprise SaaS to consumer electronics, Rahul has focused on aligning innovation with customer value while leveraging cutting-edge technologies.

Most importantly, Rahul shares Paligo’s deep commitment to putting customers first.

Paligo’s success is built on trust and customer impact. I am deeply honored to join Paligo as CEO at this pivotal moment. As industries embrace AI, our role is to elevate our product and ensure customers stay ahead. We will continue to deliver on Paligo’s promise while building the next generation of intelligent content management.

- Rahul Yadav

Why This Matters for Paligo Customers

Paligo has grown into the CCMS of choice for over 500 customers in 37 countries, including industry leaders such as Mitsubishi, Sitecore, and The Washington Post. With Rahul’s leadership, we are doubling down on:

• Product-led innovation: Delivering powerful, intuitive solutions for complex documentation.
• AI as a catalyst: Reimagining how content is authored, reused, and maintained
• Customer-first growth: Investing in long-term value, scalability, and customer success.

This direction reflects our vision: to be the global standard for structured content - enabling knowledge to move effortlessly.

Voices from the Board and Founders

Rahul brings exactly the kind of visionary, product-focused leadership Paligo needs for its next decade of growth. His track record in scaling technology organizations and building AI-enabled platforms will accelerate Paligo’s transformation and strengthen our market leadership.

Andreas Thome, Chairman of the Board

As one of the founders and the original CEO of Paligo, it has been very important to me that we find the right successor who can take Paligo to the next level at this pivotal moment in time. I believe we have found that in Rahul, and I am truly excited about supporting him in taking us through this next phase of Paligo’s journey.

Anders Svensson, Founder and former CEO of Paligo

A New Chapter for Paligo

Backed by leading software investors GRO and Alfvén & Didrikson, Paligo is entering its next decade with renewed focus on innovation, customer trust, and global growth.

As Rahul steps into his new role, we remain committed to helping organizations turn documentation into a strategic business asset that fuels collaboration, clarity, and measurable outcomes.

This marks the beginning of a new chapter: one where AI, innovation, and customer impact guide our path forward.

Then there’s the change management required. The tricky part about a content migration is not necessarily finding the right tools to do it or running the actual migration. The trickiest part is convincing people it needs to be done.

Getting the right people on board and convincing them that the migration is worth the cost and effort can be difficult, but it’s not impossible. Not if you prepare ahead of time and address the key questions and concerns of each role you need buy-in from. And there are a few different stakeholders who need to support you, including your technical writing team lead, department lead, Head of Product, VP of Finance or Chief Finance Officer (CFO), and the C-Suite (CEO, CTO, or CIO).

Josh Anderson, Information Architect at Paligo, and Fabrice Lacroix, CEO of Fluid Topics, want to help you get your content migration approved. In the webinar, Migration Myths: Faster, Easier and More Fun(!) Than You Think, they take a look at each stakeholder role and the concerns they have, and then provide advice for how you can respond to get their support. We’re sharing their advice here.

Let’s dive in.

Technical Writing Lead

You’re a technical writer and you know things need to change. But you also know that writers don’t always want to learn a new system or process. Maybe they are already struggling with the tools they have, and they think that trying to learn something new will take too long and too much effort.

Your technical writing team lead wants to do what’s best for their team, so the key to getting them (and the rest of the team for that matter) on board is to focus on the benefits of a better authoring experience. Show them that a code-free authoring view, one that creates well-formed Docbook XML in the background (like Paligo does) will allow them to take advantage of structured authoring and XML without having to get into the weeds of writing it.

Explain that a modern authoring experience is as easy as using Microsoft Word or Google Docs, but you get the benefits of structured content, tagging, filtering, and more. Better yet, show them, Josh said. Research and find a system that works best for your organization’s goals, then reach out for a demo. If you like what you see, request a one to two-week pilot and test the system again on one or two key use cases.

Let's say we create support documentation. Can I do that inside the new system? Let me just try that out for a week or so. And if you can prove to yourself that you can meet the use cases that you have, and you can show that to your team lead, I think that will go a long way in demonstrating that this is the right move, and you'll be able to get them on your side.

Once you have shown your team lead that a content migration to a modern authoring and publishing tool will help the team be more efficient and support more publishing formats and channels, it’s time to move on to the Department Lead.

The Department Lead

You have your team lead on board. Now you need to get buy-in from your Department Lead. Whether your technical team lives in the marketing, product, or support department, the Department Lead will have the same concerns.

The Department Lead will consider the time required to purchase the new system or the time needed to migrate content to it. They may also have concerns about having the resources or the skills for the migration. Will you need to hire consultants or experts to help? For this stakeholder, it’s about resources, budgets, and time.

The key to convincing your Department Lead is to explain that the migration doesn’t have to take a long time, and that a faster migration leads to immediate improvements in the technical writing team’s user experience.

