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THE MANIFESTO

The firms that sold you "digital transformation" in 2015 are now selling you "AI transformation."

Same playbook. Same methodology decks. Same €500K engagement that ends with a roadmap and a handshake.

Different acronym.

Most AI projects fail in production. Not because the model was wrong. Not because the data was bad. Because the people who designed the system had never shipped a production AI in their careers. They'd shipped recommendations. They'd shipped decks. They'd shipped pilots.

And when the pilot didn't survive contact with real operations, they were already on their next engagement.

That's the dirty secret of AI consulting: the billable model rewards the recommendation, not the result.

Before your next AI engagement, ask three questions:

1

Do they write evals before features?

Every production AI system needs a test suite — cases that define what "correct" looks like for your specific problem. If they can't show you an eval harness before they write a single prompt, they have no way to measure whether what they built is actually working. No regression protection. No accountability.

Most don't have one. Ask to see it.
2

Do they instrument telemetry from day one?

"The model is wrong" is unfalsifiable without logs. Production AI without telemetry means you inherit a black box. When it breaks — and it will — you'll have no way to diagnose it, iterate on it, or hold anyone accountable.

Most don't instrument. Ask to see their observability setup.
3

Do you own the code, the hosting, the data, and the models?

Or do you own a SaaS subscription that disappears when they pivot? Owned infrastructure isn't just a security question — it's whether you're building a competitive asset or a dependency.

Most leave you with the dependency. Ask for the ownership clause in the contract.

If the answers are no, no, and no — you're not working with an engineering partner. You're working with a vendor who's designed the engagement so you'll always need them for the next one.

That's not AI Product Engineering. That's AI consulting.
They're not the same thing.

This is the gap I built PRIONATION to close.

We don't write roadmaps.We write eval suites.

We don't recommend infrastructure.We build it and hand you the keys.

We don't run 6-month discovery engagements.We run an 8-week Diagnostic + Build, fixed scope, fixed price, or we don't take the work.

The accountability is structural
  • Evals before features. Always.
  • Telemetry from day one. Always.
  • Owned infrastructure. Always.
  • ≥60% of Diagnostics become Builds — because we only take engagements where the math works.

There's a generation of mid-market companies that got burned by the first wave of "AI transformation."

I talk to their operators every week.

They're not skeptical of AI.
They're skeptical of the industry that sold it to them.

They're right to be.

If you've been burned before and you have a bottleneck you can name in one sentence — that's the only qualification to work with us.

Name your bottleneck.

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