Jun 29, 2026
Build or Rent Your AI Infrastructure? Run the Reversibility Test First
Feature comparisons will not settle build-vs-rent. Estimate the 12-month switching cost for each stack layer: that number makes the call.
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Leadership experience at Gartner and AWS: startups, frontier AI, data & analytics
I help leaders turn AI investment into operating results. And I’m building again.
I work with founders, executives, boards, and investors at the point where AI ambition meets the operating model. In plain language: which bets to fund, what is really blocking deployment, who owns the outcome, and how to prove the value is real. I stay close to the work: embedded, not advising from a distance. Select engagements include fractional CAIO/CDO leadership.
Judgment earned inside the work.
Anthropic 
OpenAI 
Cursor 
Perplexity 
Salesforce 
Adobe Future driven architecture
What’s underneath your AI strategy?
What sinks an AI initiative is rarely visible on launch day. The cracks are set earlier. Common pitfalls:
- Pilot theater: the demo lands well in the room and never reaches a production workflow.
- Change resistance and reversion: people go back to the old way once the launch attention fades.
- No workflow redesign: the model is bolted onto a process nobody rebuilt around it.
- Vague value case: no one agreed which number the programme moves, so no one can show it moved.
- Misaligned ownership: the work sits between teams and no single team owns the outcome.
- Late-stage governance: risk, legal, and compliance arrive after the build instead of before it.
- Fragile technical deployment: the data, the evaluation, and the monitoring are held together by hand.
Future driven architecture
What’s underneath your AI strategy?
What sinks an AI initiative is rarely visible on launch day. The cracks are set earlier: in the scoping, the ownership, and everything left to settle later: and they only surface once the build is standing on them.
Let’s discuss your AI strategy 
Elevating the art of building with AI
How I work
Two altitudes. One operating thesis.
Advice only holds when it stays close to what is actually being built.
01
Forward deployed
Embedded executive leadership.
I work inside the operating cadence: decision forums, tradeoffs, adoption, and value measurement: until an AI bet becomes a capability the organization owns. Including selective fractional CAIO/CDO work.
02
Hands on
Experienced operator and builder.
Founder, operator, engineer: I have carried the P&L and written the code. The advice holds because I am still shipping products and living with the decisions.
Elevating the art of building with AI
How I work
Two altitudes. One operating thesis.
Advice only holds when it stays close to what is actually being built.
01
Forward deployed
Embedded executive leadership.
I work inside the operating cadence: decision forums, tradeoffs, adoption, and value measurement: until an AI bet becomes a capability the organization owns. Including selective fractional CAIO/CDO work.
02
Hands on
Experienced operator and builder.
Founder, operator, engineer: I have carried the P&L and written the code. The advice holds because I am still shipping products and living with the decisions.
How I approach the work.
AI rarely stalls on the model. It stalls on decision rights, workflow design, risk appetite, and who is accountable for the result.
[ 01 / 10 ]
Jun 29, 2026
The Hidden Cost of Premature Infrastructure Optimization at Series A
Building the right infrastructure too early costs more than building it wrong. Here is how to tell whether you are solving a real constraint or your board’s anxiety.
Read
Jun 27, 2026
Signal, Noise, and the Things Being Mistaken for Both in AI Right Now
Most leaders lose the AI cycle by treating every release as signal. A three-question filter: cost curve, work structure, compounding: fixes that.
Read
Jun 17, 2026
Where AI Strategy Goes to Die: The Execution Gap and How Leaders Read It
AI initiatives do not fail in the boardroom: they die in translation. Five structural failure points, and how to read the gap from your current seat.
Read
Jun 17, 2026
How Experienced Leaders Decide Where to Bet on AI, and Where to Wait
A three-axis framework: reversibility, time-to-signal, and strategic fit: for making disciplined AI investment calls at any level.
Read
Jun 16, 2026
Frontier Model Risk: How AI-Native Companies Plan for Continuity
Five failure modes, a blast-radius scoring worksheet, and stage-matched continuity tiers for companies that depend on models they do not control.
Read
Jun 8, 2026
Does Your Career Path Actually Lead to the Chair You Want?
Six signals, four untested assumptions, and a one-sitting diagnostic to tell whether your path is climbing: or running sideways at altitude.
Read
Jun 7, 2026
The Four Scaling Decisions That Compound
Between Series A and Series C, four decisions compound faster than the rest. Here is how to spot which one is quietly drifting before it rewrites your quarter.
Read
Jun 7, 2026
Why the Team That Got You to Series A Will Break Between B and C
The slowdown between Series A and Series C is structural, not motivational. Five failure modes, and the interventions that actually fix them.
Read
May 26, 2026
What AI Is Quietly Doing to Career Compounding
AI is not replacing your job. It is repricing your experience. Here is how to audit which parts of your career are still earning interest.
ReadWhere the work shows up: Founders Executives Boards Investors PE/VC teams
The questions that decide whether AI pays.
Where to bet, where to wait.
How to translate ambition into an operating decision: reversibility, time-to-signal, and strategic fit, before the market forces the issue.
