What does it cost an organization when AI gets the answer wrong?

Before an organization can weigh the cost of a wrong answer, it needs to know how often wrong answers occur and what follows when they do. 70.1% of respondents report that an enterprise AI application produced a materially wrong answer or action at least monthly in the previous 12 months. The question covered information that was incorrect, outdated, unsuitable for the context or unsupported by available information. [2] The figure shows how often respondents encountered a problem in their organization. It is not an error rate: the survey did not count all outputs or measure errors per answer.

The consequences can reach beyond the application itself. In a separate question, respondents identified what their organization had experienced over the same period because of incorrect or unreliable enterprise AI outputs. Their answers span operations, customer relationships and areas of greater business risk.

Each consequence carries a different kind of cost. Additional employee time is work the organization has to resource. A process delay can hold up a decision or project. Incorrect information supplied to customers means establishing what was communicated and how to correct it. Reported financial and reputational effects show why an error may need attention beyond the team operating the application.

Two categories need careful reading. Respondents reporting a safety issue chose a single answer option that covers both an actual safety incident and increased safety risk. The survey therefore cannot show how many of them experienced actual harm. The compliance, legal or regulatory category similarly groups a range of possible circumstances into one answer. The findings establish reported exposure; they do not specify its severity or determine liability.

Priority should reflect both frequency and consequence. A relatively infrequent error in a safety-related decision may warrant tighter controls than a more common error in a routine task. Leaders need to examine what could happen if an answer were acted on, alongside the effort needed to detect and correct it.

That starts with the incident record, which should capture the full sequence: the output, who relied on it, the action taken and the resulting effect. It should also record the work needed to put the issue right. That work is a more immediate, measurable part of the organizational cost.

Resolving an error can also involve several teams, each responsible for a different part of the problem. A technical team can investigate retrieval, while a content owner determines whether an instruction was appropriate. If the output has informed a customer communication or a sensitive operational decision, the team responsible for that activity needs to assess the consequence. Clear handoffs help the organization establish who can resolve each part of the problem.

This effort belongs in the AI business case, alongside any time saved producing the initial response. The comparison should follow the task through to a usable result, including necessary review and the work triggered by errors. The survey does not establish whether these costs outweigh AI’s benefits. It identifies work that an assessment of those benefits needs to consider.

Once the immediate issue has been addressed, the organization needs to decide what should change. Correcting a response, updating an obsolete source and changing how information is retrieved address different problems. Choosing an effective intervention starts with understanding the cause.

Correcting a wrong answer requires a substantial commitment of employee hours. Among respondents who report materially wrong AI outputs, 51.2% say investigating, verifying and correcting a typical issue requires at least five employee hours in total. Within the same group, 26.6% report more than one working day of total employee effort. [4] These figures need to be read precisely. They describe combined employee time, which can be spread across several people, not the time that passes before a case is resolved.

The survey also asked about diagnosis: 33.5% of this subgroup say establishing why a typical issue occurred takes more than four hours. [5] Diagnosis time and total employee effort measure different aspects of the response, so the two cannot be added together.

A replacement answer might only resolve part of the incident. An investigation may require someone to locate the source, check its version and establish how the application used it. The relevant business team may then need to review a decision or correct information already shared.

When organizations investigate why an AI application produced a wrong answer, they find several distinct causes, from outdated information to content the model generated without support from the available sources. Each calls for a different response.

Asked which causes their organization had identified through investigation, 26.8% of respondents selected outdated retrieved source information and 25.3% selected relevant information from multiple sources being combined incorrectly. 23.6% identified information applied from the wrong product, market, jurisdiction, customer or other context. These percentages use all 411 respondents, including those who had not investigated the issue or did not know the cause. [6]

Each of these failures concerns the relationship between information and its use. An instruction may be valid for one product configuration, while another source sets out an exception or prerequisite. If the material is combined without preserving those conditions, the answer can be unsuitable for the task even when the individual passages are accurate.

Two further causes concern how sources relate to one another and which version applies. 19.7% identify relationships or dependencies between sources not being preserved or understood, and 18.2% identify the wrong version of otherwise valid information being used. [6] Version and currency require separate checks: information may remain current for an older product even when a newer document exists for a different version.

