Decision Guide · 1 minute read
The AI Proof of Concept That Actually Ships
An AI proof of concept that ships uses real data, targets a defined success metric, and is built with a path to production from day one—not a throwaway demo on clean data. Design the POC to answer the real risk (usually data and integration, not the model), and treat a passed POC as the first increment of the real system, not a separate artifact.
Most AI proofs of concept succeed and then die—an impressive demo with no path to production. A POC done right is the first step of the real system. Here's how to run one that ships.
Why most POCs are dead ends
A typical POC runs on clean, curated data, hits a happy path, and proves the model works. But the model was never the risk—data, integration, and governance are. So the POC answers the wrong question and can't scale. This is exactly why AI pilots fail.
Design the POC to answer the real risk
| Weak POC | POC that ships |
|---|---|
| Clean, curated data | Real (safely handled) data |
| No success metric | A defined metric |
| Proves the model | Answers data/integration risk |
| Throwaway artifact | First production increment |
The four rules
- Real data — a POC on curated data proves nothing about production.
- A success metric — define what "passed" means before you start.
- Answer the real risk — usually data readiness and integration.
- Build toward production — a passed POC is the system's first increment.
Treat it as increment one
The AI MVP mindset applies: build the smallest production-worthy version, not a disposable demo. A passed POC should already be running on real data, ready to expand—the forward deployed engineer approach.
POC vs pilot vs MVP
These terms blur. What matters is the discipline: real data, a metric, and a production path. Call it what you like—see AI POC vs MVP.
Why FISTA
FISTA Solutions runs POCs that become production—real data, a metric, and a build path—through its Applied Division, backed by 150+ projects across 12+ countries.
Want a POC that ships? Talk to FISTA.
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Clear answers
Questions raised by this field note.
Straightforward guidance for evaluating scope, fit, and the next step.
01What makes an AI proof of concept successful?
Using real data, targeting a defined success metric, answering the real risk (usually data and integration), and being built with a path to production—so a passed POC becomes the first increment of the real system rather than a throwaway demo.
02Why do AI POCs fail to become production?
Because they were built to impress on clean data, with no success metric and no production path. They prove the model works while ignoring the data, integration, and governance that actually decide production success.
03Should an AI POC use real data?
Yes. A POC on curated data proves nothing about production, where messy real data is the main risk. Using real (safely handled) data is what makes a POC predictive of production success.
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