GoKawiilMy thoughts on why enterprise knowledge systems have failed for sixty years, and what might finally replace them.
A couple of weeks ago I demoed one part of what I have been building to a senior exec at a global enterprise - someone who had been asked to lead and guide AI adoption in their part of this billion dollar company - our conversation was off the record, but what they told me - and why they couldn't buy my product - that is the basis of my essay.
First, they told me that what I had shown them was the first time they had seen an AI system for complex enterprise work that looked ready to deploy. Yes, they had familiar reservations: their data had to stay under their control (no problem, my architecture is designed around exactly that).
Next, they told me what the large consulting firms had been pitching them: the quotes sat in the hundreds of thousands, spread across a roughly threefold range. The high end was a gold-standard 99.5% accuracy promise while the low end was priced to be a deliberate foot in the door and the common thread was these firms were selling their own learning curve.
I had demoed a product that worked, while these behemoths - my competitors - were asking to be paid to build a product that worked.
Next I was told that they could not buy from me. Why?
Risk
And they put it succinctly: buying from a small innovative company is brave while buying from a big, well recognised name is an insurance policy and the risk-averse buyer must have the insurance.
That insurance - more than price and more than product - is what enterprise software has always traded on.
My conversation was not a one-off - of course - it is the shape of a sixty-year failure the industry has learned to call "prudent".
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