AI Strategy
Where AI actually pays: adoption phases, unit economics, and the operating-model change that separates leverage from theater. 21 essays, from 25 years at the seam between the boardroom and the codebase. This is the thinking behind AI strategy consulting.
Who Is Responsible When AI Gets It Wrong?
Somebody in your company is already accountable for an AI mistake that hasn't happened yet. They just don't know it, because nobody wrote the name down.
Read →Twelve Questions to Ask an AI Vendor Before You Sign
Every AI demo runs on data the vendor chose. These questions are about everything the demo left out, which is where your money actually goes.
Read →How to Build an AI Business Case Your CFO Won't Reject
Most AI business cases fail on the same two lines: value counted as saved hours nobody ever removes, and a cost model that stops at the build.
Read →The AI Readiness Checklist: Twelve Questions Before You Build
Most failed AI projects were decided before anyone wrote a prompt. These twelve questions surface the reason in an afternoon, and you can run them yourself.
Read →Why AI Pilots Fail: 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 →AI Adoption Phase 5, Transform: The 6% Who Redesigned the Org
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 Economics: When a $20K Box Beats the Cloud
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 →AI Adoption Phase 4, Industrialize: Scale Agents, Not 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 →Claude Fable 5 Pricing: The Price Went Up, 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 →AI Adoption Phase 3, Operationalize: Agent to 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 →AI Adoption Phase 2, Experiment: From Pilot to a Production Decision
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 →AI Adoption Phase 1, Equip: You Bought Tools, 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 →How AI Changes Software Economics: When Building Gets Cheap
Becoming AI-Native isn't a tooling change. It's learning to shape a problem, set an appetite, and bet on the outcome.
Read →LLM Cost Tracking With Langfuse and Finout: AI Gross Margin
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 →What Is an AI-Native Operating Model? Digestion, Not Adoption
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 →Is Product Management Dead? AI Removed Its Reason to Exist
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 →Should You Automate Customer Support With AI? Mostly No
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 Strategy for Operations and Customer Experience: One Loop
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 →AI Digital Transformation: The Playbook Has Changed
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 →How to Become an AI-Native Company: Rebuild the Operating Model
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.
Read →