Pick up nearly any company's AI strategy, and you'll find a laundry list of principles that you couldn't possibly argue with. Keep a human in the loop. Steer clear of dark code no one can really comprehend. That is why you stay tool-agnostic and not vendor locked. Measure the impact. Label what was AI-assisted.
These are all correct. And none of them are happening on the floor in most organizations.
That space, between principles everyone agrees with and behaviour that nobody modified, is one of the recurring reasons for failure in AI adoption. It is useful to know why this happens.
Principle alone does nothing
Principles are statements of intent It encapsulates a Wanted Future condition. It does not prevent anything, however.
"No single bad merge is prevented by keeping a human in the loop. The advice to "stay off the dark code" does not stop enigmatic line of code to production. Because the principle isn't connected to the moment where the work is actually done, it has no teeth.
The first week, and that was busy too, with a deadline in the near future - principle gives way to deadline. Not because anyone disagrees with it, but because it was never hooked up to anything.
What makes a principle existential; three things
Only when you can find three specific things that it generates behind it, does a principle become a working control.
An artifact. Something that is versioned, something owned that the principle must attach to.
"Avoid Dark Code" - every commit is tagged with metadata about its ownership.
Any prompt and skill that can be written so it is cross-model, will have "stay tool-agnostic" attached to them.
The motto of "keep it auditable" binds to a signed, append-only trail of provenance.
The principle is a wish, if you cannot name the artifact.
A gate. A point in the workflow where, if that particular check fails, work stops.
"Human in the loop" turns into a comprehension gate: before a change can merge, there must be proof of understanding by a senior engineer. If they are not able, the merge is prevented.
A security review that an agents change must pass before being deployed: "Secure by default"
Having no gate with a principle is called an inclination. And preferences lose to deadlines every time.
A metric. A number that changes, so you can see if the knob is working.
- Comprehension-gate pass rate.
- Eval coverage.
- Dark-code ratio.
- Time that is taken to reinstall the system after performing a rollback
You cannot manage a principle that has no metric. It can only be believed.
A simple test
Read your AI policy line by line. For each line, ask the following three questions:
What does this attach to (what artifact)?
Which means, where's the gate to enforce that?
What number indicates to me that this is working?
Even if you have two of the three, you still do not have a control. You have a good intent, that first time the team is pressed it skips. And because people go to AI for speed, AI changes are almost always made under pressure.
Why is this more important with AI than ever before
With this manual nature of the work, in a human authored world, you could rely on catching such problems. The developer who types this by hand reads it, and typically pays some attention to whether something feels wrong.
AI removes that natural friction. The more widespread - and less visible - failure modes now happen faster.
They accept insecure code because it seemed sensible.
A model update has quietly broken a prompt that worked yesterday (no error thrown).
Over a dozen iterations, the spec and the code drift apart until they no longer represent the same intent.
None of these will trip a compiler. None of them announce themselves. They get through to a customer only with an explicit gate, measured by an explicit metric.
Where the leadership work really resides
This transition is also not largely about choosing tools and writing vision statements; that is the work of frontline leaders. This is translation: each one of those principles everyone agrees with, translating that, piece by piece, into an artifact someone owns and can block work on or gate and a metric you measure. Slow, mundane work with no flashy demo. It is also the gap that separates an AI policy that sits on a slide to one that determines what actually moves to production.
Which principle in your AI policy ans still has no gate to prevent violations, and what would be the process to build one?
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