For example, Josh said if you already have content in XML format, then the migration isn’t going to take a long time. If you’re migrating from DITA XML to Docbook XML, then you know there are tools available that can do the conversion. He explained that the conversion might not be perfect and you may have to do some massaging, but the bulk will convert quicker than you realize.

One way to deal with the time required to migrate, is to not migrate everything at once. Migrate one publication at a time, or focus on the content in one department, selecting a subset of content and get started. Fabrice said that shorter migration projects deliver results faster because they don’t take as long or need a lot of resources.

Plus, the lessons you learn from the first migration will give you a feel for how the rest of the migration will go. You may find that you do have the resources and skills in-house and it won’t be as hard as you initially expected.

Also, having modern tools readily available that can ingest and transform the existing content formats into new formats is important, Fabrice said.

For example, let me take a set of files and show you how I can load that into our new system and how ninety percent of that is done in matters of hours. At the end, you might need to massage that content, re-adapt it, chunk it further, retarget it to make it more adapted to what you aim to do next. But you have to show that it's going to be easy. That you aren’t going to have to copy-paste everything into the new system and rewrite everything.

You can also consider leaving older content in its legacy format in the new system. If you are no longer updating it, migrate it as is, as it will be faster.

The key to getting the Department Lead on board is to think strategically, and be tactical. Talk about new products or product updates and the documentation that needs to be updated along with it. Know what content is most important to migrate and when. Spending months migrating everything just to have the same content, and then need to quickly update it doesn’t make sense. Show your Department Lead how you can balance the efforts so the migration pays back faster.

Head of Product

Up to now you have been trying to convince people that are directly connected to the content migration strategy. Now, you’re talking to a stakeholder who might see technical documentation as necessary, but not in their mission.

The Head of Product looks at things that don’t directly support their work as something to be avoided. This person may be afraid the change will impact their ability to release a new product or product updates on time. This concern is especially true for software and manufacturing companies that are releasing updates more often (some software companies release updates every month). The Head of Product will be reluctant to support any changes that could slow down their own processes and ways of working.

The key to getting support here is showing them that switching to modern tools will not slow things down, but do exactly the opposite.

Explain that if you migrate content as part of a product update process, the work will not be frozen for months. Instead, content is migrated as needed. For new products, you can directly create content in the new system.

Another way to convince your Product Lead relates to other documentation. If you are migrating from a tool that combined authoring and publishing to a modern tool that decouples the two (like Paligo + Fluid Topics), you’ll find that you can use the new publishing tool for other content as well, like your API documentation written in Swagger, or your knowledge base content currently written in markdown and stored in GitHub. A solution that supports the ability to get all types of content into the hands of your customers is something your Product Lead can support.

Modern tools like CCMSs and CDPs enable you to streamline both the authoring and the publishing process.

Fabrice explained it this way:

By adopting this modern strategy and this new tool, you can explain to your Head of Product that not only will they benefit from a better tool that’s faster for writing the new versions of the documentation due to content reuse, content variants, and better metadata management, but by also having a CDP in place, you will be able to get more content, different formats from different sources, different types of content being published, being created, being managed in parallel so it's faster as well.

You can also explain that using a modern tool enables the technical writers to update content more often and publish it immediately. Fabrice provided the example of a software company that could only release one or two versions of their product a year because the technical documentation took up to four weeks to generate the PDFs and HTML content. When they switched to Fluid Topics, they were able to start delivering content on the fly. So as the product team delivered a new version, the tech docs team could publish updated content at the same time.

The key to getting support from the Product Lead lies in showing them that it will improve their processes as well. Once you do that, it’s time to move on to the CFO.

Chief Financial Officer/VP Finance

Convincing the CFO or VP of Finance is probably the toughest job. This person thinks the costs of migration will be high, including both the technical and the time and resources required to perform the content migration. Your job is to show them that there are already costs associated with how the technical documentation team works today and how the migration will reduce those costs.

If your technical documentation team is using Word/Google Docs, there are licensing costs for those tools. There are also licensing costs for using other legacy content management tools. Along with direct licensing costs, there are other costs associated with using unstructured or legacy authoring tools. In these cases, you’ll need to show the costs exist when there is no line item in the budget.

Show the CFO the hidden costs like the time to create copies or variants (including fixing or updating those copies), including translated versions. Talk about the work required to create multiple formats, like PDF and HTML and the effort to publish that content to the appropriate channel.

The CFO needs to see the ROI of the content migration. You need to surface all the costs to doing business as usual and how you can reduce them, even if you do create a new budget line item.

“What I see as a huge challenge is that you are, in many cases, creating a new budget line. But in fact, this budget line is just making visible something that already exists, and sometimes it's way more expensive than the cost of the tools,” said Fabrice.

