Every Enterprise AI POC Dies the Same Way
The first half of 2024 was a lot of enterprise AI POCs. The second half of 2024 is going to be a lot of them quietly disappearing.
I want to write down the pattern while it's still fresh, because I've watched it happen at this point in something like fifty different organizations. The shape is identical almost every time. The shape is not what most of the people writing about enterprise AI think it is.
the pattern
It starts in January. The new fiscal year. The CTO or CIO or some equivalent has a number on a slide that says "AI adoption." They need to pick a thing. They pick a thing. Maybe it's a customer support bot. Maybe it's a knowledge management thing for sales. Maybe it's a contract review tool for legal.
By February the vendors have been selected. There are three of them. One is the incumbent platform vendor (Salesforce, Microsoft, ServiceNow). One is a specialist AI startup. One is a consulting firm that will build it custom. Some combination of the three gets the contract.
By April there's a demo. The demo is great. Everyone gets excited. There's a slide with the executive sponsor's name on it. There might be a press release.
By June the POC is "in production with a pilot user group." There are 30 people using it. It mostly works. There are some glitches. The team is fixing them.
By August it's quiet. The pilot group hasn't grown. The slides about "rollout" have stopped happening. The vendor isn't returning calls as quickly. There's a new initiative starting up around something else.
By December nobody mentions it. The next year's planning is for a different AI project.
This happens approximately 80% of the time. The dollar amounts on the contracts that get quietly let to lapse are real. The cumulative cost of this pattern across the enterprise AI market in 2024 is going to be in the billions.
the conventional explanation
When the pattern is discussed, the usual diagnosis is one of these.
"The model wasn't good enough." Almost never true in 2024. The models are extremely good. The use cases people are picking for these POCs are not that hard.
"The integration was too complex." Usually a symptom, not a cause. The integration is whatever it needs to be. People build vastly more complex integrations for non-AI projects every day.
"The vendor didn't deliver." Sometimes. But the same vendor is succeeding at deployments at other companies, often with the same use case.
"The technology wasn't ready." A common excuse from buyers because it lets nobody at the buying company be the one who failed. Almost always false.
The actual cause is different. The actual cause is that nobody decided who owns the thing after it ships.
the ownership problem
Here is the question that kills enterprise AI POCs. It is a single question.
When this is in production, and it produces an output that is wrong, who is responsible for that?
The answers most organizations have for this question are: "the vendor," "IT," "the business unit that uses it," or "nobody, it's just a tool." All four of these answers fail in production. Let me show you how.
If the vendor owns it, you have built a critical workflow on top of a third party whose roadmap you don't control. The vendor will not keep up with your changes. Your CRM schema will evolve. Your product catalog will evolve. Your support documentation will evolve. The vendor's "AI" will degrade silently as the world drifts away from the data it was tuned on. You won't notice until accuracy is already gone. By then it's hard to recover.
If IT owns it, IT is staffed for keeping the lights on, not for tuning models and managing prompts. There is a 100% chance that the IT team you've handed it to does not include anyone who has thought about an eval harness. By month four they are running it on autopilot. By month six it's a ticket queue.
If the business unit owns it, the business unit doesn't have the technical depth to fix it when it breaks. They will file tickets. The tickets will sit. They will lose trust. They will stop using it.
If nobody owns it, you don't even have these problems. You just have a dead deployment.
The POCs that succeed in 2024 are the ones where the answer to "who owns this in production" is given a specific name on day one, that name is a person who has both the technical depth and the operational authority to actually own it, and the org chart was redrawn around the answer before any code was written. This is rare. It involves real political work. It is often the hardest part of the engagement and it is almost never the part the executive sponsor wants to focus on.
the second-day problem
The other thing that kills these POCs is that nobody figured out where the data comes from after launch.
A POC ships with a static knowledge base. You loaded 800 pages of product documentation into the retrieval store. The bot works. It can answer questions about those 800 pages.
It is six months later. Product has shipped 14 new features. The product documentation has been updated. The 800 pages are now 1,100 pages, with material changes to about 300 of the original ones. Who updated the store?
In most of the dead deployments I've seen, the answer is "nobody, because nobody owns it, because we never talked about that part." So the bot is now answering questions from stale documentation, sometimes confidently wrong. Users discover this. Trust evaporates. The bot is now worse than no bot, because it actively misleads. The deployment becomes net negative. Someone pulls the plug.
The fix is that the data refresh pipeline is part of the POC, not an afterthought. You ship the bot and the pipeline that keeps its source of truth in sync. This is unglamorous engineering. It is the work that determines whether the POC actually survives. It is the work that almost never gets prioritized because it doesn't make for a good demo.
what to do about it
If you are running an AI POC right now, the questions you need to answer before launch, not after, are:
Who is the named, accountable owner of this system in production? That person needs to exist, by name, and they need to have the bandwidth.
What is the data refresh strategy? Specifically: when does the underlying source change, how do we detect it, how do we re-embed and re-index and re-tune, and who does it.
What is the monitoring strategy? How do we know when the system starts performing worse? Not "we'll know" but specifically: what dashboard, what alerts, what threshold.
What is the escalation path when the system is wrong? Who decides whether it should be paused, retrained, or replaced.
If you cannot answer these questions with names and processes, you do not have a POC. You have a demo. There is a real distinction.
why this isn't getting fixed
The reason this pattern persists is structural. The people buying enterprise AI in most large companies are buying it for two reasons. The first is that they need to show their board they're "doing something" on AI. The second is that they genuinely think the technology might help.
The first reason gets the contract signed. The second reason is what would need to drive operating model decisions. But the first reason is the dominant one in 2024, and the first reason is satisfied by signing the contract, regardless of what happens to the deployment afterward.
This will get better. The next wave of buyers, the ones doing AI procurement in 2025 and 2026, will have watched the first wave's POCs die. They'll know to ask the questions. The contracts will start including operating model requirements. The vendors will start including data-refresh services. The maturity curve is just slower than the hype.
In the meantime, if you're a builder, sell to the buyers who answer the ownership question first. They're rare. They're the ones whose deployments will still be running in 2027. They're the customers you want.