The pattern is now familiar enough that most senior leaders can describe it without prompting. The organisation commissions an AI proof of concept. The POC produces encouraging results. A steering committee reviews the work, congratulates the team, and commissions the next POC. Twelve months later, the organisation has fifteen proofs of concept and no models in production. Twenty-four months later, several of the original POCs have been rerun by different teams, because the first version is lost on someone’s laptop or trapped in a sandbox environment that was decommissioned when a cloud contract was renegotiated. Meanwhile, the CEO is asking, with some impatience, what has happened to all the investment in AI.
This pattern has a name — the Proof-of-Concept Trap — and in my experience it affects the majority of enterprise AI functions. The consulting market has a standard answer: deliver more POCs, more efficiently, with better tooling. The answer is wrong. Adding POCs to an organisation stuck in the trap is like adding passengers to an already-overweight aircraft; the problem is not lift, it is load-bearing capacity. The organisation cannot productionise what it already has, and producing more of the same only exacerbates that failure.
What follows is an attempt to name the structural causes of the trap, which I think are four, and to sketch the organisational changes that allow an enterprise to escape it. A theme runs through all four causes: the trap is not a failure of effort, skill, or individual commitment. It is a predictable consequence of the incentives that operate in most enterprise AI functions. Organisations that try to escape by exhortation — “ship more models” — will fail. Organisations that change the structure will succeed.
The first cause: POCs are cheap and look productive
The fundamental asymmetry is this. A proof of concept costs between ten and fifty per cent of what a production model costs. It produces an impressive demo, a deck, and a data scientist who looks genuinely busy. It requires no cross-functional negotiation with IT, no security review, no commercial conversation with procurement, no legal sign-off on terms of use, and no capital commitment to infrastructure. The sponsor can show the POC to their board, log a win, and move on. The data science team can log a delivery and move on. Everyone has, in a narrow sense, succeeded.
Production, by contrast, costs ten to fifty times more than the POC. It requires all of the cross-functional work the POC avoided. It produces a model that must be monitored, maintained, retrained, documented, and eventually retired. It exposes the organisation to operational risk it did not previously carry. It commits the sponsor to ongoing resource allocation, not a one-off engagement. The natural bias of any enterprise reward system is therefore toward POCs: they are cheaper, faster, less risky to the individuals involved, and they produce visible outputs. The alternative — production — is slower, more expensive, and produces outputs that are only visible once they have been running for months.
Adding POCs to an organisation stuck in the trap is like adding passengers to an already-overweight aircraft. The problem is not lift, it is load-bearing capacity.
An organisation that evaluates its AI function on the number of POCs delivered is optimising for exactly the wrong metric. This is not a subtle mistake; it is a widespread and expensive one. The number of POCs is a leading indicator of activity, but it is a misleading indicator of value. The useful metric is the number of models in production, weighted by the business value each is demonstrably producing. An organisation that substitutes the former for the latter will stay in the trap indefinitely, because its incentives are aligned with staying there.
The second cause: there is no production path
The second cause is more quietly ruinous. In many organisations, the path from a working POC to a production model does not exist as a defined capability. There is no MLOps platform. There is no governance process ready to assess the model. There is no infrastructure team with a queue for AI workloads. There is no monitoring. There is no incident response for model failures. The data scientist who has built a promising POC is told, implicitly or explicitly, that productionising it is their problem, and that the organisation will support them in some vague way if they ask.
Under these conditions, the data scientist has two rational choices. They can attempt to productionise the POC through heroic individual effort, becoming, in the process, the single point of failure for a system the organisation depends on. Or they can quietly move on to the next POC, where the work is more tractable and the political environment is more rewarding. Most data scientists choose the second option, because the first destroys their career. The POC they built is archived, its insights are lost, and the organisation learns nothing from the investment it has made.
The response I sometimes hear to this description is that such an organisation deserves to stay in the trap — the data scientists should push harder, take ownership, force the productionising conversation. This is, I think, naïve. The data scientist is not the right person to build the organisation’s MLOps capability. They are the person who needs it to exist. An enterprise that expects its data scientists to build the productionising path as a side-effect of their model-building work has misunderstood its own operating model, and is blaming the wrong people for the consequences.
The third cause: sponsor rotation
The sponsor who commissioned the POC is, in my experience, rarely the sponsor who signs off on productionising it. Enterprise leadership cycles are eighteen to thirty-six months for most roles; a serious AI use case takes twelve to twenty-four months from POC to production. The arithmetic is unforgiving. The sponsor who championed the idea has, by the time the model is ready to go live, often moved on to a new role, a new division, or a new company. Their successor has their own priorities, has no emotional investment in the predecessor’s POC, and discovers — on day one — that they have inherited an AI model they did not commission, which comes with risks they did not assess, and which the AI-CoE is now asking them to sponsor into production.
