大多數企業的AI策略是錯誤的

Most enterprise AI strategy is backwards

That’s the claim made by LinkedIn co-founder Reid Hoffman an. It’s a bold assertion, so I set out to investigate whether the data supports it.

The result is a comprehensive report, backed by more than 30 sources, available at philippdubach.com/backwards.

Global AI spending hit $13.8 billion; a six-fold increase since late 2023. Yet 85% of AI projects never reach production. Only 26% of companies can translate pilots into outcomes. The gap between ambition and execution has become so predictable that Gartner now officially places generative AI in the "trough of disillusionment."

There's an economic concept called Jevons paradox. When efficiency improves for a resource, consumption increases, not decreases. Coal-efficient steam engines didn't reduce coal usage, they made coal so useful that demand exploded. The same logic applies to organizational communication. Email was supposed to reduce meetings. Slack was supposed to reduce email. AI was supposed to reduce everything.

Instead, the average employee now spends 57% of their workday on coordination: communicating, updating, aligning. Meetings alone cost the US economy $532 billion per year. This is the coordination layer, where organizations actually run, and where organizations quietly bleed.

Three observations:

Only 26% of companies have the maturity to translate AI pilots into outcomes. The rest are layering AI on legacy workflows instead of redesigning them.Language models bridge the gap between messy human communication and structured data. Transcripts to CRM fields. Teams using these tools report 30% higher win rates and 80% less manual work.AI gains compound when shareable. A summary helps one person. A system that captures and distributes knowledge helps everyone downstream.

The coordination layer isn't glamorous. It's transcripts, status updates, action items, CRM entries. It's the administrative exhaust of getting anything done with other people. And it's almost entirely composed of language. We have language models now. Models that extract structured data from messy transcripts, convert meeting notes into CRM fields with 99% accuracy. Sales teams using these tools report 30% higher win rates and 80% less manual work.

Yet most enterprise AI strategies ignore this entirely. They're focused on chatbots and demos for board presentations. Meanwhile, the language processing that constitutes the primary workload of any modern business remains stuck in the same recursive loops. The winners won't be companies with great AI announcements. They'll be the ones building daily habits early enough for the gains to stack.