AI transformation
Operations first. AI second.
Most companies are spending millions on AI with nothing to show for it. The problem is not the technology. It is the operating model underneath it. AI amplifies whatever it lands on, so when decision rights are unclear, the cadence is weak, and the data is a mess, AI amplifies the mess. That is why so many pilots impress in a demo and then quietly die.
AI transformation is changing how a company operates with AI, not just adding tools. Done right, it starts with the operating model: where decisions are made, how work flows, and where the measurable ROI actually is. Fix that, then apply AI where the return is clearest. Sequence it the other way and you get expensive experiments instead of compounding gains.
Why most AI initiatives fail
- No clear owner, so the initiative escalates to the CEO and stalls.
- No defined outcome, so "success" means "we'll discuss next steps."
- AI bolted onto broken operations, amplifying the dysfunction.
- Pilots with no pre-commitment to scale, so they never leave the lab.
How to do it right
Install the operating system first: clear decision rights, measurable outcomes, a cadence that forces closure, and the data discipline to trust what you are feeding the models. That is the VOOCS framework. Then pick the two or three places where AI has obvious ROI, pre-commit to scaling what works, and measure it like any other initiative. The full execution framework is here: VOOCS.
The keynote
I speak on this for CEO audiences, PE operating partners, and leadership offsites. The talk is Why Your AI Strategy Is Failing, and it is about getting ROI from enterprise AI by fixing operations first. See speaking.