Quick Wins Fund Bigger Wins
Each useful experiment should make the next one easier.
The mistake is treating AI transformation as a two-year plan that starts with abstraction. The practical path is smaller: find a real workflow, build something useful, learn from use, and keep the residue.
The residue matters. A good experiment leaves behind reusable context, cleaner data, examples, prompts, tools, evaluation criteria, and confidence.
The Pattern
- Understand - Map the workflow and the friction
- Build - Create one bounded capability
- Use - Put it in front of real work
- Learn - Decide what to keep, improve, share, or stop
- Compound - Reuse what survived in the next build
Each phase generates data that informs the next. No big upfront bets.
Why This Works
- Low risk: Small experiments, not massive commitments
- Data-driven: Each phase produces evidence for the next decision
- Compounding: Context and infrastructure from early work accelerate later work
Example
Even if early experiments find nothing actionable, you now have:
- Queryable data warehouse
- Mapped entity relationships
- Better examples of what good and bad output look like
- Foundation for future tooling
The investment isn’t “solve problem X” - it’s “build the substrate that makes solving future problems cheaper.”
Implication
Do not measure early AI work only by whether the first artifact becomes permanent. Measure whether the organization learned, captured context, and made the next useful thing cheaper.
Contrarian To
“We need a comprehensive AI strategy before we can start”
No. You need enough strategy to choose a safe, useful first move. The strategy gets sharper as capability compounds.