Josh suggested to flip things around and ask, “can we justify the cost of doing things the way we are today? Can we continue on this path?” Consider giving the CFO a demo that shows how much faster you can perform a key use case, and how much money is saved as a result.

When you move to a new system you will see cost savings due to an increase in author efficiency, he said. How long does it take to produce a topic or a document today? How many people are involved and how much of their time does it require? How much does each person make an hour? With a little bit of effort you can build rough estimates and show ROI.

Translations are a good use case to prove the value of a content migration to your CFO. Even if translations are still on the roadmap, you can show how a modern system, like a CCMS can set you up for success when the time comes. For example, you can show how easy it is to export topics to XLIFF (the industry standard for translation and localization), or how the system can integrate with leading translation providers, enabling you to easily send topics for translation.

When you send structured content for translation, you only need to send the topics that require translation, not the entire publication. You can also leverage translation memory. Translation providers use translation memory to store previous translations, which means they do not have to translate the same content again (they simply retrieve it from memory).

The reduction in support tickets is another use case you can provide. Well-organized, robust content that is easy to navigate goes a long way in helping users solve their problems and avoid spending time on support calls or emails. Again, you can show the savings in time and costs of your support team by having improved technical documentation in your knowledge base or Help Center.

The key to ultimately convincing the CFO is to build a business case that illustrates the numbers. What are your business goals, and are they being met with the content as it’s currently produced? How can the new system help them reach those goals? For example, will it help publish many different types of content or content in multiple formats for different channels? Can production time be reduced by taking advantage of content reuse? Can it help customers find answers quicker?

These are the things the CFO needs to understand because they tie back to the company’s financial situation. Anything that can improve the finances is a win for the CFO and the company and they will quickly offer their support.

The C-Suite (CEO, CIO, CTO)

You’ve made it past your Technical Writing Team Lead, your Department Head, the Head of Product, and the CFO, but now you need to convince the C-Suite. And this group is as far from what you do as possible.

The C-Suite doesn’t see your work as core to the business. Sometimes, it seems like they don’t see content as important at all. But you know it is, because it’s content that brings customers to your site and to your product.

A couple of stats Josh and Fabrice shared to prove this point:

• 74% - That’s the number of B2B customers that review user documentation as part of their purchase process.
• ⅔ - This is the number of B2B buyers that said the most useful type of content to aid their purchase decision was content that talked about product specifications and functionality (content that is suited for CCMS and CDPs).
• 77% - This is the number of people who view organizations more positively if they offer self-service support options (and you know content is the foundation of self-service support).

The C-Suite is also numbers-oriented, just in a slightly expanded view from the CFO. Josh said to look at the money marketing spends creating brand and market awareness.

You can throw all the money you want at paid search ads, but if you don't have content, right? What are they clicking on? What are they getting to? You need to establish that presence through content. You need to get your name out there, and it's content that will help you do that.

The technical writing team is already creating this content. Technical content contains the keywords customers search for. It contains the features and functionality they want to learn about. Show the C-Suite how, if that content is made publicly available in a highly indexable search engine, it can give customers and prospects the exact information they need to make a decision to purchase and use your product(s).

One other selling point that will get the C-Suite on board fast:

One other thing I think we should point out is that when you adopt single sourcing, you can really promote consistency throughout your whole content corpus. And this will reduce the chance that maybe an outdated variation of content exists somewhere, protecting the organization from potentially expensive lawsuits or other troubles. The C-Suite might be interested in knowing that you'll have a way of maintaining that consistency throughout all the content.

Wrap Up

Getting cross-organizational buy-in for a modern technical documentation authoring and publishing system doesn’t have to be complicated. When you tailor the message to each stakeholder, including the Team Lead, Department Lead, Head of Product, CFO, and the C-Suite, you replace concerns about resources, effort, and costs with clear, measurable upside. Whether it’s a CCMS and CDP, or a CCMS alone, you can migrate in sprints, build reusable content, and publish richer, searchable documentation exactly when you need it.

And remember, when the numbers show that great documentation wins customers, approving your migration becomes the obvious business decision. Start with a small pilot, track the gains, and watch the buy-in follow.

Watch the complete webinar on-demand to hear more from Josh and Fabrice.

However, the decision to provide your content in multiple languages is not as straightforward as simply writing your source content and asking someone to translate it into additional languages. You need a clear strategy for translation management, and you need the right technology.

In this blog, we'll explore the need for multilingual technical documentation and the challenges associated with translating it. We'll examine the creation of effective translation workflows and explore the value that a Translation Management System (TMS) brings to the process. Plus, we'll tell you how Paligo CCMS integrates with three popular TMS platforms.

Ready to get started? Let's go.

