How Do You Rescue a Failing AI Project? A Week-One Triage

Q: How often do AI projects fail, and why?

RAND's August 2024 report, The Root Causes of Failure for Artificial Intelligence Projects and How They Can Succeed, starts from an estimate rather than a finding: more than 80% of AI projects fail, twice the rate of failure for information technology projects that do not involve AI (RAND, RR-A2680-1, 2024-08-13). RAND introduces it with "by some estimates": a figure it cites, not a rate it measured. Its notes trace the figure to a July 2022 Fortune article and a November-December 2023 Harvard Business Review article. RAND's own contribution is a list of causes. Its authors interviewed 65 data scientists and engineers with at least five years of experience building AI/ML models, 50 in industry and 15 in academia, between August and December 2023. The industry interviews produced five leading root causes: - Stakeholders misunderstand or miscommunicate the problem AI is meant to solve. - The organization lacks the data to train an effective model. - The organization focuses on the latest technology rather than on real problems for its intended users. - Infrastructure to manage data and deploy finished models is inadequate. - The technology is applied to problems that are too difficult for AI. Three limits apply. "Failure" meant a project the organization perceived as one, so this is testimony, not a measured rate. Most interviewees were non-managerial engineers, which RAND says may skew the results toward leadership failures. And the scope was machine-learning projects: those that simply prompted a pretrained language model were excluded, so a pilot built that way sits outside what RAND studied. Use the list as questions to put to your project, not as a diagnosis.

Last updated: October 2026. External figures are attributed and dated in the text.

In short: Do not start by fixing a failing AI project, and do not start by cancelling it. Start with a one-week triage that ends in a written verdict: recover, re-scope or retire. RAND's August 2024 report, drawing on 65 interviews with experienced AI practitioners, cites estimates that more than 80% of AI projects fail and names five root causes. In our reading, four are about how a project was framed, fed and run rather than about the model. NewGenApps' AI Rescue runs that triage in week one, with a real off-ramp, inside a fixed-scope engagement of about 90 days that starts from $25,000.

Should you rescue a failing AI project or cancel it?

Neither yet. Both are outcomes of a decision, and the decision needs a short, evidence-based look at what exists. A triage returns one of three verdicts. Recover: the problem is still worth solving and a costed path to production exists. Re-scope: the problem is real, but the target as set cannot be reached or measured, so it is narrowed. Retire: the evidence says the project should not be carried further, and says why. A reasoned "retire" is a successful result. It costs the triage, not a second failed relaunch.

If you are not yet sure the project has stalled, the five-signal self-check is quicker, and why AI pilots fail covers the upstream causes.

How often do AI projects fail, and why?

RAND's August 2024 report, The Root Causes of Failure for Artificial Intelligence Projects and How They Can Succeed, starts from an estimate rather than a finding: more than 80% of AI projects fail, twice the rate of failure for information technology projects that do not involve AI (RAND, RR-A2680-1, 2024-08-13). RAND introduces it with "by some estimates": a figure it cites, not a rate it measured. Its notes trace the figure to a July 2022 Fortune article and a November-December 2023 Harvard Business Review article.

RAND's own contribution is a list of causes. Its authors interviewed 65 data scientists and engineers with at least five years of experience building AI/ML models, 50 in industry and 15 in academia, between August and December 2023. The industry interviews produced five leading root causes:

Stakeholders misunderstand or miscommunicate the problem AI is meant to solve.
The organization lacks the data to train an effective model.
The organization focuses on the latest technology rather than on real problems for its intended users.
Infrastructure to manage data and deploy finished models is inadequate.
The technology is applied to problems that are too difficult for AI.

Three limits apply. "Failure" meant a project the organization perceived as one, so this is testimony, not a measured rate. Most interviewees were non-managerial engineers, which RAND says may skew the results toward leadership failures. And the scope was machine-learning projects: those that simply prompted a pretrained language model were excluded, so a pilot built that way sits outside what RAND studied. Use the list as questions to put to your project, not as a diagnosis.

What does a week-one triage involve?

In AI Rescue the first week is a structured forensic of the existing project. It produces a root-cause diagnosis, a verdict and a register of the dependencies a recovery would rest on, in six parts:

Take delivery of what exists. The code, data access, any evaluation artifacts, the integration points, the run history and the account of how the project stalled.
Hear from the people involved. At least one person who worked on or commissioned the project has to be available that week to recount what happened.
Test each finding against the record. A finding enters the diagnosis only if it traces to evidence in the code, the data or the run history, not to impression.
Map findings to root causes. Each goes under one or more of four headings: data, evaluation, integration and ownership.
Define the production target and cost the path. For a project worth continuing, state what "working in production" would mean and what reaching it would take.
Write the verdict. Recover, re-scope or retire, with the reasoning and the conditions that would change it.

Your sponsor is needed that week for the verdict itself. A re-scope needs the narrowed target agreed before further work starts; a retire closes the engagement with the written rationale.

What questions frame the verdict?

Six questions help frame it, one for each way a project fails: the wrong problem, missing data, technology ahead of the user, missing infrastructure, a problem too hard for AI, and no owner. Each answer points toward recovering the project or retiring it. The table follows RAND's five causes, plus ownership. The verdict itself rests on the diagnosis under the four headings (data, evaluation, integration and ownership) and on the three questions in the next section.

