Artificial Intelligence
The AI Value Gap—and How to Close It
Many teams adopt AI tools but struggle to see impact. High performers tie AI to value engines—growth, pricing, cost, and capital—and reinvest returns into skills, tooling, and reusable scaling patterns. Make adoption easy by embedding AI into core workflows, not just pilots.
"Measure outcomes where value is created—growth, pricing, cost, capital—and reinvest wins to compound impact."
Key takeaways
- Target use cases that directly map to growth, pricing, cost, or capital efficiency.
- Build scaling patterns (data pipelines, templates, change management) you can reuse.
- Reinvest early returns into talent and tooling to compound impact.
Common pitfalls
- Pilot sprawl without a path to production and measurement.
- Focusing on usage metrics instead of business outcomes.
- Underinvesting in skills and workflow integration.