AI Strategy
Where AI actually pays: adoption phases, unit economics, and the operating-model change that separates leverage from theater. 17 essays, from 25 years at the seam between the boardroom and the codebase. This is the thinking behind AI strategy consulting.
AI Pilot Purgatory Is an Orchestration Problem
AI got funded, piloted, and then stalled. Escaping pilot purgatory has little to do with the model or the tooling. It comes down to orchestration, and that architecture decision belongs in the boardroom.
Read →Transform: The 6% Who Redesigned the Organization
Only 6% of companies capture real EBIT impact from AI, and they're nearly three times more likely to have redesigned how they work. The last phase isn't technical. It's the operating model, and it's the one only leadership can change.
Read →On-Device AI Is the Future. The Unit Economics Just Aren't There Yet.
If frontier AI lands at $1K to $5K per employee per month, a $20K private AI server per employee stops sounding crazy and starts sounding like procurement. The hardware has mostly caught up; the math hasn't, not quite.
Read →Industrialize: Scaling Agents Without Scaling the Chaos
Fewer than a quarter of companies have scaled AI agents beyond the first win. Scale is where AI stops being a project and becomes infrastructure, and infrastructure has rules most AI teams haven't learned yet.
Read →Fable 5: The Price Went Up and the Knobs Came Off
Anthropic just shipped a model tier above Opus. The price doubled and the dials disappeared, and both of those facts tell you how to run an AI-native organization.
Read →Operationalize: You Built an Agent. Now Make It an Employee.
A third of companies have an agent doing real work in production. Most of them built a heroic one-off: brilliant, fragile, and understood by exactly one engineer. That's not a capability. It's a liability with good PR.
Read →Experiment: Pilot Purgatory
Two-thirds of companies are running AI pilots. Most pilots are built to demo, not to ship, and a pilot without a production path is just an expensive way to postpone a decision.
Read →Equip: You Bought the Tools and Nothing Changed
88% of companies have bought AI tools. Most of them mistook the purchase order for the transformation. Procurement is not adoption, and seat counts are not leverage.
Read →The Five Phases of AI Adoption, and Where Companies Stall
AI adoption moves through five phases: Equip, Experiment, Operationalize, Industrialize, Transform. Each transition fails for a different reason, and almost everyone is stalled in the first two.
Read →When Building Gets Cheap, Shaping Becomes the Job
Becoming AI-Native isn't a tooling change. It's learning to shape a problem, set an appetite, and bet on the outcome.
Read →Your AI Feature Has a Gross Margin. Your CFO Just Can't See It Yet.
You can watch an LLM feature work and still have no idea what it costs you per customer. LLM Ops is two jobs, not one: Langfuse tells you what every model call did and what it cost, and Finout drops that cost into the same bill as your cloud, allocated per team, product, and customer. Wire them together and an AI feature stops being a mystery line on the OpenAI invoice and starts being a P&L you can defend.
Read →Your Company Doesn't Adopt AI. AI Digests Your Company.
AI is the first technology that metabolizes organizations rather than augmenting them. Going "AI-native" doesn't upgrade your operating model, it dissolves the structure that existed to manage problems AI just erased.
Read →Product Management Was a Workaround. AI Removed the Thing It Worked Around.
Product management was an optimization layer for a constraint, expensive software, that no longer exists. When building gets cheap, the PM's job inverts: from advocate for what gets built to editor of what shouldn't.
Read →Automating Your Support Queue Is the Worst Use of AI Your Company Will Make This Year
A support ticket isn't a cost to be deflected. It's the highest-fidelity evidence you have about where your product, pricing, and onboarding are broken, and the standard AI playbook is quietly destroying it.
Read →AI on Both Sides of the House
Most companies put AI to work on one side of the business and ignore the other. The leverage is in running it on both, with a human accountable where it touches customers.
Read →Digital Transformation Is an AI Problem Now
The old transformation playbook moved you to the cloud and tidied your processes. The new one rebuilds operations around AI. The lever changed; plans didn't.
Read →Becoming AI-Native: Rebuilding the Operating Model, Not Just the Product
Most companies are bolting AI onto an operating model designed for a pre-AI world. Going AI-native means rewiring how products are built and how the organization runs.
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