The Unique Challenges of Translating Technical Documentation

Translation management is challenging for every type of business content, especially if you don't have a clearly defined strategy and process. If you work with an ad hoc translation process, then you understand these challenges well:

• Tracking translations in a spreadsheet, emailing documents back and forth to multiple people to add translations, review and comment, make changes, resend for review, and eventually approve and publish.
• Version control is a nightmare - there's no way to ensure the correct version of a document is being reviewed or edited.
• Deadlines are missed because there are no assigned tasks, notifications, or reporting. Terminology is inconsistent across (and sometimes within) documents, and critical errors in instructions or legal disclaimers are often missed.
• Content is translated inconsistently across publishing formats, including XML, PDF, in-app help, HTML, etc.

Apply these challenges to translating technical documentation such as manuals, guides, knowledge base articles, and instructions for use, and you'll have even more concerns. Unlike marketing copy or business content, technical information has no room for error or ambiguity. Inaccurate technical instructions or safety warnings can lead to misusing a product or equipment, which could result in serious consequences.

Then, there's the added challenge of consistently managing highly specific, often proprietary, technical terms across documentation and languages. This type of documentation can change frequently, sometimes involving small changes to update bug fixes or minor enhancements, and other times major updates that replace complete documentation or add significant sections.

Another aspect to consider is legal and compliance. Technical documentation must adhere to different regulations and legal standards in different countries or markets. This includes legal content in documentation such as warranties, regulatory statements, terms of use, and compliance declarations. It is critical to ensure this type of content is translated accurately and according to proper jurisdictions.

Translation management for technical documentation requires a clearly defined strategy, one that is best put in place when you are planning your documentation, not at the end after that documentation is already created. When you think about your multilingual requirements from the start, you will be better equipped to deal with the range of translation needs as they happen.

What is Translation Management?

Translation management is the process of taking content written in a source language and translating it into other languages and locales. Note the distinction between translation and localization.

• Translation: Translating text from one language to another, ensuring the meaning and purpose of the source content are accurate across languages.
• Localization: Ensuring translated content adheres to cultural, technical, and legal requirements of a location or market, including currency and date formats, product naming and standards, and legal terms.

When you frame translation management as a strategic business process, you develop a systematic process for planning, executing, tracking, and optimizing all the activities involved in producing multilingual content. This process ensures that your multilingual content is accurate, consistent, and compliant.

Some things you include in your process:

• Who's involved and what are their roles and responsibilities: translators, reviewers, legal/compliance teams, and technical writers.
• Translation workflows that map the translation process as part of the content creation and editing process.
• The definition of controlled vocabularies and glossaries that ensure translated content is consistent and accurate across documentation.
• Structured content authoring for easier translation, including reusing translations across documentation.

Of course, the best translation management process also includes using technology, specifically a translation management system.

Understanding Translation Management Systems (TMS)

A translation management system (TMS) is typically a cloud-based SaaS platform that manages the end-to-end process of translating content for a company. A TMS streamlines the translation process, centralizing all the activities required to translate technical content. It provides both human and AI-powered translation, combining the best of both to deliver multilingual content quickly and accurately.

Some key capabilities of a TMS include:

• Translation projects, with progress tracking and reporting
• Workflows that manage the creation of translations and the review and approval of translations in the TMS before they are exported.
• Glossaries and terminology management provide guidelines for translators on the meaning and usage of key terms and phrases.
• Quality assurance and automated validation, including fixing typos, formatting, and terminology inconsistencies.
• Translation memory (TM) that stores previously translated content, which is used to reduce the amount of new translation required.
• Out-of-the-box integration with component content management systems (CCMS) and other content management systems.

A TMS provides a collaborative environment where companies have visibility into the translation process and can support translators when they have questions.

The best TMS systems reduce both the manual effort and costs associated with translation.

A Note on Translation Memory

Translation memory is especially important when translating large amounts of technical documentation. A database stores previously translated text segments, such as sentences, paragraphs, or headings, along with their corresponding source language texts. In the case of structured content, the TMS can store the components.

When new content needs translation, the TMS can compare the new source components against the ones stored in the TM and determine:

• A perfect match
• 100% match - The content matches the source, but the context differs. The translator needs to review the translation to determine if it is accurate for the new context.
• Fuzzy match - The content matches 99% or less. The translator must then edit the suggestion instead of translating from scratch.

Translation memory provides many benefits, including:

• Speeding up the translation process because translators don't need to start from scratch every time or re-translate existing translated content.
• Ensuring the same content is translated identically across all projects and documentation.
• Helping to maintain the overall quality of translated content.
• Reducing the costs associated with translating content.

TMS + CCMS: A Critical Integration for Technical Documentation

Let's talk about integrating a Translation Management System and a Component Content Management System, with a particular focus on Paligo CCMS.

