Essays on AI, business, and the work it takes to bring the two together. Published as the practice generates them, usually weekly.
Three AI agents running on schedule can hand you your entire business day by 7am. Not as noise. As signal. Decisions ready to make, problems ready to solve. This is how fractional operators actually scale.
It's 2006. A first-year analyst at a top-tier London consulting firm, doing work nobody had quite named yet. Twenty years later, that work has a name and AI just changed everything about who can do it.
A CEO asked me which platform her exec team should standardize on. I told her she was asking the wrong question. The right question is which tool earns which work, and how to make that division explicit.
For years, "our data can't leave the building" ended more enterprise AI conversations than any other sentence. Three frontier labs just made that excuse much harder to defend.
Last week, UK financial regulators made something unmistakably clear: AI is no longer an innovation topic. It is a governance topic. That shift matters far beyond financial services. If you lead a business, it should get your attention.
Most executives have tried ChatGPT. Most were underwhelmed. The problem is rarely the model. It is how they use it, and what changes when they stop treating AI like a vending machine.
Most executives think they are late to AI. In reality, most are still early. The difference is that AI literacy is a compounding asset, and the executives who start building it now are gaining an advantage that becomes harder to close every quarter.
Most regulated firms are stuck between two bad options: expose sensitive information to public AI tools or avoid AI altogether and fall behind. That binary is starting to collapse. This piece explains how private AI changes the adoption equation, where firms should start, and what leadership needs to evaluate before buying anything.
Local AI agents are becoming a serious option for regulated firms, but they are not the answer to every workflow. This piece explains where local deployment makes sense, where it does not, and how leaders should evaluate the trade-offs.