Use the first month to pick the right workflow
A 150-person business is large enough to have repeated workflows and small enough that a bad AI rollout creates visible disruption. RSM middle-market AI survey, San Francisco Fed analysis of AI and small businesses, and the OECD report on AI adoption by small and medium-sized enterprises all support a practical conclusion: adoption should start with business workflows where the sources, owners, and outcomes are clear.
The first 30 days should inventory candidate workflows, score data readiness, identify risk, and choose one production candidate. Good first workflows often include ticket triage, quote turnaround, reporting, knowledge search, document intake, or employee helpdesk routing.
Use the AI use-case scoring model to rank candidates by value, risk, and readiness.
Build controls while the workflow is still narrow
CISA AI Data Security Best Practices and NIST AI Risk Management Framework should shape the middle 30 days. Define approved data sources, permissions, reviewer roles, output logs, exception rules, and the decision rights for each workflow.
This is where many companies move too fast. Buying licenses is easy. Building a workflow people trust requires a small operating model: who owns the source, who approves the output, what gets logged, and what stops the workflow.
Use the 90-day AI implementation plan to keep the work sequenced.
Use the final month to prove production value
Deloitte State of AI in the Enterprise 2026 reinforces that AI value depends on moving beyond scattered experiments. The final 30 days should launch the first workflow with real users, measure output quality, track exceptions, and decide whether to scale, revise, or stop.
Measure cycle time, rework, review burden, adoption, customer or employee impact, and whether the business action happens sooner. The outcome should be a repeatable pattern for the second workflow.
Use AI ROI measurement without fake savings to keep the roadmap tied to operating results.