A CCMS creates and manages documentation following a structured content model. A structured content model means content is broken down into discrete topics or components that authors can reuse across documentation. This approach to content management is particularly well-suited for translation management, as a TMS can more easily track what content is new, what has changed, and what remains unchanged in each component. Plus, even though a component is used many times, it only needs to be translated once. The result is faster translations and significant cost savings.

Consider a situation where structured content is not used for technical documentation. Each document would be sent to a TMS for translation, even if multiple documents contain duplicate content. The content is translated multiple times, which poses a risk of inconsistencies in translations across documents.

Another reason structured content is better for translations is that it is translated before it is formatted. If you send a formatted Word or PDF document for translation, you will incur an additional charge for desktop publishing formatting (DTP) in addition to the actual translation. According to Sarah Keefe, CEO and founder of Scriptorium, DTP typically accounts for 30-50% of a total translation invoice, so removing it would significantly reduce translation costs.

The CCMS also remains the master repository for all language versions of the content, ensuring consistency and control, and it handles the complexities of assembling and formatting multilingual publications from the approved translated components.

Paligo's Built-in TMS Integrations

Paligo offers built-in integrations with several TMS, including Phrase, Crowdin, and LanguageWire. It also provides an API to support integration with other TMSs.

Integration with Phrase

Phrase TMS is directly integrated with Paligo's translation management workflow, allowing content to be sent to and received from Phrase. In Paligo, you create a translation project that links to a Phrase template. When you have content ready to send to translation, set its stage to "In Translation" and then export the translation. The content is automatically packaged and sent directly to Phrase.

You must notify Phrase that the content has been sent, but everything else is done for you once the integration is set up. When translated, the content is directly imported back into Paligo as either matched and approved in Phrase and ready for final checks in Paligo or fully translated but requires review and approval in Paligo. Once the translation content is approved, its status is set to "Released," marking it as ready for publication.

It's also important to note that images, variables, and profiled filters require separate translation packages and handling.

Integration with LanguageWire and Crowdin

The LanguageWire and Crowdin translation management systems do not directly integrate with Paligo from Paligo's platform. Instead, they integrate with Paligo within their platforms.

Crowdin provides a Paligo application that you download and connect to your Paligo instance. When you have content to translate, set the translation workflow stage to "In Translation" in Paligo and then navigate to the Crowdin app to sync the content. This imports the content to Crowdin for translation.

You can set up scheduled synchronizations and email notifications for the status of translations. The translated content is automatically pushed back into Paligo, and the translation state is set to "In Translation Review." Once the review is completed, the workflow stage is set to "Released," and the content is ready for publication.

LanguageWire is slightly different, connecting to Paligo via a secure API. You set your content to "In Translation" in Paligo, then go to your LanguageWire account and initiate the export of that content from Paligo. Once translated, the content is automatically imported into Paligo for review and approval.

The Benefits of Integrating Paligo CCMS and a TMS

There are many benefits for both the business and the technical documentation team when integrating a TMS with Paligo CCMS.

Business Benefits

For the business, the biggest benefit is reduced translation costs. By leveraging Translation Memory and AI-powered/generative AI translations, there is less content to translate, and translations happen much faster. A faster turnaround time helps businesses accelerate multilingual content delivery, which can help them move into new markets faster.

Using a TMS to manage all translations also helps to enhance brand consistency. Translation memory, along with the use of terminology banks and glossaries, ensures a consistent brand voice, terminology, and messaging across languages and target markets. It also ensures that content, such as instructions for use, is consistent across languages.

Finally, compliance and security are also key benefits when using a TMS. Content moves securely between the CCMS and the TMS, and they follow robust version control and audit trails to ensure the correct content is always being translated and published, regardless of what language it's in.

Documentation Team Benefits

There are also many benefits for technical writers and content creators. First, a seamless workflow enables you to initiate, manage, and track the entire translation lifecycle directly within the Paligo CCMS environment as part of your regular content creation and update process.

Direct integrations eliminate errors associated with manual tasks, such as copying and pasting content between systems, and accelerate the translation process because content is only translated once, even if it is used in multiple publications within the CCMS.

A consistent brand voice and terminology are also important for technical writers. Using a TMS means they are confident that the translated content accurately matches the source language and is updated as the source content changes.

Translated content is managed alongside its source content in the Paligo CCMS, which means that version control and audit trails help ensure the accuracy and compliance of multilingual content.

Best Practices for Effective Translation Management

Let's wrap up our look at translation management with some best practices.

Think Globally from the Start

Integrate translation planning into the content lifecycle, not as an afterthought. Some companies add translation management after the fact. They assume any process they design to create and manage content will work fine for translations. However, writing content with translation in mind can dramatically alter how you think about your content workflow.

Even if you aren't ready to publish translated content, you should design your content development strategy with multilingual content in mind. Identify the markets and languages you want to include and map out any key requirements that could affect how you write your content.

