When the operating model is right, a buyer can't poke holes in the AI story — because it's not a story anymore. It's a system: metrics, accountable owners, board oversight that's already working. That's what I build. Not handed over at the end of an engagement and wished luck. I stay until the model holds.
The conventional playbook for AI in enterprise is backwards. Companies are buying tools before they've defined decision rights. Boards are receiving AI updates without governance frameworks. PE firms are underwriting AI theses they can't operationalize.
My book, The AI-Native Organization, reframes AI as delegated labor — not software. That shift changes everything about how you govern it, how you measure it, and how you create value from it. The structural implication: an AI-native organization needs a new operating system — one that governs AI labor and human labor together, with explicit decision rights, accountability architecture, and trust frameworks at every layer.
I don't follow the standard consulting playbook because the standard playbook adds AI to existing org structures and calls it transformation. My differentiator is operating model design — rebuilding how the organization makes decisions, assigns accountability, and integrates AI as a managed business asset. That's been my operating thesis for 25 years — from the Air Force to Microsoft to founding and exiting a company — and it's what I bring to every PE engagement and board conversation.
Most PE and board advisors know governance or technology. Very few have built both at scale — and fewer still have written the frameworks that boards are now adopting.
There's a difference between an AI briefing and AI governance. Most boards have the former. I help install the latter: decision rights, trust architecture, risk tiers, and accountability that holds across the investment lifecycle. The result: your board stops deferring AI decisions to management and starts owning them.
AI adoption without operating model redesign creates technical debt, compliance risk, and talent confusion. I design AI-native operating models — not AI bolt-ons to legacy org charts. The result: AI actually makes it into core workflows — quarter after quarter — instead of stalling in pilot purgatory.
In 2025, every investment deck has an AI story. Very few can survive diligence. I help portfolio companies build the operational evidence behind the narrative — and the governance infrastructure to back it up. The result: when a buyer asks 'show me where AI actually hits the P&L,' your team can walk them through systems, metrics, and accountable owners — not a roadmap.
Each practice area addresses a specific inflection point. I take on a small number of engagements at a time — because this work requires genuine presence, not a slide deck from the outside.
I design the operating architecture for AI — decision rights, accountability frameworks, trust tiers, and board-level governance models. This is the work described in The AI-Native Organization, applied to your specific portfolio or enterprise context.
I serve as operating partner or independent board director for PE-backed companies at critical inflection points — post-acquisition integration, AI-driven transformation, leadership transition, and pre-exit optimization.
For PE portfolios where the product IS the asset — I lead product strategy, platform investment decisions, AI-native product design, and product-led growth architecture. Built on 25 years of operating at scale from startup to $1.2B enterprise.
For boards that need direct operating judgment on AI investment, leadership risk, and value creation decisions. I help translate between governance responsibility, operating reality, and what the business can actually sustain.
These aren't books written from the sidelines. They're operational blueprints developed from 25 years of running, advising, and transforming organizations.
AI Governance · Operating Model
Reframes AI as delegated labor rather than tooling and introduces board-level frameworks for decision rights, accountability, and trust architecture in the agentic enterprise.
Read on Amazon →Product Leadership · Complexity Navigation
A practical framework for technical program managers and product leaders on how to recognize which leadership mode a situation demands — and make the switch before the work suffers.
Read on Amazon →Market Strategy · Founder Toolkit
A rigorous framework for founders and product teams to evaluate whether an idea is worth pursuing — before committing resources.
Read on Amazon →
"The organizations that are winning with AI aren't the ones who hired the most consultants. They're the ones who redesigned how they think."
I started my career in the Air Force — Aerospace Technology, not business school. That non-linear path — from military precision to startup chaos to Microsoft scale to PE advisory — is the reason I see around corners most advisors can't.
I ran a $1.2B business at CDW, led Defense and Intelligence AI programs at Microsoft, built and exited a company, and launched an advisory practice that helped nearly 50 organizations from founder-led companies to enterprise scale. Now I help PE firms and boards apply that operating experience to the hardest problem in enterprise right now: making AI work as a governed business asset, not a technology experiment.
Joe gave our board a usable way to talk about AI risk, investment, and accountability. The conversation moved from vague enthusiasm to actual decisions.
He does not disappear after the strategy work. He stays in the problem until the operating model is clearer and the team can actually use it.
Joe earned trust with both executives and engineers because he could connect the product decision, the technical reality, and the business case without losing the room.
The practical companion to The AI-Native Organization. Distilled for PE operating partners and board directors who need to move from AI awareness to AI governance — fast.
No newsletter, no drip sequence, no pitch. Delivered immediately and used in live PE engagements.
No newsletter. Just the download.
Recent writing on AI governance, PE operations, board advisory, and product leadership.
All articles →Traditional software is shaped the way it is because it cannot understand intent. AI removes that constraint — and with it, most of the scaffolding we have been calling product.
Read →Agentic systems are the engine. Discovery is the output. Once you separate the two, the strategic picture for operators, boards, and capital allocators looks very different.
Read →AI is already making decisions at scale in most PE-backed and regulated businesses. The question is not whether to allow it — it is whether you designed it.
Read →Discovery calls are 30 minutes. No pitch. I'll tell you directly whether I can help — and if I can't, I'll tell you who can.
Engagements begin within 2–4 weeks of a confirmed fit · Limited availability each quarter