Strategic Experimentation is Decision Quality - Not Failed AI PoCs

Analysts frequently report that 65–95% of AI proof-of-concept (PoC) initiatives never reach production. On the surface, that sounds alarming. In a regulated environment, it may be the opposite.

Click the image to download the PDF.

In a regulated environment, a "failed" PoC can be successful audit of risk.

A £50k controlled experiment that prevents a £2m scaled implementation failure is not waste. It is capital discipline.

The question boards should be asking is not: “Why are so many failing?”.

But rather:

• Were hypotheses explicitly defined?

• Were model, operational and conduct risks bounded?

• Were exit criteria agreed in advance?

• Did governance participate from day one?

• Did the exercise generate decision-grade evidence?

If the answer to those questions is yes, then termination is not failure.

It is evidence of:

🔹Risk containment

🔹Learning velocity

🔹Capital protection

🔹Institutional maturity

The real risk is not a high PoC attrition rate.

The real risk is scaling systems that were never rigorously challenged.

Failure rate is an easy headline metric.

Decision quality is the one that matters.