Key considerations include your structured content model and how you manage images and text strings. Think about your content reuse strategy to understand where you can reduce translation costs. Also, think about how to best incorporate translation review into your content management process so that translated content is quickly reviewed and approved for publishing.

Build Translation into the Content Workflow

A simple yet effective content workflow will have translation built in. Content workflows provide accountability and transparency and reduce errors and bottlenecks. A workflow with translation built-in might look like this:

• Stage 1: Content preparation (component-level, structured), reviewed and approved in the source language.
• Stage 2 (In translation): Language assignment and automated handoff to TMS.
• Stage 3 (Translation Review): Translated content is imported back into the CCMS for translation and review
• Stage 4: Legal/compliance review (if required)
• Stage 5 (Released): Final approval and publishing.

Empower Translation Reviewers

Provide SMEs and legal reviewers with easy-to-use tools to review translated content, including reviewing translations in context. Establish clear guidelines on what to do if there are translation issues and regularly provide effective feedback.

Choose TMS Technology Wisely

Select a TMS that fits your needs and integrates seamlessly with your CCMS. You will need to understand your translation requirements and map those against the capabilities and limitations of the Translation Management Systems you are looking at.

Treat your selected TMS as a strategic partner. Regularly provide clear instructions, context, and feedback to keep everyone on the same page. Also, a clearly defined SLA (service level agreement) should be established so that everyone understands the expectations of the TMS provider, including translation turnaround times and how to manage issues that might arise.

Centralize and Govern Assets

Look for a TMS that maintains a single, high-quality TM, controlled vocabulary and terminology. Your documentation team must collaborate with the business units to create glossaries and terminology you want your TMS to use. Establish clear ownership and update processes of that terminology, and define the process for updating it in the TMS as required.

Measure Everything That Matters

Define Key Performance Indicators (KPIs) such as cost savings from TM leverage, turnaround time, first-pass quality rates, and reviewer turnaround time, and use these analytics to drive continuous improvement.

Prioritize Legal Diligence

Ensure legal review is a mandatory, tracked step for relevant content, especially documentation components that must adhere to compliance rules and specific market regulations. Leverage your CCMS translation workflow to record and maintain an audit trail of approvals.

Effective Translation Management is Essential for Technical Documentation Success in Global Markets

Effective translation management is non-negotiable for delivering accurate and compliant technical documentation in global markets. Success requires moving beyond ad hoc methods to a strategic approach involving clear workflows, structured content, and robust technology.

Integrating a Component Content Management System (CCMS), such as Paligo, with a Translation Management System (TMS) is crucial. Paligo's integrations with leading TMS platforms such as Phrase, Crowdin, and Language Wire streamline the entire process, from automated handoffs to leveraging translation memory and terminology. This powerful combination significantly reduces costs and turnaround times, enhances quality and consistency, and ensures compliance.

Ultimately, you want a scalable foundation for managing multilingual technical content effectively. Let us know if you want to learn more about Paligo translation management processes.

At Paligo we're working on a new implementation of the XPath and XSLT standards, and we recently decided to open source this project. Its name is Xee, which stands for "XML Execution Engine". Because Xee is now open source, others are free to use it and can also contribute to its development. We hope it finds many such users in the future, as anyone interested in XML can benefit from this collaboration, including Paligo.

Here's the Xee repository that contains the project source code.

XML, XPath and XSLT

To explain what Xee does we first need to explain what XPath and XSLT are.

Consider this simple XML document:

<?xml version="1.0" encoding="UTF-8"?> <article xmlns="http://docbook.org/ns/docbook"> <chapter> <title>First Chapter</title> <para>This is the first paragraph in the first chapter.</para> <para>This is the second paragraph in the first chapter.</para> <section> <title>A Section in the First Chapter</title> <para>This is a paragraph in a section of the first chapter.</para> </section> </chapter> <chapter> <title>Second Chapter</title> <para>This is the first paragraph in the second chapter.</para> </chapter> </article>

As you can see, this XML document defines an article with multiple chapters, and inside these chapters can be sections and paragraphs. XML uses tags designated by angle brackets (< and >) to "mark up" parts of the document. Tags enclose something between open and close tags (like <para> and </para>).

The tags say something about what they enclose; this is a chapter, section, paragraph, etc. There are many different ways to structure XML; what we showed you here is docbook, which is what Paligo uses.

/article//para

This goes to the article and then finds all marked up elements inside named para.

XPath is a fully-fledged programming language, supporting sophisticated queries; you can query an XML document a bit like you can query a database. XPath is fully specified in a series of W3C specifications. W3C is the World Wide Web Consortium, an international standards organization for web related technologies. Because it's extensively specified we know it's going to be around in its present shape for a long time, so we can build on it for a long time. Built on top of XPath is another standard named XSLT, again specified as W3C specifications. This defines another programming language geared towards transforming one XML document into another (or also HTML and other formats). This can be used to create many different kinds of useful output given a structured document as input.

