When to Kill an AI Project… And How to Know

Not every AI project succeeds. In fact, most don’t.

According to recent research, the numbers paint a sobering picture. MIT’s 2025 “GenAI Divide” report found that approximately 95% of generative AI pilots fail to deliver measurable impact on financial results. RAND Corporation’s analysis puts the broader AI failure rate at over 80%, twice the failure rate of traditional IT projects. And S&P Global’s 2025 survey revealed that 42% of companies abandoned most of their AI initiatives, up dramatically from just 17% the previous year.

These aren’t abstract statistics. They represent millions of dollars in investment, countless hours of effort, and significant opportunity costs. The average organization scrapped 46% of AI proof-of-concepts before they reached production.

But here’s what these numbers don’t tell you: failure isn’t always failure. Sometimes killing a project is exactly the right decision. The real problem isn’t that AI projects fail; it’s that organizations often fail to recognize when a project should be killed, leading them to pour additional resources into doomed initiatives.

Understanding when to pull the plug – and having the courage to do it – is one of the most important ROI skills an organization can develop.

The Sunk Cost Fallacy in AI Investments

The Concorde supersonic jet flew for 27 years after it became clear it would never be profitable. Investors had already committed $2.8 billion, and the psychological weight of that investment kept the project alive long past its rational end date. This phenomenon is so well-documented that “Concorde fallacy” became synonymous with what economists call the sunk cost fallacy.

AI projects are particularly susceptible to this trap.

The sunk cost fallacy is our tendency to continue investing in something (time, money, effort, reputation) simply because we’ve already invested so much, even when the rational decision would be to stop. Past costs are irrecoverable; rational choices should focus only on future costs and benefits. But emotionally, that’s incredibly hard to do.

In AI investments, sunk cost thinking manifests in several ways. Organizations that have spent months training models, building infrastructure, and reorganizing teams around an AI initiative find it nearly impossible to walk away. The licensing fees have been paid. The change management efforts have been made. The board has been promised results.

This creates what one researcher calls a “deeper and deeper hole” effect. Companies continue to invest additional resources hoping to recover what they’ve already spent, even as evidence mounts that the project won’t deliver value. One study of IT projects found that while average cost overruns were 27%, one in six projects ended up with cost overruns of around 200%, often because organizations couldn’t bring themselves to pull the plug when they should have.

The opportunity cost compounds the problem. Every month spent on a failing AI project is a month not spent on initiatives that might actually succeed. Everything you didn’t do while pursuing a doomed project represents real value lost to the organization.

Failing Fast vs. Premature Abandonment

There’s a tension in the advice organizations receive about AI projects. On one hand, experts counsel “failing fast” – running lean pilots to quickly identify what works before scaling. On the other hand, AI projects genuinely do take time to mature. Benefits often follow a J-curve pattern, with front-loaded costs and delayed returns.

How do you distinguish between a project that needs more time and one that should be terminated?

The key is understanding what kind of problem you’re facing. Some failures are fundamental: the approach itself is flawed. Others are execution failures: the right idea implemented poorly. And some apparent failures are simply projects experiencing normal growing pains on the path to success.

A fundamental failure might look like an AI system that can’t achieve the accuracy needed for its business purpose, regardless of how much data or tuning you apply. An execution failure might be the same system struggling because of data quality issues that could be resolved with proper investment. Growing pains might be a system that’s technically working but hasn’t yet achieved the adoption needed to demonstrate value.

The distinction matters enormously for decision-making. Fundamental failures should be killed quickly. Execution failures might warrant a pivot: same problem, different solution. Projects experiencing growing pains often just need patience and continued support.

Red Flags by Project Stage

Different warning signs emerge at different stages of an AI project’s lifecycle. Understanding what to watch for – and when – can help you catch problems before they become catastrophic.

Concept Stage Red Flags

At the earliest stage, warning signs are often about preparation rather than performance. Projects that can’t clearly articulate success metrics before implementation begin are at high risk. If you can’t define what success looks like, you certainly can’t measure whether you’ve achieved it.

Lack of baseline data is another early warning sign. Without knowing current performance, you can’t calculate improvement. Organizations that skip baseline measurement often find themselves unable to demonstrate ROI even from successful projects.

Watch also for misalignment between AI capabilities and the actual problem. RAND Corporation’s research found that a primary reason AI projects fail is that stakeholders misunderstand or miscommunicate what problem needs to be solved using AI. Leaders sometimes deploy AI for problems better suited to traditional methods, or they overestimate AI’s readiness for complex tasks.

Pilot Stage Red Flags

During proof-of-concept, the most obvious red flag is performance worse than baseline. If your AI system performs worse than the manual process it’s meant to improve, that’s a serious problem requiring immediate attention.

User rejection is equally concerning. A technically successful model means nothing if the people who need to use it won’t adopt it. One fraud detection model was technically flawless but failed because bank employees didn’t trust it; without clear explanations or training, they simply ignored the model’s alerts.

Cost explosion is another warning sign. If pilot costs significantly exceed projections, production costs will likely be even worse. AI projects often have hidden costs in data preparation, integration, and ongoing maintenance that only become apparent during implementation.

