Most AI pilots are designed to avoid the real work
Clean pilots skip permissions, exceptions and ownership, so they succeed on paper and then stall in production.
Clean pilots skip permissions, exceptions and ownership, so they succeed on paper and then stall in production.
The pilot that went perfectly
The pilot looked great.
The team picked a tidy workflow, set up the AI with a test account and admin access, fed it a clean sample of last quarter's data, and had one enthusiastic person keep an eye on it. Results were impressive. The demo to leadership went well. Everyone agreed to roll it out.
Three months later, it is still "about to go live".
Security will not grant the same access in production. The real data has duplicates, missing fields and odd cases the sample never had. Nobody knows who approves the outputs when the enthusiastic person is on leave. The team that would use it every day was never asked how it fits their work. The pilot proved the AI could do the task. It did not prove the business could run it.
Why clean pilots mislead
Pilots are usually designed to answer one question: can the AI do this? That is a reasonable first question. It is rarely the one that decides whether the project succeeds.
To get a clear answer quickly, pilots strip away everything messy:
- Permissions are broad, because setting up proper access takes time.
- Data is curated, because real data slows things down.
- Exceptions are excluded, because they complicate the results.
- Ownership sits with the project champion, not the team that will live with it.
- Integration is manual, with someone copying results into the real systems.
Each shortcut makes sense on its own. Together, they mean the pilot tests a version of the work that does not exist in your business. Success in those conditions tells you very little about success in real ones.
Pilot the messy version instead
The fix is not to avoid pilots. It is to design them to include the hard parts early, while changes are still cheap.
Use real permissions. Run the pilot with the access the AI would have in production. If that access cannot be granted, you have found the blocker in week one instead of month three.
Use real data, including the ugly bits. A sample with duplicates, gaps and edge cases tells you far more than a clean one.
Include a slice of exceptions. Not every edge case, but enough to see what the AI does when something is unusual, and who catches it.
Give it to the real owner. The person or team who will be accountable after go-live should own the pilot from day one.
Connect it to the real workflow. Even if only partly. If outputs are copied by hand, you are not testing whether the workflow works.
Agree what "ready for production" means before you start. Not "the AI is accurate", but "the team uses it without the champion, the exceptions are handled, the access is approved and someone owns it."
Signs your pilot is avoiding the real work
- It only works with one specific person involved.
- It uses an admin or personal account.
- The data was cleaned or hand-picked for the trial.
- Unusual cases were removed "to keep it simple".
- Nobody from the day-to-day team has used it yet.
- There is no clear owner after the pilot ends.
- Success is measured by a demo rather than by the work getting done.
Two or three of those are normal at the very start. If they are all still true at the end, the pilot has not tested what matters.
Questions to ask before your next AI pilot
- Will this pilot run with the same access, data and people as production?
- Which hard cases are we deliberately including?
- Who owns this after the pilot, and are they involved now?
- What would make us decide not to roll it out?
That last question matters. A pilot that cannot fail is not testing anything.
What this means at Walter
At Walter, I care less about whether an AI can do a task in ideal conditions and more about whether a business can rely on it on a messy Tuesday. That means real permissions, real exceptions, a named owner and a clear handoff into the systems people already use.
Designing pilots around the real work is slower in week one. It is much faster by month three, because you are not discovering the hard parts after everyone has already said yes.
Join the waitlist at https://hirewalter.ai/
