Insights
Writing & analysis
Long-form thinking on the practice of enterprise AI and data transformation — governance, Responsible AI, modernization, and turning data into measurable value.
Financial Health Is a Data Problem
"Improving customers' financial health" has become the mission of modern consumer banking. It's a good mission — and most of the industry treats it as a marketing or product problem. It's actually a customer-data and AI problem. Here's what that really takes.
Read the essay →The Identity Resolution Problem Nobody Budgets For
Every Customer 360 program starts with a platform decision. Most of them quietly stall on a deceptively simple question the budget never accounted for: is this the same person? Here's the part of customer data that makes or breaks the whole thing.
Read the essay →Your AI Problem Is a Data Problem
The most expensive AI failures I've seen weren't model failures. They were data failures — wearing an AI costume. Here's how to tell the difference, and what 'AI-ready data' actually means.
Read the essay →Technology Is Rented. The Team Is the Asset.
I helped a bank grow from $4B to $20B in assets. The tools we started with are mostly gone now — the warehouse, the reporting platforms, the on-prem stack. What survived every change was the organization. Here's what that taught me about building a data function that outlasts the technology it runs on.
Read the essay →Putting GenAI to Work in a Regulated Enterprise — Without Losing Control of It
Every board wants an AI strategy; every risk committee wants to know what could go wrong. The teams that win stop treating that as a contradiction — and build the controls that let them deploy aggressively because they can trust the system.
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