Frame the strategic choice
Focus the AI agenda. Separate consequential choices from activity. Connect business ambition to the operating conditions required to deliver it.
Executive AI Strategy & Advisory
Executive AI strategy, informed by the work of building.
For CEOs, boards, and technology leaders making consequential choices about where AI can matter—and what the organization must make true to act responsibly.
Advisory
Three places to begin.
Connect governance to work
Govern the real work. Translate principles into decision rights, risk thresholds, architectural guardrails, and accountable delivery practices.
Read the practitioner view
Learn by building. Use targeted experiments to reveal what policy, architecture, workflow, data, and capability choices need to change.
Practitioner perspective
Approach
The question isn’t whether AI works.It’s what you’ll do differently because it does.
- Built
- Advice grounded in having built AI systems—delivery experience, not theory.
- Tested
- Ideas pressure-tested against your operating reality—policy, data, workflow, people.
- Learned
- Evidence comes back to your decision, not to a demo theater.
Insights
Perspectives from the practice.
The Week in AI · Issue 14 · October 12–18, 2026
Regulators in Washington, Brussels and eight US states moved against the leading AI labs on the same day, one lab drafted its own rules for halting a dangerous model, model files became export- controlled goods, and i…
Read the brief (PDF)October 4, 20269 min read
AI ROI Starts Before the Pilot: How to Track Value from Intake to the 90-Day Review
Most AI business cases convert hours saved into a dollar figure and call it return. That conversion is where the number stops being true — freed capacity is not reduced spending unless someone can point to the payroll, contractor, or hiring line that actually moved. A seven-step method for tracking value from initial intake through a 90-day review that ends in a real decision: continue, expand, modify, or retire.
August 25, 202611 min read
The AI Analytics Workspace That Remembers What It Learns
Analytics teams pay for the same lesson more than once — a column that looks right and isn't, a query that silently truncates, a metric whose name doesn't match its definition — because nothing from the last investigation survived it. The fix isn't a platform. It's a plain folder of Markdown and SQL that an AI coding agent maintains on its own, governed by read-only discipline, cost estimates before spend, and rules written as 'before X, do Y' instead of 'be careful.'
August 2, 20268 min read
AI Pilots Don't Fail Because the Model Is Bad. They Fail Because the Operating Model Is Missing
Stalled pilots rarely break on model quality — pull the outputs in isolation and they usually performed fine. What was missing was everything wrapped around the model: a named owner, an escalation path, a review checkpoint, a business success metric. Roughly 1 in 10 organizations run AI at genuinely embedded scale, and they got there by redesigning the work, not by picking a better model.
Contact
Start with the consequential decision.
Bring the strategy, governance, architecture, delivery, and adoption questions into one candid conversation.
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