I’m an AI and machine-learning strategist. For nearly 20 years I have helped organizations find the decision that actually matters, learn how the people who own it make it today, and build systems those people trust enough to use. Legal Education AI Advisory brings that work to law schools.
The hard part of AI in legal education isn’t the technology. It’s deciding what a school should measure, teach and require when the tools change every few months, and getting a faculty to act on that decision. That calls for three things I do every day: knowing what current AI tools can and can’t do, measuring carefully, and designing change people will adopt.
I’m not a lawyer or a law professor. I work alongside the people who are, and bring the evidence, the analysis and the process.
The company assigned salespeople to homeowner appointments by hand. I led work that predicted which salesperson suited each appointment and turned those predictions into full-day plans, built with the people who made the assignments. After a three-month pilot and a six-month rollout across its operating companies, the company reported tens of millions of dollars of bottom-line impact.
The company wanted an AI scheduling feature but hadn’t settled what it should do. Leading a team of three for one quarter, I split the problem in two, catalogued more than 30 data issues with remedies, and delivered a working engine and a specification of more than 90 requirements that the company used for more than a year.
The platform’s ranking relied on a single measure that had stopped improving results. In one quarter, with three engineers, I replaced it with a system whose goals business users can adjust themselves; three systems went live at handoff.
Every school is starting from a different place. Tell me where yours is.
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