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AI adoption in a business
How organizations move from experiments to durable value with AI, and where it usually goes wrong.
What it is
- Adoption is the organizational side of AI: picking the right problems, building capability, and changing how work is done.
- Most failures are organizational, not technical.
How it works
- Start from real problems with measurable value, not from the technology.
- Pilot, measure with evals, then scale what works and kill what does not.
- Invest in people, governance, and integration, not just models.
Trade-offs
- Moving fast captures value early but risks unsafe or throwaway work; moving slow is safer but cedes advantage.
- Buying is faster, building is more tailored; most organizations do both.
When to use it
- Whenever AI is meant to deliver business value rather than a demo.
Common pitfalls
- Technology-first projects with no clear problem or metric.
- Pilots that never scale, and no investment in capability or governance.
Quick check
What most often decides whether AI adoption succeeds in a business?
Related concepts
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