AI Readiness & Governance
AI adoption is outpacing most organizations' ability to manage it. Tools arrive through every team, vendors embed AI into products you already licensed, and the rules keep moving.
The common response — write a policy — creates false confidence and real liability. A policy without posture means your name is on a document that doesn't describe what your organization actually does.
The methodology
Every engagement works the same three fronts. Here's what each covers in practice:
Policies no one understands don't change behavior. Readiness starts with people — training boards, executives, and staff to understand what AI actually does, where it fails, and what responsible use looks like in their daily work.
You can't govern tools you haven't assessed. Readiness means knowing your technical posture — which AI systems are in use, what data they touch, how vendors handle it, and whether your controls match reality.
Governance makes readiness durable — frameworks, policies, and compliance mapping that fit how your organization actually works. On its own, it's paperwork. On top of education and technology, it's posture.
Who this is for
You’re asked to bless AI use you can’t see, under rules that are still moving.
You’re accountable for AI risk you haven’t been given the tools to understand.
AI tools are entering through every door — sanctioned or not — and each one changes your surface.
A policy exists. Whether anyone follows it — or whether it matches your actual AI use — is another question.
How engagements work
Three paths, in the order most clients take them.
A structured review of where your organization actually stands: what AI is in use (approved or not), what data it touches, what your people know, and where your exposure is. You get a plain-English readiness report and a prioritized roadmap.
Request the AssessmentTurn the roadmap into working infrastructure: policies that fit how you operate, vendor and tool review processes, role-based training, and compliance mapping to the frameworks that apply to you.
AI capability shifts monthly; your posture has to keep up. Ongoing advisory keeps your training, tooling decisions, and governance current as the technology and the law move.
This practice sits on an unusual foundation: an electrical engineer and U.S. Air Force cybersecurity veteran who became a patent lawyer, wrote the book on AI in patent practice, chairs the State Bar of Texas Emerging Technology Committee, and builds AI ventures across education, technology, and governance. Juan has lived all three pillars — that's why the methodology works.
Outlines a three-pillar framework for responsible AI adoption in legal organizations — the rules (governance), the tools (evaluation and selection), and the people (training) — arguing that weakness in any one pillar undermines the others.
How AI hallucinations — plausible but fabricated output, including fake case citations — expose lawyers to sanctions and malpractice risk, with lessons from Mata v. Avianca, error-rate research, and practical verification strategies.
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