Quality degradation over time can indicate that your model isn’t generalizing well to real-world data. Models often perform well in controlled testing environments but struggle when faced with the messiness of production data.

Scale Stage Red Flags

When projects move to production, different problems emerge. Benefits not materializing as projected is the most direct warning sign. If your business case assumed certain improvements and they’re not appearing, the gap needs explanation.

Adoption stalling indicates that the change management side of your implementation isn’t working. Even excellent AI systems fail if people don’t use them. Track not just whether the system is available but whether it’s actually being used in daily operations.

Escalating maintenance costs can undermine ROI even when the system technically works. AI systems require ongoing monitoring, retraining, and tuning. If those costs are higher than expected, your business case may no longer make sense.

Production Stage Red Flags

For systems that have reached steady-state operation, watch for ROI deterioration over time. Markets change, competitors adapt, and your baseline shifts. A system that delivered value last year may not deliver value next year.

Model drift (where performance degrades as real-world conditions change) is common and often underestimated. Without continuous monitoring, you may not notice until the degradation becomes severe.

Better alternatives emerging is perhaps the most uncomfortable red flag. Technology moves fast in AI. A system that represented cutting-edge capabilities two years ago may now be significantly inferior to available alternatives. Continuing to run an outdated system has real opportunity costs.

The Kill/Pivot/Persevere Framework

When red flags appear, organizations face three basic choices: kill the project entirely, pivot to a different approach, or persevere through temporary difficulties. Here’s a framework for making that decision.

When to Kill

Kill a project when the fundamental approach is flawed. This means the core assumptions about what AI can do, what data is available, or what the business actually needs have proven incorrect. No amount of iteration will fix a fundamentally flawed approach.

Specific kill indicators include technical impossibility with current technology, where the AI simply cannot perform the required task at acceptable accuracy regardless of data or tuning. Market conditions changing to eliminate the need is another kill signal. If the problem you’re solving no longer exists, the solution has no value. Strategic misalignment occurs when business priorities shift and the project no longer serves organizational goals.

Killing a project isn’t failure; it’s discipline. The resources freed up can be redirected to initiatives with better prospects.

When to Pivot

Pivot when you have the right problem but the wrong solution. The business need is real and AI can address it, but your current approach isn’t working.

Common pivot scenarios include changing from custom-built to vendor solutions (research shows that purchased AI solutions succeed about 67% of the time versus only 33% for internal builds), shifting from one model architecture to another, redefining the scope to focus on a narrower, more achievable goal, or moving from full automation to human-AI collaboration.

A successful pivot preserves the learning from the failed approach while applying it to something more likely to succeed. It requires honest assessment of what went wrong and why.

When to Persevere

Persevere when you’re facing temporary setbacks rather than structural issues. Some legitimate reasons to continue despite difficulties include data quality problems that are being actively resolved, adoption challenges that change management can address, performance issues that are improving with iteration, or timeline delays that don’t affect the fundamental value proposition.

The key distinction is whether the problems are solvable with reasonable additional investment. If you can articulate a clear path from current state to success, and that path is economically viable, perseverance may be justified.

But be honest. It’s easy to convince yourself that any problem is temporary. Set clear milestones and timelines. If improvement doesn’t materialize as expected, revisit the kill/pivot/persevere decision.

Conducting an Honest Post-Mortem

Whether you kill, pivot, or eventually succeed, every AI project should conclude with a thorough post-mortem. The goal isn’t to assign blame but to extract maximum learning for future initiatives.

What Did We Learn?

Start with technical lessons. What did we discover about our data, our infrastructure, and our capabilities? What assumptions proved incorrect? What unexpected challenges emerged?

Then examine organizational lessons. Was our team properly staffed and skilled? Did we have appropriate executive sponsorship? Were stakeholders aligned on goals and expectations?

Finally, consider process lessons. Did our project management approach fit the experimental nature of AI? Did we build in appropriate checkpoints for go/no-go decisions? Did we measure the right things?

What Would We Do Differently?

Convert lessons into actionable recommendations. Be specific. “Better planning” isn’t useful guidance. “Conduct data quality assessment before committing to model architecture” is actionable.

Consider what you’d change at each stage: project selection, team composition, technical approach, measurement framework, stakeholder communication, and decision-making process.

How Do We Apply This to Future Projects?

Lessons learned have no value unless they’re actually applied. Document findings in a format that future project teams will actually read. Update your project selection criteria, your risk assessment frameworks, and your stage-gate processes based on what you’ve learned.

Some organizations create “project graveyards”: repositories of documentation from failed projects that new teams can reference. Others require post-mortem findings to be presented to leadership before new projects are approved. The specific mechanism matters less than ensuring that learning actually transfers.

Building an ROI-Driven AI Culture

Individual project decisions exist within a broader organizational culture. The most successful AI organizations build cultures that support honest measurement, appropriate risk-taking, and continuous learning.

