Practical writing on AI program leadership, governance, and execution inside regulated financial services institutions. Less theory, more patterns I'm actually seeing.
The model isn't the problem. It almost never is. After watching dozens of AI initiatives stall inside large financial institutions, the patterns are remarkably consistent — and so are the things that actually get them moving again.
Read articleMost AI governance frameworks are designed by people who have never had to defend a model to a regulator. The ones that work treat governance as an enabler, not a gate — and start with the audit trail in mind.
Read articleThe technical handoff from data science to operations is where most AI programs die. Here's what the gap actually looks like — and how to close it before the program runs out of executive patience.
Read articleThe word "agentic" is doing a lot of work in vendor pitches right now. Here's what it actually changes for risk, audit, and operational control inside a bank — and what it doesn't.
Read articleMost AI steering committees become status theater within three meetings. The structure that keeps them useful is simpler than people think — and it starts with what's not on the agenda.
Read articleThe real question isn't whether to buy or build. It's which capabilities you can afford to be dependent on a vendor for, and which need to be controlled internally to defend in front of a regulator.
Read articleIf any of this resonates with what's happening at your institution, I'd be glad to compare notes — even if it doesn't lead to an engagement.
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