The first 90 days of AI adoption in an Australian SME
A week-by-week sequence for getting one AI use case into production safely, without a governance programme that outlasts the benefit.
Why 90 days
A quarter is long enough to reach production with one meaningful workflow and short enough that attention holds. Programmes that plan a twelve-month AI strategy before shipping anything usually run out of sponsorship first.
The objective for the quarter is not transformation. It is one measured outcome, one operating pattern the organisation can repeat, and a clear view of what to fund next.
Weeks 1 to 3: baseline and shortlist
Run short sessions with the teams doing the work and capture where time actually goes. Score candidate use cases on business value, data availability, integration effort and risk exposure.
Measure the current state of the chosen workflow — volume, cycle time, rework rate, error cost. Skipping this step is the single most common reason organisations cannot later prove whether AI helped.
Weeks 4 to 5: the risk gate
Before any build, run the use case through a short gate: what data does it touch, who can already access that data, where does the vendor process and retain it, what happens when the output is wrong, and who reviews it.
Most gate failures are fixable in days — a permissions clean-up, a sensitivity label, a contract clause. Discovering them after go-live costs far more than the gate does.
Weeks 6 to 9: build small and instrument it
Build the narrowest version that produces the outcome. Resist scope creep toward a platform. Instrument usage, quality and exception rates from day one so the pilot generates evidence rather than anecdotes.
Keep a named human in the loop for the whole pilot, and give them a fast route to flag bad output. Their feedback is the most valuable data the pilot produces.
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Weeks 10 to 12: decide, then productionise or stop
Compare against the baseline. Include the operating cost of oversight, not just licences. If net benefit is not visible, stopping is a legitimate and cheap outcome — far cheaper than a permanent half-adopted tool.
Where it works, write down the pattern: the gate, the oversight model, the measures, the support path. That pattern is what makes the second and third use case fast.
What to avoid
Avoid enterprise-wide rollouts before one workflow has proven out, avoid tools that cannot be integrated with your identity provider, and avoid governance documents written before you know what you are governing.
Capability compounds. The organisations getting value from AI are usually two or three quarters into a boring, sequenced habit rather than one quarter into an ambitious programme.
Sources and further reading
- NIST AI Risk Management Framework
- ISO/IEC 42001 AI management systems
- Australian Signals Directorate Essential Eight
This article provides general information and decision support. It is not legal advice, audit assurance, certification advice or a guarantee of outcome.
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