AI pilot purgatory, also called the AI pilot-to-production gap, is the state in which an organisation keeps proving that AI works but never puts it into production. Pilots succeed, demos impress the leadership team, and yet almost nothing reaches the business at scale. It is rarely a technology failure. It is an operating model failure: nobody designed the route from “this works” to “this runs”.
The condition goes by several names: pilot purgatory, proof-of-concept prison, POC purgatory, pilot paralysis and the pilot-to-production gap. All of them describe the same pattern: a growing portfolio of experiments and a flat line of production value.
This matters at board level because the frameworks boards are now expected to follow assume that someone owns AI once it is live. ISO/IEC 42001:2023 requires roles, responsibilities and authorities for AI to be assigned and communicated. The NIST AI Risk Management Framework (AI RMF 1.0) makes accountability structures part of its Govern function. A pilot that drifts into daily use without an owner satisfies neither.
The scale of the problem is well documented. Only 11.7% of organisations move directly from AI validation into full production rollout (Source: AvePoint, State of AI Report, 2025). More than two-thirds of organisations expect 30% or fewer of their generative AI experiments to be fully scaled (Source: Deloitte, State of Generative AI in the Enterprise, 2024).
This analysis draws on a two-part conversation with Luise Freese, a developer, consultant and author who helps organisations move AI into production. We have translated her argument into the questions an executive team needs to answer.
Why do successful AI projects get stuck in the pilot-to-production gap?
Successful AI projects get stuck between pilot and production because production is a different environment, not a bigger pilot. A pilot needs enthusiasm, a small budget and a tolerant audience. Production needs an accountable owner, an operational budget, integration with real systems, monitoring, and a plan for when things go wrong. Most organisations fund the first and never decide who provides the second.
Freese describes three things that typically happen after a pilot succeeds:
- It disappears. The pilot team moves on, the budget ends, and nobody owns the next step. A proven idea quietly dies.
- It becomes permanent by accident. A demonstration tool slips into a real workflow and stays there, without the monitoring, resilience or support that production demands. This is the most dangerous outcome, because the risk is invisible until it fails.
- The easy work wins. The pilots that advance are the ones easiest to operationalise, not the ones with the most value. The high-potential ideas stall precisely because they touch more systems and more people.
How can you tell if your organisation is in pilot purgatory?
You can tell by comparing how quickly pilots start with how rarely they finish. The warning signs are consistent across sectors:
- New experiments launch in weeks; nothing moves to production in months.
- Successful prototypes have no named owner once the pilot ends.
- “Temporary” AI tools are embedded in live workflows with no support model.
- Progress depends on a few committed individuals rather than a repeatable process.
- Every post-pilot decision is debated from scratch, because no route to production has been agreed.
Freese sums up the effect well: activity increases and experience grows, but the organisation’s ability to deliver systems that run reliably does not improve. In her words, many organisations are “very good at starting, but not designed to finish.”
The AIPEX view: pilot purgatory is a leadership gap, not a technology gap
At AIPEX we see the same root cause behind almost every stalled AI portfolio. Pilots are funded as experiments and judged as demonstrations, but nobody at executive level owns the transition between the two. Technology teams can build a pilot. Only the executive team can decide who owns it, what budget sustains it and what it is allowed to do.
That makes pilot purgatory a governance question for the CEO, CFO and board, not a delivery question for the IT function. Three executive decisions unlock it:
- Ownership: every pilot has a named business owner for the production outcome before it starts, not after it succeeds.
- Funding: the business case for a pilot includes the operational cost of running it: monitoring, maintenance, model updates and user support.
- Selection: pilots are chosen for value and ranked honestly against the effort to operationalise them, so the portfolio is not quietly filled with the easy options.
The Funding decision sits naturally with the finance function, which is why we argue the CFO should own the AI business case; see The CFO’s AI Mandate.
What should the board ask before approving another AI pilot?
The board should ask what happens if the pilot succeeds, before it asks whether it will. Five questions do most of the work:
- If this pilot succeeds, who owns it in production, by name?
- Which live systems and workflows will it integrate with, and who controls those systems?
- What, specifically, will “production-ready” mean for this use case?
- What will it cost to run for a year, and whose budget pays for that?
- What is the AI allowed to decide on its own, and what must a person approve?
If a proposal cannot answer these, it is not a pilot. It is an experiment with no exit, and it should be approved, if at all, on that basis.
How does an organisation escape pilot purgatory, and what does it require from the executive team?
An organisation escapes by taking one proven pilot through a structured route to production, then making that route the standard for every pilot that follows. Freese’s approach does this in 90 days: map the real workflow and its blockers, assign accountable ownership and minimum governance, then deploy to real users with live monitoring. We set out the executive version in The 90-Day Route from AI Pilot to Production.
From the executive team it requires two things that no technology purchase provides: a named owner with the authority to make production decisions, and a clear statement of what the AI is allowed to do. The second is the subject of Who Owns the AI Agent When It Breaks?
If your organisation has no one at executive level who owns the AI portfolio, that is usually the first gap to close. A Fractional Chief AI Officer provides that accountability without a full-time hire. For a structured review of your current pilots, the Executive Strategy Day maps each one to an owner, a budget and a decision. Our briefing From Pilot to Scale: The Execution Gap covers the same ground for leadership teams.
Frequently asked questions
What is AI pilot purgatory?
AI pilot purgatory is the state in which an organisation runs successful AI pilots that never move into production. The technology works, but nobody owns the route from pilot to live operation, so proven ideas stall, disappear or drift into use without support. For a full explanation, see AI Pilot Purgatory: Why Successful Pilots Never Reach Production →
Who should own an AI pilot once it succeeds?
A named business owner should own the production outcome, and that person should be agreed before the pilot starts. A committee can oversee the work, but it cannot make operational decisions when something fails. For a full explanation, see Who Owns the AI Agent When It Breaks? Delegated Authority for Boards →
How long does it take to move an AI pilot into production?
A well-chosen pilot can reach production in around 90 days: 30 days to map the real workflow and its blockers, 30 days to assign ownership and minimum governance, and 30 days to deploy with live monitoring. For a full explanation, see The 90-Day Route from AI Pilot to Production: An Executive Playbook →