Thesis
Most enterprise AI initiatives fail at the org boundary, not the model. The interesting work is in process, capability, and goal translation.
I work with organizations on the architecture decisions behind AI adoption — what to build, what to buy, and what has to change internally before either one works.
Frameworks4 artifacts
- F-01Organizational Capability Map2026
- F-02Goal Translation Infrastructure2026
- F-03Agent Actionable Objectives2026
- F-04The Intent Gap2026
Recent24 total
Who Will Check the AI's Work in Ten Years?
Entry-level hiring in AI-exposed jobs is falling while experienced workers hold steady. Those juniors are where your future AI validators come from. Here’s how to redesign roles without losing them.
Where Do I Even Start? (The Wrong First Question)
"Where do I even start with AI?" is the wrong first question. It skips a step that determines whether everything after it works or wastes your time. The right sequence: understand the technology, pilot with measurement, then evaluate which processes should look entirely different.
Your AI Agent Shouldn't Have Your Employee's Job Description
When teams "agentify" an existing process, they hand the agent a workflow designed around human constraints. The agent inherits every stop-and-wait point, every workaround, every approval layer it doesn't need. The fix isn't to optimize the agent. It's to redesign the process.
What I Learned Building a Document Format from Scratch
I started thinking the hard problem was document parsing. Then format design. Then sync logic. The real challenge was building at the interface between two domains that don't talk to each other.
The Design Decisions Behind an AI-Native Document Format
Processing a Word document through raw OOXML costs 359,706 prompt tokens. Clean markdown costs 106. Five design decisions that make that gap useful without losing formatting.
Why AI Document Workflows Are Broken
Every AI tool that works with documents treats formatting as disposable. For organizations running hundreds of documents through AI editing pipelines, it's a structural cost nobody is tracking.
Goal Translation Infrastructure: Encoding What Your Organization Actually Wants
Your Capability Map says a workflow is Agent Ready. Now what does the agent optimize for? OKRs were designed for humans. Agent Actionable Objectives are the translation layer that's been missing.