Take a Portfolio Approach

Don’t expect every AI project to succeed. The most successful organizations treat AI investments as a portfolio, expecting some failures alongside their successes. This doesn’t mean being careless with resources; it means accepting that experimentation inherently involves projects that don’t work out.

Gartner predicts that over 40% of agentic AI projects will be canceled by 2027. Organizations that accept this reality can plan for it. Those that expect every project to succeed will struggle to make rational kill decisions.

Create Psychological Safety for Honest Measurement

People won’t report bad news if they fear punishment for delivering it. Create an environment where teams can honestly assess project health without fear of career consequences.

This requires leadership modeling. When executives acknowledge their own failed initiatives and discuss lessons learned openly, it signals that honest assessment is valued. When executives punish teams for project failures, it guarantees that problems will be hidden until they become catastrophic.

Research consistently shows that psychological safety correlates with innovation. Teams that feel safe to take risks and report failures actually produce better outcomes than teams operating in fear.

Celebrate Course Corrections

Stopping an underperforming project is not failure; it’s discipline. Organizations that treat project termination as shameful will consistently over-invest in losing initiatives. Organizations that celebrate appropriate course corrections will allocate resources more effectively.

When a team kills a project that should be killed, recognize that decision publicly. Acknowledge the courage required to admit a sunk cost and move on. This reinforces the behavior you want to see throughout the organization.

Learn from Both Successes and Failures

Post-mortems shouldn’t be reserved for failures. Successful projects also contain lessons: about what worked, what nearly didn’t, and what could work even better next time.

Create systematic processes for capturing and sharing these lessons. The specific format matters less than consistency. Organizations that learn continuously from their AI experiences will steadily improve their success rates over time.

Final Thoughts

The goal of AI ROI measurement isn’t perfect prediction; it’s informed decision-making. No framework can guarantee success or precisely identify which projects will fail. The uncertainty is inherent to AI’s experimental nature.

What good measurement can do is help you recognize problems earlier, make better decisions about resource allocation, and learn more effectively from both successes and failures. It can help you avoid the sunk cost trap and give you the data needed to make rational kill decisions when they’re warranted.

The best ROI comes from combining the right measurement approach with organizational readiness. Technical capability matters, but so does culture. The organizations that succeed with AI will be those that can honestly assess their initiatives, have the courage to kill projects that should be killed, and systematically learn from every outcome.

Start measuring early. Adjust often. Be honest always. And remember that sometimes the highest-ROI decision is knowing when to stop.

---

Sources

1. MIT NANDA Initiative. “The GenAI Divide: State of AI in Business 2025.” Massachusetts Institute of Technology, 2025. https://fortune.com/2025/08/18/mit-report-95-percent-generative-ai-pilots-at-companies-failing-cfo/
2. RAND Corporation. “The Root Causes of Failure for Artificial Intelligence Projects and How They Can Succeed: Avoiding the Anti-Patterns of AI.” Research Report RR-A2680-1, 2024. https://www.rand.org/pubs/research_reports/RRA2680-1.html
3. S&P Global Market Intelligence. “AI Project Failure Rates Survey 2025.” CIO Dive, March 2025. https://www.ciodive.com/news/AI-project-fail-data-SPGlobal/742590/
4. Gartner, Inc. “Gartner Predicts Over 40% of Agentic AI Projects Will Be Canceled by End of 2027.” Press Release, June 2025. https://www.gartner.com/en/newsroom/press-releases/2025-06-25-gartner-predicts-over-40-percent-of-agentic-ai-projects-will-be-canceled-by-end-of-2027
5. Informatica. “CDO Insights 2025 Survey: The Surprising Reason Most AI Projects Fail.” March 2025. https://www.informatica.com/blogs/the-surprising-reason-most-ai-projects-fail-and-how-to-avoid-it-at-your-enterprise.html
6. WorkOS. “Why Most Enterprise AI Projects Fail – And The Patterns That Actually Work.” July 2025. https://workos.com/blog/why-most-enterprise-ai-projects-fail-patterns-that-work
7. Asana. “How Sunk Cost Fallacy Influences Our Decisions.” February 2025. https://asana.com/resources/sunk-cost-fallacy
8. Leading AI. “Knowing When to Fold ‘Em: How to Avoid the Sunk Cost Fallacy in AI.” November 2025. https://www.leadingai.co.uk/blog/avoid-sunk-cost-in-ai/
9. IBM. “How to Maximize ROI on AI in 2025.” November 2025. https://www.ibm.com/think/insights/ai-roi
10. Harvard Business Review. “How Behavioral Science Can Improve the Return on AI Investments.” November 2025. https://hbr.org/2025/11/how-behavioral-science-can-improve-the-return-on-ai-investments
11. Dataconomy. “Why 84% Of AI Projects Fail – and It’s Not The Technology.” December 2025. https://dataconomy.com/2025/12/10/why-84-percent-of-ai-projects-fail-and-its-not-the-technology/
12. PM Square. “5 Ways Your AI Projects Fail: After Action Reviews and Post-Mortems.” June 2025. https://pmsquare.com/resource/blogs/5-ways-your-ai-projects-fail-after-action-reviews-and-post-mortems/