Insights
Field notes from making businesses AI-native.
5 pillars, 19 posts. No hot takes, no hype — this is what we would tell an operator over coffee, written down.
The library — 19 posts
- Context & the org brain4 posts
- Governance & shadow AI4 posts
- Bankable AI4 posts
- Architecture that lasts3 posts
- Sector worked examples4 posts
- Machine-readableThe list_insights tool on our public MCP endpoint returns this same list.
Every statistic links to its source. Figures we could not trace to a primary report were cut.
1Start here
The argument, in three posts.
Every company now has the same models. Almost none have the context. These three are the flagship case for why that gap is the whole problem, and where to start closing it.
01
Context & the org brain
Why context, not capability, decides what AI can do for a business.
02
Governance & shadow AI
Getting the upside of AI without the exposure.
- 01Would you know if a staff member pasted a customer contract into ChatGPT last week?
- 02The board's ten questions to ask before AI touches your data.
- 03Governed AI, plainly: how a business gets the upside without the exposure.
- 04Why "your data never trains a public model" is a promise you should demand in writing.
03
Bankable AI
Outcomes you can measure, and defend to a board.
- 01Why most AI projects stall, and the one thing the survivors do differently.
- 02Hours are not the whole story: the hard-dollar outcomes AI actually moves.
- 03What an AI Readiness Scan (now the AI Review) tells you that a demo never will.
- 04Automate, Connect, Delegate: what each step is really worth, and when.
04
Architecture that lasts
Foundations that outlast the model of the month.
05
Sector worked examples
The method applied to particular sectors. Examples, not limits: we work with any business.