If you use XML, XPath and XSLT are very useful. Paligo uses them a lot!

Existing implementations of XPath and XSLT

If you use the Java programming language you have access to good XPath and XSLT support. XPath and XSLT are implemented by Saxon, which has been around for a long time. Saxon is available for various other programming languages as well, though the integration can be somewhat involved.

If you step out of the Java world and its periphery, and if you look for other implementations, you run into libxml2 and libxslt, which form a single larger project together. The libxml2/libxslt combo is everywhere; on Linux servers, in MacOS, and as part of many larger projects. libxml2/libxslt is written in the C language, which is a widely-used and venerable programming language.

Software libraries written in the C language are more easy to integrate with other programming languages than Java projects tend to be, so you see that libxml2/libxslt is widely integrated with other programming languages, such as Python. In fact, I, as the main developer of Xee, once created a popular Python integration for libxml2 named lxml.

Unfortunately libxml2 is stuck in the past - it implements XPath, but only XPath 1.0, and similarly libxslt implements XSLT 1.0 only. These are old specifications from 1999. The XPath 2 specification was released in 2007, and we're currently actually at XPath 3.1, released in 2017. Similarly XSLT 2.0 was released in 2007 and XSLT 3.0, the current version, in 2017. These newer standards define many new features for XPath and XSLT that libxml2/libxslt does not support.

Why a New Implementation?

But the modern versions of XPath and XSLT standards have already been implemented, through Saxon. Why then create a new one with Xee?

Having an alternative implementation is good for the standard themselves: for XPath and XSLT to be thriving standards they need multiple implementations, in multiple programming languages, by multiple parties. Xee now contributes such an alternative. Xee is built using a modern programming language named Rust. Rust is a systems programming language like the C language, meaning it offers a lot of control and also high performance. This makes it well suited to implement programming languages such as XPath and XSLT. Rust prevents many of the pitfalls traditionally brings, such as memory leaks and security flaws, unlike the C programming language which was used to implement libxml2/libxslt. Software that is written in Rust plays well with other programming languages; our hope is therefore that Xee becomes integrated with many other programming languages, such as Python and PHP. We've in fact created a prototype named xee-php that lets you use Xee's XPath facilities from PHP.

Our hope is that Xee can be a more modern and secure alternative to libxml2/libxslt that finds its home in the open source world and many projects.

Xee Status

The Xee project is a huge undertaking; implementing a programming language is no small feat, and Xee aims to support two! The specification texts describing XPath and XSLT run over 1800 pages.

What we're most proud of is the XPath 3.1 implementation in Xee. The XPath core language and most of its standard library have been implemented. There are gaps in the standard library implementation still - some formatting functions are particularly huge, for instance, but overall it's pretty complete.

Besides using Xee from the Rust programming language (and, as we hope, other programming languages in the future) we also provide a "xee" command line tool you can use to execute XPath expressions and, soon, issue XSLT transformations (for as much as that works). You can download it from our releases page.

There's an XPath 3.1 conformance test suite which provides a lot of automated tests for all aspects of XPath. Of the 21,859 tests, 20,221 tests are passing at the time of writing - that's already 91 more since we open sourced Xee recently, thanks to open source contributors! Most of the still failing tests have to do with the implementation of missing standard library functionality.

Meanwhile Xee also provides a solid basis for XSLT, reusing a lot of the XPath infrastructure. We've recently made it possible to run the XSLT conformance tests as well so we can track our progress. We are now passing 921 XSLT conformance tests, which sounds less impressive once you know there are in fact 14,595 of them. Still, that's a lot more than just a week ago! A few people have already started to contribute to the XSLT implementation as well.

Xee has now grown beyond just Paligo's project. We hope that we'll find more people that are interested in going on this journey with us, together!

We offer an industry-leading solution with years of experience providing leading pharma and life science companies with easy-to-use CCMS. It will drive efficiency, reduce hidden costs, and convert regulatory challenges into strategic advantages. Continue reading to find valuable information about how to write instructions for use.

FAQ: Instructions for Use of medical devices

Before diving into the topic of IFU in Pharma, let’s address some of the most common questions about it.

What does IFU mean?

IFU stands for Instructions For Use. These documents provide clear guidelines on how to use medical products and devices safely. They are required, for certain products, according to regulatory bodies such as the FDA and the EMA.

What does IFU mean in medical devices?

In medical devices, IFU stands for Instructions for Use, FDA-approved labels. They contain detailed patient-use instructions for products with complex or specific usage requirements.

What is the difference between IFU and DFU?

