What We Learned Building AIPath: Why Chat Failed & what everyone gets wrong about ‘THAT MIT REPORT'
- David Isaac
- Aug 28
- 2 min read
Most teams now admit it: the MIT headline was right. But deeply misunderstood. Ninety-five percent of enterprise AI pilots fail. But AI wasn’t the problem. Chat looked magical in the demo, then collapsed under multi-actor workflows, shifting priorities, missing data, and handoffs that never stayed in sync.
The hard truth is simple: chat is a great scratchpad, but businesses need a system of record: Chat is where ideas start, not where execution begins.
Here’s the deeper blind spot we discovered the hard way. Your inputs are biased toward what you already know, so the system keeps optimizing for current customers while innovation for non-customers gets ignored. Meanwhile, your teams are patching data gaps every day with judgment and back-channels, but chat quietly assumes perfect knowledge. Real work lives in ambiguity.
Great systems expose the unknowns, ask for the next fact, and support owners to close the gap.
What actually moved the needle: we stopped trying to make chat do the job of a workflow. We made decisions structured, ran simulations to turn opinions into operating plans, committed outcomes back into Productboard or Aha!, JIRA, and CRM, and published weekly diffs of what changed and why.
When trade-offs became visible to product, sales, and leadership, we stopped pretending capacity was unlimited and backed the right few with full resources.
Half-funded priorities are where strategy goes to die.
I wrote up the full founder’s account with the exact shifts, the 90-day blueprint, and how to make learning visible and auditable across Product and GTM. If you lead Product, Ops, Data, or GTM, this is for you. Read the article, then tell me what broke in your pilots and what finally worked.
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