Enterprise AI Strategy is Backwards
Updated 4 October 2026
Key takeaways
- We would prioritize enterprise AI that redesigns daily coordination work, helping information move between people and systems with less administrative effort.
- Language models can turn meeting transcripts and notes into structured records, reducing the administrative work that keeps teams coordinated.
- Shared knowledge systems can spread AI’s benefits beyond one person’s summary, but those gains require daily habits around the tools.
That’s the claim made by LinkedIn co-founder Reid Hoffman. I investigated whether the data supports it.
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 (yes, I referenced this before). 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 coordination work consumes time that could go to other tasks.
Three observations:
(1) 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.
(2) 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.
(3) AI gains compound when shareable. A summary helps one person. A system that captures and distributes knowledge helps everyone downstream.
The coordination layer consists of transcripts, status updates, action items and CRM entries: the administrative work required to get things done with other people. It’s almost entirely composed of language. Language models can extract structured data from messy transcripts and 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 gains depend on building daily habits around these tools, not on announcing them.