Marketing organizations have bought the platforms and accumulated the data. The returns have not caught up.
Twenty-five years across agency consulting, publishing, and financial services, leading the work in-house and advising it from outside. Today, applying that operating discipline to agentic AI.
The gap between what companies spend on marketing technology and what they get back is old. It can be traced back to the first martech buildouts, through data platforms and CDPs, and AI is widening it again. The industry keeps attacking it with technical talent, engineers embedded with customers and integration teams brought in after the sale. Useful, and incomplete. Getting a system into production is one problem. Getting an organization to run on it is a different one, and it belongs to whoever owns decisions, budgets, and the way work actually gets done.
That second problem has been my job the whole way. I led digital product at American Express, digital marketing and analytics at Time Inc., and data inside WPP, where I ran a hundred-person data practice as a chief data officer and a $15M consulting P&L as an EVP. And I stay hands-on. Right now that means building and governing agentic AI systems myself, human gates and audit trails included, so I know where they break in production rather than in a demo.
I'm looking for my next full-time operating seat: SVP, EVP, or C-suite in marketing, data, AI, or all three. If that's the conversation you want to have, email me or reach me on LinkedIn. For advisory or consulting conversations, the same address works. And if you're building in this space and just want to compare notes, I answer those too.