The Enterprise AI Gap: Why Proof of Concept Success Doesn't Predict Production Value

The PoC paradox

Enterprise AI has a dirty secret: proof of concept success is a terrible predictor of production value. We’ve watched this play out across 23 initiatives, in organisations ranging from $200M to $15B in revenue, across financial services, healthcare, retail, and industrial sectors.

The pattern is so consistent it deserves a name. We call it the PoC paradox: the factors that make a proof of concept succeed are often the same factors that prevent the solution from delivering value at scale.

What makes PoCs succeed

Successful proofs of concept share common characteristics:

Curated data. The data used in a PoC is typically clean, complete, and representative, because someone spent weeks preparing it. In production, data arrives messy, incomplete, late, and from sources that nobody told the AI team about.

Controlled scope. A PoC operates on a defined problem with clear boundaries. The edge cases have been identified and handled (or excluded). In production, the boundary between “in scope” and “out of scope” is fuzzy, constantly shifting, and different depending on who you ask.

Dedicated attention. During a PoC, the AI team is focused entirely on making it work. They monitor outputs, catch errors quickly, adjust parameters in real-time. In production, the system runs with whatever monitoring was built (usually minimal) and whoever is responsible for it (often unclear).

Sympathetic users. PoC users are typically volunteers, early adopters who want the technology to succeed. They provide generous feedback, tolerate imperfections, and adapt their behaviour to work with the system. Production users are everyone else.

What predicts production value

After analysing the 23 initiatives, we identified four factors that actually predicted whether an AI system would deliver sustained production value:

1. Operational ownership clarity

The single strongest predictor of production success was whether there was a clearly defined operational owner (not the AI team, but a business owner who had accountability for the system’s outcomes and authority over the process it was embedded in).

In 8 of the 9 initiatives that delivered sustained value, operational ownership was established before the PoC concluded. In only 2 of the 14 that failed to deliver, ownership was clear.

2. Feedback loop architecture

Successful initiatives had explicit, structured mechanisms for users to report issues, for errors to be triaged, and for model improvements to be prioritised and deployed. This isn’t just a “feedback button.” It’s a complete operational loop connecting the people experiencing the system’s failures to the people who can fix them.

3. Graceful degradation design

Systems that delivered sustained value were designed to fail gracefully, to recognise when they were operating outside their competence and hand off to human judgment. Systems that failed in production were typically designed to always produce an output, regardless of confidence level. The latter created a steady stream of confident-sounding errors that eroded trust faster than accurate outputs could build it.

4. Process integration depth

Superficial integration, where the AI system sits alongside existing processes as an optional tool, consistently failed to deliver value. Deep integration, where the AI system is embedded in the workflow with defined inputs, outputs, and handoff protocols, consistently succeeded.

This seems obvious in retrospect, but the pattern in practice is that organisations treat integration as a final step (“we’ll integrate it once the model is good enough”) rather than a design constraint (“we’ll design the model around the integration requirements”).

The implications for AI strategy

If PoC success doesn’t predict production value, then the standard enterprise AI playbook (build a PoC, demonstrate success, secure funding for production deployment) is structurally flawed. It selects for initiatives that demo well, not initiatives that deliver value.

A better approach inverts the sequence:

1. Start with the operational context. Before building anything, map the process the AI will be embedded in. Understand the data as it actually exists (not as it could be curated to exist). Identify the users, their behaviour, their incentives, their tolerance for imperfection.

2. Design the operational architecture first. Who owns this in production? What does the feedback loop look like? How does the system fail gracefully? How is it integrated into the workflow? Answer these questions before writing a line of model code.

3. Build the PoC within production constraints. Use real data, real users, real processes. The PoC will be harder to build, slower to show results, and less impressive in demos. It will also be a much better predictor of production value.

4. Measure what matters. Not model accuracy on test data. Not processing speed on curated inputs. But the actual business outcome the system is supposed to influence, measured in the actual operational context it’s supposed to influence it in.

This approach is slower, less glamorous, and harder to get funded. It also works.