The week-one questions, mapped to the verdict

Reason a project fails	Question the triage asks	Points toward recover or re-scope	Points toward retire
The wrong problem, or no clear success	Who defined the problem, and would they sign today's definition of success?	The problem is real and a named owner will sign a measurable outcome	No one can say what outcome the project exists to change, or a few written rules would do the job
Missing data	Does data like what production will supply exist, and may it be used?	The gaps can be closed from data you already hold	The data does not hold what the task needs and cannot be gathered in time
Technology ahead of the user	Was the approach chosen for the user's problem or for the tool?	A simpler or more established approach would meet the need	Remove the technology and no user need remains
Missing infrastructure	What stands between this model and a live process?	The gap is engineering work of a known size	The path to production needs investment no one will fund
A problem too hard	At the bar the decision needs, has anything come close on real inputs?	Results so far on real inputs are near the bar, or there are none yet and nothing in the record rules the approach out	Results are far from the bar, and the gap lies in the approach, not the build
Ownership and commitment	Who owns the outcome, and is the commitment funded beyond the pilot?	An owner exists or can be named this week	No one with budget will own the outcome

The table is our reading of RAND's causes, not RAND's framework. RAND's industry findings add that 84% of interviewees cited at least one leadership-driven cause, such as setting the wrong problem or underestimating the time needed, as a primary reason projects fail. If the data row is where your project is stuck, the standard we hold any data path to is on the data-integrity contract page.

How is the recover, re-scope or retire call made?

The call reduces to three questions, each answered in writing: is the problem still worth solving, does the evidence so far give a credible reason to expect the approach can reach the required bar on real inputs (the bar itself is agreed and measured in the weeks that follow), and is the path from here to production known and fundable? Yes to all three is recover. A real problem recoverable only with a narrowed task or an agreed data fix is re-scope. A problem not worth solving, an approach the record shows cannot reach the bar, or a path no one will fund is retire.

The three verdicts at a glance

Verdict	What it says	What you receive	What happens next
Recover	A costed path to production exists	A costed, sequenced recovery plan and a defined production target	Recovery continues through stabilize-and-verify and relaunch; the fix is checked by someone other than its builder
Re-scope	Recoverable only with a narrowed task or a data fix that has to be agreed	The same plan, for a narrowed target	The narrowed target is agreed in writing before any further work
Retire	The evidence says not to carry this further, and why	A written rationale and the conditions that would change the verdict	The engagement closes; no further weeks are committed

The verdict states what evidence would reverse it, so a sponsor can see what would have to change. A reasoned "retire" meets the acceptance criterion for the verdict in full: it counts as delivered, not as a failed engagement.

When is retiring the project the right call?

When the evidence shows that the project cannot clear the bar it was meant to clear, was never needed, or has no funded way to production. One of RAND's industry interviewees describes the "never needed" case: teams told to apply machine learning to data whose main patterns a few if-then rules could have captured. Such projects might succeed in a narrow sense but were never necessary. Retiring the AI approach is not dropping the goal: the verdict can point to a rules-based or process fix.

One case is usually a re-scope, not a retire. RAND notes that AI models are inherently probabilistic, and that leaders who expect certainty lose faith when a model falls short. In our judgment, if the decision needs a guarantee a probabilistic component cannot give, the fix is typically to let a person or a fixed rule decide and have the model assist.

Who should make the call on a failing AI project?

Ideally someone with no stake in the project's history. In a 1999 study, Mark Keil and Daniel Robey interviewed 42 information-systems auditors about troubled software projects. They report that no factor always turned a project around, but that in the majority of cases the move away from the failing course was triggered by actors such as senior managers, internal auditors or external consultants. Through de-escalation, they note, a troubled project may be turned around or sensibly abandoned. The study is exploratory, rests on auditors' judgments and predates modern AI: direction, not a rule.

Our reading is that the people closest to a project are the least free to call it. Whoever makes the call, ask what they need from you in week one, what you will hold in writing at the end, and whether they earn more if the verdict is recover. How to choose an AI partner covers the wider checks.

What does AI Rescue cost, and what is the off-ramp?

AI Rescue starts from $25,000, runs about 90 days at a fixed scope, and has a real off-ramp after week one (NewGenApps engagements page, 2026-09-29). That is the purpose of the first week: a retire verdict ends the engagement there, rather than committing the remaining weeks to a foregone conclusion. The fee is credited toward a subsequent production build, on the terms in the engagement letter.

Ask any firm what you pay if the verdict is retire, and get it in writing before you sign. Prices across the ladder are in how much AI consulting costs; full terms are on the engagements page.

When is a rescue the wrong tool?

When there is nothing yet to rescue, or the system already works. Match the situation to the rung:

An idea not yet tested on real data: a proof of concept comes first; see what it should prove.
No clear view of where AI pays off: start with AI in a Day or AI Compass.
A validated use case that has to run in production: see what an AI production build includes and the checks to run first in AI production readiness.
Not sure which applies: which AI engagement you need sorts it.