About / Internal AI Playbook
Field notes from building internal AI at work.
I build internal AI systems at an NYSE-listed company, from product knowledge and AI SDR workflows to customer success, expansion, and revenue automation. I’m writing the playbook I wish I had when I started.
01 / Why this exists
AI advice is easy to publish. Operating knowledge is harder to earn.
The same problems keep showing up: product knowledge has no owner, customer context is fragmented, an agent has too much permission, or nobody can tell whether a workflow changed an outcome. Those are the problems I write about.
02 / How I work
I start with the job as people do it today.
- Map the work
Find who does it, what slows them down, and which evidence they trust.
- Define the system
Name the decision, owner, inputs, permissions, and expected result.
- Test real cases
Run missing data, conflicting records, exceptions, and human overrides.
- Measure what changed
Check adoption, decision quality, downstream work, and the business result.
03 / Where to start