Browse this topicRecent in this topic
Jun 29, 2026
Build or Rent Your AI Infrastructure? Run the Reversibility Test First
Jun 27, 2026
Signal, Noise, and the Things Being Mistaken for Both in AI Right Now
Jun 17, 2026
How Experienced Leaders Decide Where to Bet on AI, and Where to Wait
Jun 7, 2026
How to Tell Which AI Capabilities Will Be Commodities in 18 Months
7 published
I’ve helped enough organizations with operating reviews to see where AI programs stall.
14000+
organizations served, leading Gartner’s Data & Analytics practice
400+
accounts supported, as head of AWS startups and frontier AI labs, North America
27
executive teams advised from AI mandate to owned capability
63
operating reviews connecting AI investments to accountable owners and measurable outcomes
The warning signs surface early, while there’s still time to correct course. Addressing them before they become embedded in budgets, workflows, and ownership structures protects momentum and keeps the investment focused on measurable value.
The engineer deploys the system. Who deploys the decisions?
A forward deployed engineer embeds with a customer until the technology works in the real environment. The executive half of that job is now the harder one: which bets get funded, who owns the workflow, what risk is acceptable, and how value gets measured. This is the crucial work that leads to success or failure.
| This work | Consulting firm | Agency / SI | Full-time hire | |
|---|---|---|---|---|
| Starts from the decision, not a package | ||||
| Embedded in the decision forums, not the readout | ||||
| Run by someone who has operated it, not only advised it | ||||
| Close enough to delivery to judge what is real | ||||
| Accountable for adoption and measured value | ||||
| Sets the baseline before the value is claimed | ||||
| Available as fractional CAIO/CDO leadership | ||||
| Leaves capability behind, not dependence | ||||
| Ends when the decision holds |
A weekly field report from the operator’s seat.
One executive decision each week that turns AI investment into operating results: what it is, why it matters now, and what to do next.
It is for
People with something at stake.
AI startup founders, corporate AI leaders, rising technology leaders, and AI-aware professionals making decisions with budget, headcount, company value, or career capital on the line.
It covers
Bets, blockers, ownership, value.
Which bets are worth funding, what prevents deployment in a real organization, who must decide and own the change, how the workflow is redesigned, and how value gets measured and scaled.
It avoids
The firehose.
No prompt tutorials, productivity hacks, breathless launch recaps, or predictions dressed up as operating truth. One decision you can act on beats fifty updates.
Get the next Operator’s Log.
Free, weekly, unsubscribe whenever.
Questions, answered.
I help leaders turn AI investment into operating results. That means the executive decisions around a deployment: which bets to fund, what is actually blocking them, who decides and owns the change, how the workflow has to be redesigned, and how value is measured before it is claimed. The work is plain-language decision support first, and selective fractional CAIO/CDO leadership second.
It is the executive counterpart to the forward deployed engineer. The engineer embeds with a customer until the system works in the real environment. The forward deployed executive embeds with leadership until the operating model works around it: decision rights, funding, workflow redesign, risk boundaries, adoption, and measured value. The title is new and may not last. The capability is the point: a senior operator who stays close enough to the work to be accountable for the result.
Founders, executives, boards, investors, and PE/VC teams: at moments when technology strategy, operating design, and capital allocation collide. The work suits organizations that are large, regulated, data-rich, or operationally complex, where the barrier is execution rather than access to models.
It may look like fractional AI/data leadership, a strategy reset, diligence on a company or platform, operating-model design, or helping an executive team turn pressure into a decision it can execute. It begins with a written charter: the target workflow, the baseline, the success measure, the decision rights, the risk boundaries, and the handoff plan. The aim is a capability you own, not a dependence on me.
No. I departed AWS in July 2026, where I was Head of Premium Support, Startups and Frontier AI, North America. That is a former role, not a current affiliation.
I held leadership within Gartner’s global Data & Analytics practice, which serves more than 14,000 organizations, helping technology leaders reason through data and AI strategy.
In July 2026 I co-founded a new technology company with an established industry leader. The company’s name, industry, product, and my co-founder’s identity remain undisclosed. I’ll share more when the company is ready to be understood on the strength of what it has built.
A free weekly field report from the operator’s seat. Each issue takes one executive decision required to turn AI investment into operating results. No firehose, no tutorials, no borrowed certainty.
Use the contact form to tell me briefly what you’re working on, the decision in front of you, why it matters now, and what success would make possible. Your message goes directly to my inbox and won’t be added to a newsletter or contact database.
From the log.
Short field reports on the executive decisions behind AI deployment.
Deployment Reality Jun 29, 2026
The Hidden Cost of Premature Infrastructure Optimization at Series A
AI Bets Jun 27, 2026
Signal, Noise, and the Things Being Mistaken for Both in AI Right Now
Deployment Reality Jun 17, 2026
Where AI Strategy Goes to Die: The Execution Gap and How Leaders Read It
AI Bets Jun 17, 2026
How Experienced Leaders Decide Where to Bet on AI, and Where to Wait
Selective opportunities
Start with the opportunity.
Tell me about your project: the decision in front of you, why it matters now, and what success would make possible.
Advisory ·Fractional CAIO/CDO ·Board & investor work ·Speaking & media ·Founder conversations