Hallucination sits alongside these context-related causes. The National Institute of Standards and Technology (NIST), U.S. Department of Commerce, describes the generation and confident presentation of erroneous or false content as confabulation, a phenomenon also commonly called hallucination. [12] In this survey, 23.6% select model-generated information unsupported by the available sources. [6] The finding is relevant, but it provides no count of hallucinated answers or independently verified rate of model failure.

These percentages should not be combined. Respondents could identify several causes, so the figures cannot be added into a share of failures attributable to context or used to establish a dominant cause. Their practical value lies in the range of problems businesses report and the different responses those problems require.

The response has to match the cause. If an obsolete instruction caused the error, the organization needs to address that source and its availability for future requests. If valid information was applied to the wrong context, it needs to examine how the application selected and interpreted it. Unsupported model generation requires investigation of how the answer was produced. In each case, the corrective action should follow the evidence about the failure.

That evidence also matters for understanding the cost. Before the organization can establish what needs correcting, it has to reconstruct what the application relied on. The next question is how consistently businesses can do that.

To correct a wrong answer, an organization first has to trace it back to the information it came from and establish whether that information was suitable for use. For applications in production or near production, the survey asked how consistently organizations could perform a series of checks, from locating the source to determining its applicability. The results reflect respondents’ own assessments of those capabilities. [7]

Each check answers a different question. Identifying the document gives a reviewer somewhere to begin. Identifying the passage and version gives them a specific record to examine. Currency and approval establish the status of the information at the time it was used, while applicability connects it to the request. Together, these checks help the reviewer determine where a correction should begin.

Traceability is particularly important when information changes. A link to a document that has since been updated may leave the reviewer unsure what the application actually used. Retaining the relevant version, its approval status and its effective period allows the organization to reconstruct the source as it stood when the answer was produced.

The “usually” responses deserve a balanced reading. They indicate that many respondents can perform these checks in a range of circumstances. The concern is consistency, where a missing check could affect a consequential decision. Respondents outside the “always” category may still have the capability, and the results do not reveal which checks coexist within an individual organization.

Businesses should test traceability against the decisions their applications support. A practical review could ask someone to reconstruct the evidence behind selected answers, including cases involving changed documents or different product versions. Wherever the reviewer cannot establish what applied, the organization has an unresolved question about the basis for using that answer.

Source assurance therefore belongs in day-to-day control as well as incident investigation. When the information is insufficient, the workflow needs a clear decision: proceed, seek review or defer the request.

Not every request can be answered reliably from the information available. When that happens, the organization faces a decision before anyone acts on the output. Respondents describe different approaches. 21.9% say their AI application normally provides the best available answer regardless of confidence. 13.6% say it is required to decline or defer the request, while 28.0% say it provides an answer that clearly flags uncertainty and asks for human review. A further 26.5% say the approach varies according to risk or use case. [8]

The survey records these approaches; it does not test their effectiveness. The share that declines or defers should be read alongside the human-review and risk-based options. For each application, the useful question is what evidence must be available before an answer can support the intended action.

Where that evidence is missing, pausing a consequential action can be an appropriate control. The workflow should direct the request to someone with the authority and information to resolve it. The user needs to understand the outstanding issue and what will happen next, so that a deferral creates a route to a decision.

Human review requires resources and a sound basis for judgment. A reviewer should be able to examine the relevant source and understand its conditions of use. A warning that identifies conflicting versions or missing approval gives them a specific issue to resolve. A general uncertainty flag leaves more of that work to the reviewer.

Ownership of the source is part of the same process. 5.4% of respondents report no clearly defined owner for deciding which enterprise information AI may rely on, and 6.3% do not know who holds that responsibility. [9] These findings point to a need for clearer ownership in part of the sample. They support a focused discussion about who can approve information, correct it or suspend its use when a problem emerges.

Those responsibilities should connect to the people operating the application and the business teams affected by its outputs. The organization also needs a record of the evidence used, the review performed and the action authorized. Such records provide a basis for examining accountability after an incident. This research does not determine legal liability.

Managing uncertainty is therefore part of the cost question. It includes the resources devoted to preventing an uncertain answer from becoming a harmful decision, and appropriate review serves an operational purpose. The next task is to establish where that work is necessary and where unresolved context makes people repeat it.

Wrong answers are not the only source of cost. Checking that an answer is suitable for use takes resources even when no error is ultimately found. Paligo uses the term “context tax” to describe the additional work and computing resources that can be consumed when AI cannot establish reliable context efficiently. Human verification, repeated retrieval and additional token use are possible components of that cost.

