From “we should use AI” to a working pilot in five steps.

Why most AI pilots stall

Most small businesses don't fail at AI because the technology doesn't work. They fail because the first project is too big: a company-wide rollout, a platform migration, a “transform how we work” initiative that needs buy-in from six people before anyone ships anything.

The businesses that get real value start narrower. One workflow. One owner. A deadline measured in weeks, not quarters.

The five-step framework

This is the sequence we walk clients through before any custom build gets discussed:

1. Audit the busywork. List every recurring task that's repetitive, rule-based, and currently done by a human. Rank by hours per week.
2. Pick one. The top of the list, not the most exciting one. Resist bundling two workflows into the pilot.
3. Wire the off-the-shelf tool first. Most workflows can run on an existing assistant plus a written prompt before any custom software gets involved.
4. Run it in parallel for two weeks. Human still does the task; AI output gets checked against it daily.
5. Decide, then repeat. Cut over, adjust, or kill it — then move to the next workflow on the audit list.

Common traps

The most expensive mistake is skipping the parallel-run week to “save time” — teams that do this catch bad outputs three weeks later instead of three days later. The second is picking a workflow with unclear success criteria, so nobody can agree the pilot worked.

The third: jumping to a custom build before exhausting off-the-shelf options. Custom systems start around $15,000 — the right answer only when volume or complexity justifies it.

What good looks like

A good pilot answers one question cleanly: does this save more time than it costs to run and check? Clients who follow this framework typically know the answer inside thirty days, for under $100 in tool spend.

From there, the audit list already has the next candidate queued up.