Implementation: Start With Strengths And Micro Changes
- Tom Hansen
- Aug 31
- 3 min read
Across recent industry studies the same pattern appears. Most AI initiatives falter for reasons that live in calendars and habits rather than in models. Leaders are pressed for outcomes while their days fragment into meetings, handovers and shifting priorities. Value shows up when AI is tied to a repeatable decision, a stable input and a clear definition of better. The lesson is simple and practical. Begin with what already works in your business and convert it into small, consistent moves that compound.
A strength led entry point lowers friction. Every leader has a few anchor decisions that matter week after week. It can be the weekly pipeline review, the production planning check, the customer churn watchlist or the budget variance review. When AI is invited into one such decision and given real data, the cognitive load drops and speed rises. The goal is not a showcase. The goal is a reliable pattern that your team trusts because it helps them think, decide and move.
Micro changes make that pattern stick. A micro change is a small adjustment to an existing routine, done at a fixed moment, with a narrow scope and a visible effect. Tie it to a trigger you already have. Use the same prompt skeleton every time. Log one metric that reflects the decision quality you care about. Keep the unit of change so small that it survives a heavy week. What begins as a light touch soon becomes a rhythm, and rhythm is what culture feels like from the inside.
Consider a sales leader in a Danish industrial company. Each Monday before the leadership stand up, she pulls last week’s meeting notes and customer emails into a single draft briefing. AI produces a short summary of risks, open promises and signals of intent. She reads it once, corrects what is off, and adds two lines on next steps. Ten minutes later the team meets with a shared view and fewer surprises. After four weeks the briefing becomes the expected start of the week, and the accuracy of next step commitments improves. The tool is not the hero. The habit is.
Evidence should guide design. Choose a decision that repeats. Choose inputs that are already captured. Choose a unit of value that you can observe inside one week. Decision cycle time, first draft acceptance rate and meeting preparation time are all simple to track. If none of these moves, you have learned cheaply and can adjust the routine without fanfare. If one moves in the right direction, freeze the pattern and teach it to another manager. Spread follows proof.
Leaders sometimes ask how to keep momentum without turning AI into yet another initiative. The answer is to anchor accountability in the existing cadence. Close each cycle by noting what the AI draft got right, what it missed and what you changed. Treat this as a one minute quality check that sharpens both the prompt and the shared data. Over a quarter the draft improves, the review gets faster and the discussion focuses on exceptions where human judgment adds the most.
There are risks worth naming. If you chase novelty and shift tools every few weeks, you never build muscle memory. If you keep AI outside the system of record, your team will not trust the outputs. If you work from artificial examples, you do not change behavior in the real job. Solve each risk with a constraint. Hold one routine steady for a month. Log decisions in the place where work already lives. Use only your own data and language.
This approach scales without drama. When a micro change delivers a visible gain, standardize the prompt text and the review step, then move it to a second team with the same work shape. Support teams learn first, customer teams follow once the pattern is proven. The method respects the time of busy people and grows by copying proofs, not by selling ideas.
The leadership task is to choose the first routine and protect it from the noise. Start with a decision that matters and that repeats on a short cycle. Tie AI to that moment with a single clear question and a fixed input template. Review results in public for a month and let the evidence speak. Momentum comes from many small truths discovered in the flow of work, and from the confidence that builds when people see their own strengths extended by a steady partner.
