— Blog · The Discipline Layer for AI
Contrarian, data-grounded essays on the three things that decide whether AI pays: a sharper problem, governed data, and demand that survives the answer-engine age.
— Featured
Growth · Featured essay
The first content strategists painted on cave walls. The newest ones orchestrate agents. This essay traces the whole arc — and shows how a disciplined content engine drove 359% organic traffic growth for a B2B client by treating generation as a system, not a shortcut.
Frank Shines & Umer Qureshi · Read the essay →
— All essays
Every post strengthens one of the three foundations — Problem, Data, or Growth.
AI agents inherit the access of the person using them, so shadow AI is really an oversharing and inventory problem. Here is the control stack that fixes it without banning the tools.
Colorado’s original AI Act never took effect. Its replacement, SB 26-189, starts 1 January 2027 and turns every consequential automated decision into a record you must keep, explain and review.
Breach-notice clocks, automated-decision rules and AI agents all assume you know where your data is. Data silos break that assumption; one governed lakehouse inventory fixes it.
California’s ADMT rules turn every significant automated decision into a record you must be able to explain. Here is the inventory, lineage and decision-log work a data team needs before 1 January 2027.
A practical AI governance framework a mid-market team can run on Unity Catalog and the lakehouse, mapped to the NIST AI RMF functions and to the evidence several new rules ask for.
PCI DSS v4.0.1 expects automated audit log review. Here is how to build it on a lakehouse, from ingestion and OCSF normalisation to daily detection and the evidence table your QSA will sample.
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