The difference between IFU and DFU (Directions for Use) is minimal. Both terms refer to the same concept - instructions or directions for proper product use to ensure safety and effectiveness.

What are the manufacturer's instructions for use?

Manufacturer's instructions for use (IFUs) provide information on the product's intended purpose, proper usage, and necessary precautions during installation or use.

The Value of Good Instructions for Use

Clear instructions for use protect both users and manufacturers. Correct instructions prevent injuries and save lives by promoting the proper use of medical devices and prescription drugs. Well-written IFUs also reduce manufacturer liability by demonstrating due diligence in user safety.

Beyond safety, effective IFUs drive successful product adoption. Clear instructions help users apply medical products more correctly and consistently. This leads to better treatment outcomes and increased trust in the manufacturer.

Quality IFUs serve as valuable educational resources. Healthcare providers use IFUs to train their staff effectively. In-home care settings, caregivers depend on these instructions for proper patient care. Patients themselves rely on IFUs for safe self-administration of treatments. Through clear guidance, these documents build user confidence and minimize the risk of medical errors.

How to Write an IFU in Pharma for Medical Devices

Creating a clear and compliant IFU requires a systematic approach. Follow these key steps to ensure your instructions for use meet both user needs and regulatory requirements.

Instructions for use: Medical device template

• Intended Use: Define the purpose of your device according to ISO standards. Clearly state what the device is designed to do, and tailor the language to your target audience.
• Usability Engineering: Apply usability engineering principles (per IEC 62366). The principles will help you to design an IFU that is easy to follow. Organize your content to enhance user safety and comprehension.
• Risk Management: Identify potential risks from normal use and misuse. Include warnings and precautions to mitigate these risks, ensuring users are well-informed.
• Reference Standards: Incorporate relevant standards related to medical devices in your instructions for use. A few such standards are EN 1041/ISO 20417 and EN ISO 15223-1. By including these kinds of standards, you ensure your IFU meets industry guidelines. Align your content with FDA guidance when applicable.
• General Safety & Performance Requirements: Provide vital safety information, such as the device’s registered trade name, trademark, and CE mark. Further, include required details about medicinal substances.
• Regulatory & Legal Considerations: Add necessary legal statements to your instructions for use. Some examples are disclaimers, copyright notices, and trademark information. In this way, you can protect your organization and ensure compliance.
• Technical Specifications: Include key product details such as dimensions, operating conditions, and performance data.

Driving Safety, Instilling Confidence

Working with IFUs is about more than meeting regulatory requirements. It’s about protecting patients, boosting adoption rates, and preventing avoidable errors.

Clear, step-by-step instructions reduce the risk of misuse. The instructions help both healthcare professionals and patients feel confident using your product. Well-structured instructions improve their user experience. Below are key aspects to keep in mind when you and your technical writing team prepare the documentation:

• Use a sans-serif style font.
• The font size should not be smaller than 10pt.
• Use bold for the title, headings, figure titles and step numbers.
• An IFU for medical devices should be in bold and upper case letters.

By focusing on patient safety and clarity, you will achieve two things. You will both communicate that you value the user's well-being, and you will form long-term confidence from the customer.

The Difference Between IFU and User Manuals

An IFU is a specific procedure to accomplish a task, which can exist as a standalone document or within a user manual. User manuals serve as comprehensive references, while an IFU can cover operation, maintenance, and design theory, as well as include various guides and instructions. For medical devices, they provide step-by-step guidance on performing individual tasks safely and effectively.

Optimizing IFU Documentation and Compliance with Paligo CCMS

Every market sets its standards for IFU for medical devices and pharmaceuticals. For example, FDA requirements differ from those in the EU, and many companies must follow ISO guidelines.

Extra rules, such as those from the FFDCA, The Federal Food, Drug, and Cosmetic Act, and the EU Medical Devices Directive, add to the challenge. Regulatory changes - like updates on adverse effects and warnings - happen frequently.

As regulations keep changing, managing IFU documentation with traditional methods like PDFs and paper files creates risks. A modern Component Content Management System (CCMS) helps solve these challenges through:

• Centralized Content Management: Store all IFU documentation, safety summaries, and clinical evaluation reports in one secure location.
• Version Control: Track changes and maintain audit trails across departments.
• Smart Content Reuse: Organize content as reusable components to maintain consistency.
• Collaborative Workflow: Enable legal, marketing, and technical teams to work from a single source of truth.
• Global Compliance: Quickly update and translate documentation as regulations change.

Paligo's cloud-based CCMS helps organizations like yours streamline the IFU documentation process. Our platform uses a topic-based, XML-driven approach to organize content as reusable building blocks.

This method lets your technical writers focus on creating clear, compliant content. As a result, your IFU product descriptions and other technical documentation remain consistent and compliant across markets through smart content reuse.