Enterprise AI advice assumes a data team, a change management function, and a budget for consultants. Here is what the same journey looks like without any of those.
- ›Month one: foundations and one win
- ›Month two: expand where it worked
- ›Month three: governance and pruning
- ›What to skip entirely
Month one: foundations and one win
Weeks 1-2: get your data somewhere AI can reach it.
This is the unglamorous prerequisite and the reason most SMB AI projects underdeliver. AI over fragmented data produces generic answers. Consolidating your core records, or at least connecting them, is the actual first step.
Weeks 3-4: one use case, end to end.
Choose the recurring task people most dislike, with cheap failure. Set it up, run it in suggest mode, read every output, adjust. By the end of the month you want one thing working and one team that has seen it work.
Do not start five things. The single most reliable predictor of SMB AI failure is starting broad.
Month two: expand where it worked
Add two or three more use cases, chosen by the same criteria, ideally in the same team that succeeded first. Momentum is local; a second win in the same team beats a first attempt in three others.
Turn on detection broadly. Signals for stalled work, overdue items, and anomalies across modules. This costs nothing to review and catches things immediately.
Start delegating narrowly. Where a proposal has been approved unchanged for weeks, grant that specific verb. See when to give AI write access.
Month three: governance and pruning
Write down what the AI may do. Autonomy level per area, who approves what, what is out of scope permanently. This takes an afternoon and prevents the drift that causes incidents. See AI autonomy levels explained.
Review the audit trail. Not for problems specifically; to understand what has actually been happening.
Prune. Turn off every automation and agent whose output nobody reads. Expect to remove a third of what you built, and treat that as the process working rather than as failure.
Measure. Against whatever you said you would measure in month one. See calculating AI ROI honestly.
What to skip entirely
An AI strategy document. For a 30-person company, this substitutes for doing something.
A center of excellence. You have twelve people.
Custom model training. Almost never justified at this scale.
Vendor evaluations spanning months. Two weeks of real use beats a scorecard.
Change management programs. Show one team something that saves them time and let it spread.
Tip: The best month-one use case is one where the person who benefits is the person who set it up. Enthusiasm from a direct beneficiary carries a rollout further than any plan.
The realistic outcome at 90 days
Three to five working use cases, one team fluent, written autonomy boundaries, and an honest measurement of what it saved. That is a good outcome, and it is far short of what vendors imply is achievable in the same period.
FAQ
What if month one fails?
Usually the use case was too ambitious or the data was not there. Pick something smaller and check the data first.
Who should own this?
One person who uses the systems daily, with a few hours a week. Not a committee, and not necessarily someone technical.
When do we need outside help?
If integration between systems is the blocker and nobody internal can address it. Otherwise, at this scale, external help usually costs more than it accelerates.
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