I've led large businesses, built and exited a company, and worked alongside teams turning advanced technology into daily operations. Now I work directly with CEOs and boards on the decisions, workflows, and accountability that make AI useful at scale.
A CEO and CIO needed a way to decide what to build, who owned it, and what happened after launch.
Anonymized case · Diagram summarizes the published engagement
A CEO and CIO at a billion-dollar company had plenty of AI ideas. What they lacked was a consistent way to choose, build, launch, and support them across the business.
Read the enterprise AI factory case →Connect product, engineering, operations, and leadership around one delivery process.
An operating model with intake criteria, decision rights, readiness gates, and post-launch ownership.
Teams had a common way to evaluate initiatives and carry them from concept into supported operation.
Tools arrive faster than the decisions around them. Who can delegate a task to an agent? What can it access? When does a person step in? Who owns the result? Those are operating questions with technical consequences.
In The AI-Native Organization, I describe AI as delegated labor. In the field, I help teams turn that idea into working decision rights, escalation paths, delivery practices, and evidence they can actually use.
These are the decisions that get missed when AI stays in a slide deck instead of entering real workflows.
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.
An AI story has to survive diligence. I help teams build operational evidence behind the narrative so a buyer can see the systems, metrics, and accountable owners behind the plan.
I work with leadership and the people who have to make the change real.
Talk with the people doing the work and follow a real workflow from request to result.
Identify where AI belongs, who has authority, and what must escalate.
Turn the choices into usable practices, gates, and measures alongside the people implementing them.
Check whether the new way of working holds beyond the first presentation or pilot.
Fixed scope, clear outcomes, and a price you can see before we talk. I take on a small number of engagements at a time, and I am adding a few more before year-end.
Where can AI materially change this business, what should we do about it, and what should we avoid? I interview your leadership, review how the business actually runs, and deliver an executive action plan.
What agentic work actually means, where digital labor fits, and what it does to your operating model, governance, and workforce plan. Teams walk out with one shared definition and a decision list that has owners.
A standing relationship for leaders navigating AI-driven transformation: one or two working sessions a month, async access between them, and a second set of eyes on the decisions that are hard to reverse.
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.
Prefer a quick self-assessment first? Get the PE AI Governance Scorecard →
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Recent writing on AI governance, PE operations, board advisory, and product leadership.
All articles →Successful tool calls, completed workflows, and clean audit trails do not prove an agent achieved the outcome. A new research paper proposes Outcome Assurance: a task-level framework for verifying that agentic work actually finished, correctly.
Read →The AI business case ends at hours saved. That is the midpoint of the decision, not the end of it. The unasked question is where the value went, who holds a claim on it, and what survives displacement.
Read →Software was always the infrastructure between the intent and the result. AI lifts the constraint that made the artifact the job — and surfaces the harder discipline underneath: owning the venue, not the event.
Read →The Executive AI Strategy Diagnostic gives leadership a focused view of where AI can perform more meaningful work, which workflows are ready for delegation, where human judgment must remain close, and what to prioritize over the next 90 days.
Engagements begin within 2 weeks of a confirmed fit · Limited availability each quarter