What Enterprise AI Gets Wrong
The technology is sound, the assumptions aren't.
Most enterprise AI projects don’t fail because of bad technology. They fail because of bad assumptions.
Mistake 1: AI as a project instead of a capability
Organizations treat AI as a project with a beginning and an end. But AI isn’t a project. It’s a capability that has to be woven through the entire company.
Mistake 2: Starting with the technology
“We need to do something with AI” leads to technology in search of a problem. The right question is: “where are we losing time, money or quality — and can AI change that?”
Mistake 3: Proof of concept without a path to production
Ninety percent of AI PoCs never reach production. Not because they don’t work, but because nobody thought about integration, governance, maintenance and scalability.
Mistake 4: AI without a data strategy
AI is only as good as the data it gets. But most organizations don’t have a data strategy — they have data islands, inconsistent definitions and outdated systems.
Mistake 5: Vendor lock-in by default
The fastest route to AI is often a platform from a major vendor. But the fastest route is rarely the smartest. Vendor dependency in AI is more expensive than in any other technology.
The way out
The organizations that get it right start small, think in workflows instead of tools, and build knowledge instead of just software.