7 Reasons AI Proofs-of-Concept Stall Before Ship

I've lost count of the AI PoCs I've been asked to rescue. The pattern is depressingly consistent: a demo that wowed a room in week three, then six months of "we're almost there," then a quiet Slack channel and a budget that got reallocated to something else. The technology usually works. The PoC dies for reasons that have nothing to do with model quality.

Here are the seven failure modes I see most often, and what actually gets a PoC across the line.

1. Success was never defined in a way you could measure

The single biggest killer. Someone says "we want an AI agent that handles customer tickets," everyone nods, and nobody writes down what "handles" means. Six weeks later the team is arguing about whether 71% resolution is good or bad, because there was never a target.

A real success metric has three parts:

• A number. Not "improve response time," but "median first response under 4 minutes on P2 tickets."
• A denominator. "Correctly categorized" on what set? 100 handpicked tickets, or a random sample from last quarter?
• A comparison. What does the current human process score on the same test? If you don't know, measure it before you build anything.

I now refuse to start a PoC without a written eval set of at least 50 to 200 real examples, scored by a human, with a target the stakeholder has signed off on. When I skip this step to be helpful, I regret it every time. The eval set is the contract. Without it, "done" is a moving target and the PoC never ships because it can always be a little better.

2. Data access was assumed, not negotiated

The demo runs on a CSV someone exported from production. Production runs behind three layers of IAM, a data steward who's on parental leave, and a security review that takes 8 to 12 weeks. Nobody flagged this in kickoff because "we already have the data."

You do not have the data until you have:

1. A service account with read access to the actual source of truth (not an export).
2. A named human who owns that data and has approved the use case in writing.
3. A tested pipeline that pulls it on a schedule, in the environment where the PoC will run.

I ask for all three in week one. If the answer is "we'll sort that out later," that is the risk log's first entry, highlighted in red. On more than one engagement, the data access conversation took longer than the actual build. Better to start it on day one than day sixty.

3. The PoC was built somewhere production can never live

A Jupyter notebook on a laptop with the founder's OpenAI key is not a prototype of a system. It's a prototype of a decision. If the target deployment is AWS behind a VPC with no outbound internet, or a regulated environment where only approved models are allowed, the notebook proves nothing about whether the real thing will work.

My rule: build the PoC on the smallest possible version of the production substrate. For an AWS shop, that means Lambda + API Gateway + the real IAM boundaries from week two, not a Flask app you'll port later. For a client with strict data residency, it means the local LLM stack (Ollama, pgvector, a real embedding model) from the start, not GPT-4 as a placeholder you'll "swap out."

The porting phase is where PoCs go to die. If you skip it because you built on the right substrate from the start, you've eliminated the most common reason a working demo never becomes a shipped product.

4. Nobody owns the thing after it works

Engineering built it. Who runs it? Who gets paged when the vector index goes stale? Who approves prompt changes? Who pays the API bill next quarter?

If those questions don't have names attached before the PoC hits UAT, the project stalls in a limbo I call "successful orphan." It works, everyone agrees it works, and nothing happens because integrating it into an operational team requires headcount decisions nobody wants to make.

I now include an operations RACI in the PoC proposal. Not as a nice-to-have deliverable, as a gate. Before the final demo, we need agreement on:

If a cell is empty two weeks before the go/no-go meeting, the go/no-go conversation becomes about ownership, not about the model. That is the correct conversation to have.

5. The evaluation loop was a one-time thing

The team hand-scored a hundred examples in month two, hit 88%, celebrated, and then never scored anything again. Six weeks later, real user traffic looks nothing like the eval set, the underlying model got updated, the retrieval corpus grew by 40%, and nobody knows if quality went up or down.

This is where PoCs quietly rot. The demo still works when you show it to the CEO. But in production traffic, hallucination rate crept from 3% to 11% and no dashboard shows it.

A production-ready evaluation loop has four parts:

1. A frozen eval set you run on every deployment (regression).
2. A rolling sample of live traffic scored either by an LLM judge or a human reviewer weekly.
3. Drift detection on the inputs: are users asking things the corpus doesn't cover?
4. A dashboard the business owner actually looks at, with two or three numbers, not fifty.

You do not need MLOps maturity to do this. You need a cron job, a Postgres table, and 30 minutes of a reviewer's time per week. I've built this for PoCs in a day and it changes the conversation from "does it still work?" to "here's how it performed this week."

6. Scope crept because there was no v1 that was allowed to ship

Every stakeholder gets to add "just one more thing" between kickoff and launch, and the PoC becomes a Christmas tree of edge cases. By month four, it handles 43 scenarios badly instead of the original 6 well.

The mitigation is boring and it works: write down what v1 does not do, in the same document as what it does do. Explicit non-goals. "This PoC does not handle refund requests. It does not read attachments. It does not respond in languages other than English. Any of those can be a v2." Get that signed.

When someone asks for "just" attachment parsing in week five, you have a document to point at. The ask becomes a v2 conversation, not a scope conversation. This preserves the ship date, which preserves the political capital, which is what actually gets a v2 funded.

I've watched more PoCs die from month-six scope bloat than from any technical issue. The team wasn't wrong to want the extra features. They were wrong to slip them in without moving the deadline.

7. Model drift and dependency rot were treated as future problems

The stack you built on in Q1 is not the stack you're running in Q4. The model version got deprecated. The API added a new required parameter. The embedding model changed its default dimensionality. The vector database released a breaking change. Your prompts, which were carefully tuned against one model's quirks, now underperform against the replacement.

For a PoC that's meant to inform a production decision, this is fatal in two ways. First, if the PoC is more than three months old when the decision is made, its results may not be reproducible on today's stack. Second, if you shipped it and left it, it will silently degrade.

The fixes are straightforward but they need to be in the plan from day one:

• Pin every version. Model IDs, SDK versions, embedding model, DB. In config, in requirements, in Docker.
• Snapshot the eval scores per version. When you upgrade, rerun the eval set. If the numbers move, you find out before your users do.
• Budget for a quarterly refresh. Two engineer-days per quarter to update dependencies, rerun evals, and adjust prompts. This is not optional maintenance; it is the cost of running LLMs in production in 2026.
• Abstract the model behind a thin interface. Not a full framework, just a function that takes a prompt and returns a response. When you need to swap providers or run A/B tests between Claude and a local model, you change one file.

I run a content automation system that has been in production for months. The reason it still works is not that the code is brilliant. It's that when Anthropic released a new Claude version, I could rerun the eval set in an hour, compare scores, decide, and roll forward or back. That capability took maybe half a day to build. It has paid for itself many times over.

What I'd do differently, every time

If you take one thing from this: the reasons PoCs stall are almost never technical. They are contractual, operational, and organizational. The model works. Getting the model to work inside a company is the hard part.

My checklist before I agree to build a PoC now:

1. Written success metric with a target and an eval set. No metric, no start.
2. Confirmed data access with a named owner, in the actual environment. Not "we'll figure it out."
3. Build substrate matches production substrate. No throwaway notebooks.
4. Named operational owner for after ship. Empty RACI cell = no ship.
5. Continuous eval loop from day one, not month six.
6. Explicit non-goals, signed by the person who might ask for them later.
7. Pinned versions, quarterly refresh in the budget, thin model interface.

None of this is glamorous. All of it is the difference between a PoC that ships and a PoC that becomes a slide in someone's retrospective about why the AI initiative didn't work out.

If you're staring at a PoC that's been "almost done" for a suspicious number of weeks, or you're about to start one and want to skip the mistakes, I'm happy to talk. Reach out at lazar-milicevic.com/#contact, or read more on how I approach shipping AI systems on the blog.