The rational response, from the successor’s point of view, is to deprioritise the model. It was not their idea. It carries reputational risk if it fails. The value of shipping it accrues to a predecessor who is not around to share the credit. This is not cynicism; it is predictable behaviour under the incentives. A well-designed AI function recognises this dynamic and builds mechanisms that make it harder for new sponsors to quietly drop inherited projects. Organisations that rely on goodwill and memory to productionise work commissioned years earlier are relying on something the organisational system does not produce reliably.
The sponsor who commissioned the POC is rarely the sponsor who signs off on productionising it. Enterprise leadership cycles and AI delivery cycles are mismatched, and the mismatch kills projects.
The fix here is structural. Use case sponsorship should not be tied to an individual; it should be tied to a business unit or function, with the specific individual responsible at any time named in the project record. When a sponsor moves on, their successor inherits the full portfolio including in-flight productionising work, and the AI-CoE reviews the portfolio with the new sponsor within thirty days of their arrival. This turns a political vulnerability into a documented transition. It does not eliminate the tendency to deprioritise inherited work, but it exposes the decision to sunlight, which is usually enough.
The fourth cause: success criteria drift
The final cause is more subtle and more common. A POC is commissioned to answer a specific question: can we do X? The commissioning conversation is usually hopeful and slightly fuzzy. The data science team, given a fuzzy question, produces a fuzzy answer: we can do X, approximately, with caveats. The steering committee receives the fuzzy answer and interprets it in whichever direction is most politically convenient at that moment. If the appetite for production is high, the answer is interpreted as a success. If the appetite is low, it is interpreted as requiring further investigation. Either interpretation is defensible, because the original question was fuzzy.
This is a failure of intake, not a failure of delivery. A use case that arrives at the AI-CoE without a clear, quantified, pre-committed success criterion produces a POC whose conclusion can be rewritten after the fact. Organisations that insist on a pre-committed criterion — a specific metric, a specific threshold, a specific business consequence attached to each — find that some of their POCs succeed, some fail, and both outcomes are usable. The POCs that succeed are productionised automatically, because the pre-commitment bound the sponsor to do so. The POCs that fail are stopped, their insights captured, and the team moves on. Both outcomes are better than the common alternative, which is a perpetual state of ambiguous success that neither progresses nor terminates.
What a serious escape looks like
The organisations that have genuinely escaped this trap, in my experience, made four structural changes. None of them is easy, but all are available to an organisation with sufficient executive sponsorship.
First, they refuse to approve any use case that does not commit, at intake, to production deployment. Not “we’ll see how the POC goes”. A specific commitment, from a named sponsor, to productionise if the POC meets its pre-committed success criterion. This single change kills off a substantial fraction of low-quality use cases at the door, which is exactly what should happen.
Second, they build the production path before the first POC runs on it. MLOps platform, monitoring, governance process, incident response, retirement procedures — all of it stood up and tested on a low-stakes use case before the organisation commissions work that will flow through it. This is the opposite of the usual sequence, which builds the production path reactively in response to the first POC that is ready to productionise. Reactive build is always slower, more expensive, and more fragile than pre-built.
Third, they decouple sponsorship from individuals. Business units own use cases, not individuals. A sponsor leaving the organisation does not end the project; it triggers a documented handover. This feels bureaucratic, and it is. Bureaucracy is the price of operating at scale.
Fourth, they track and publish production-weighted metrics rather than POC-count metrics. The AI-CoE’s monthly report leads with the number of models in production, the aggregate business value those models are producing, and the number of retired models. The number of POCs is reported further down, as a leading indicator, and its trajectory is expected to decline as the function matures — which is the opposite of what a growth-oriented narrative would predict.
A harder truth
There is a harder truth behind all of this, and it is the one senior leaders most often decline to confront. Many enterprises are structurally not equipped to run AI in production. They have the budget for POCs but not for the infrastructure, the governance, the monitoring, and the ongoing operation that production requires. The POC Trap is, for these organisations, not a problem to be solved but a default state — the point at which their investment runs out and something harder would be needed.
For such organisations, the honest response is not to run more POCs faster. It is to face a strategic choice: either invest in the production capability properly, or acknowledge that AI experimentation is what the organisation does and stop pretending otherwise. Both are defensible answers. The indefensible answer is the one most commonly chosen, which is to continue commissioning POCs, continuing to fail at productionising them, and continuing to present this pattern to the board as if it were a temporary phase that additional effort will resolve. It is not a temporary phase. It is the structure.
The test is straightforward. At the end of next year, will your organisation have more models in production than it does today? If yes, what specifically is being done to make that happen? If that specific thing is not happening, the answer is no, regardless of how many POCs are currently in flight. Leaders who want to escape the trap must first be willing to name it honestly. Most will not. Those who do will find the path out is difficult but navigable.
A fuller treatment of use case intake, production pathways, and the MDLC appears in EIS-001: AI Center of Excellence Guide, Chapters 7 and 8, published in the Enterprise Intelligence Series.