Start with the work, not the tool

The best AI opportunities usually begin with a repeated customer or team problem: slow research, inconsistent handoffs, difficult analysis, or work that absorbs attention without creating much value.

Frame each bet around the outcome you want to improve, the information it needs, and the person who will own its quality. This keeps experimentation grounded in the work that already matters.

Test for value before scale

Run a small, observable test before investing in a broader rollout. Look for meaningful improvements in speed, quality, confidence, or customer experience—not novelty alone.

When the evidence is strong, scale the practice with clear guardrails, training, and a feedback loop. Good technology adoption is a change in how people work, not a feature announcement.