The survey examines activities relevant to this concept. 83.0% of respondents say employees frequently or occasionally need to verify AI answers against original sources. 74.2% report repeated retrieval of the same or similar information with that frequency. [10]

Other checks concern the conditions under which information applies. 71.3% frequently or occasionally encounter a need to establish the relevant product, market, jurisdiction or other context, while 68.9% report checks on which version of conflicting information is correct. [10] Establishing the basis for an answer is a recurring part of enterprise AI use.

The findings do not establish how much of that work is avoidable. A specialist review may be an appropriate safeguard for a consequential decision. Repeated work warrants closer examination when the same source conflict remains unresolved or the same approval gap returns. In those circumstances, the organization needs to establish why the check keeps arising and whether a change to the content or workflow would address it.

Measurement provides only a partial view of this effort. 27.0% say their organization measures employee time spent verifying AI outputs. 21.2% measure duplicated retrieval of the same or similar information, and 15.6% measure cost per reliable or verified answer. [10] The aggregate results do not show which respondents combine these measures with particular verification activities.

Cost per reliable answer offers a useful starting point for internal assessment. The organization needs to define what makes a result usable for the intended task, then record the effort and operating costs required to meet that standard. This makes review and correction visible in the assessment of value and gives necessary assurance an explicit place in the operating model.

Token economics and the cost of a reliable answer#

Only 15.6% of respondents say their organization measures cost per reliable or verified answer. One fifth (20.4%) measure cost per AI request, while 16.1% measure token consumption. These findings suggest limited visibility into the operating costs required to turn an initial request into a dependable result.

Token consumption is one component of that cost. Where services charge for input and output tokens, the information a model processes and generates affects the bill. Additional model requests or unnecessary material supplied to the model can therefore increase the cost of completing a task.

Repeated retrieval provides one area to investigate. Nearly three quarters (74.2%) of respondents report that the same or similar information is retrieved frequently or occasionally. Where this involves further model processing, it can contribute to additional token consumption. Repeated retrieval does not automatically mean more tokens are consumed, however, and verification may be a necessary safeguard. The survey does not quantify token waste or establish the financial savings available from reducing it.

For leaders, the practical next step is to measure the resources needed to complete the task. Record token consumption and charges across the requests required to reach a usable answer, alongside employee time spent checking and correcting it. Investigate whether recurring requests arise from conflicting versions, missing context or information unsuitable for the task.

That creates a basis for testing whether improvements to content and retrieval reduce the total cost of a reliable answer while maintaining its quality. The aim is to identify avoidable processing while preserving the information and checks needed for a dependable result.

The findings therefore identify recurring activities to investigate; the size of a potential context tax remains to be established. The same attention to resources is needed before an application enters routine use, when preparing its supporting information can affect the deployment schedule.

Some of the cost of dependable AI arises before an application goes live, or before it expands. 60.6% of respondents report frequent or occasional delays to deployment or expansion because supporting content was insufficiently current, approved, structured or governed. [11] The work required to make an application ready for use is therefore part of the same cost question.

The survey also asked what caused those delays. Among the 355 respondents reporting any such delay, including rare delays, 46.8% identify content that had not been reviewed or approved. 43.9% identify content that was not structured in a usable way. Respondents could select more than one reason. [11]

In practice, the application may need access to material whose approval is incomplete, or a content collection may require work before its instructions and conditions can be used reliably. Identifying those requirements during planning gives the relevant teams a defined task and allows the delivery schedule to account for it.

Unreliable outputs can also interrupt plans to expand. In the separate question about consequences, 17.5% report delayed deployment or expansion as a result of incorrect or unreliable outputs during the previous 12 months. [3] That finding concerns the effects of outputs already produced, while the content-readiness question asks about the state of the supporting information. The two findings should be assessed separately.

Either type of delay can postpone an application’s intended benefit and extend the commitment of project resources. The survey measures neither the duration nor the monetary value of those delays, and some pauses may be appropriate decisions to withhold an application until it is ready. Recording the reason for a delay, the work required and its duration would help businesses understand its effect on the investment case.

A deployment plan should therefore identify the information the application will rely on and who will prepare it. Where readiness varies, leaders can consider a limited deployment using information whose suitability has been established, with expansion tied to further evidence. This connects the release decision to the same reliability requirements that will apply during operation.