At a large enterprise I recently worked with, the board asked the Chief Digital Transformation Officer to develop an AI adoption strategy to drive innovation, growth, and cost efficiency. His consultant of choice conducted stakeholder interviews and proposed a three-phase program scheduled to last 3.5 years:
Phase 1: Fix digital basics and address the leftover gaps in people, processes, and technology from an incomplete digital transformation.
Phase 2: Build the AI foundation, including governance, tools, platforms, and an AI office.
Phase 3: Introduce AI and agentic business initiatives most likely to reach customers.
It was a coherent program that optimized for efficient delivery. But it’s not what the board wanted. They were looking for a strategy to become more innovative and competitive in an environment where the time required to implement IT products has shrunk significantly. What they got was a fix-the-basics project that would consume most of the budget before delivering actual business value.
From You Wanted to Become AI-Native, and All You Got Was a Lousy Foundation
That opening anecdote reminded me of most transformation initiatives I’ve either been involved in or observed: mysteriously, they seem to result in the organization doing what it’s already doing, just with new slides!
As Patzak goes on the argue, it’s better to have build up a theory of what to do, try out a little bit, see if you liked it, and adapt or keep going.
Put more plainly, you should taste food as you’re making it to ensure you get what you want at the end.
Sounds like a perfect example of Larman’s Law.