Practical AI operating guides across production AI.
Chapters, frameworks, templates, scorecards, decision tools, and case studies built from the operating picture teams pay us to map.
The 12 Levers of Enterprise AI Strategy.
An AI operating model for where AI creates value, where risk forms, who owns the lever, and what evidence proves progress.
AI Fluency is a learning curve
Editorial reads on where AI work stalls.
AI operating stack
AI systems, controls, and evidence.
The 12 levers
Levers, maturity models, and control maps.
Audit memorandum template
Audit memos and reusable eval artifacts.
Scorecard library
Fast reads on adoption, strategy, and governance.
PE portco control pattern
Proof from AI work in production.
Golden datasets for AI evaluation
Reusable test sets for AI evaluation.
NL-to-SQL evals
Audit-grade evidence for data agents.
What is an AI Audit
Inventory, Shadow AI, and board evidence.
Shadow MCP audit methodology across production AI.
Find unauthorized tool paths, owners, and evidence gaps before agents wire themselves into systems of record.
Shadow AI self-diagnostic across production AI.
A fast way to separate sanctioned tools, unmanaged usage, and the workflows that need an independent read.
How enterprises get mis-sold AI.
A buyer-side lens on demos, dashboards, and seat metrics that never prove the work changed.
AI Agent Gateways: What They Catch, and What They Miss.
Where gateways help, where source lineage still leaks, and why the trace layer has to sit underneath.
Agents scale execution. Responsibility still needs a seat.
A governance read on reviewer ownership, exception routing, and when autonomous output becomes the record.
AI fluency is a learning curve.
Why teams need role-level practice, not one-off training completion, before AI adoption compounds.
NL-to-SQL Evals for Finance.
Audit-grade checks for data agents whose answers land in finance, FP&A, and operating dashboards.
The AI Audit checklist for teams.
The quick-read checklist for inventory, Shadow AI, material workflows, owners, and board evidence.
The AI operating stack across production AI.
How systems, controls, evidence, and workflow ownership fit together before a rollout scales.
Start with the AI work that moves the number. Keep the proof built in.
Start with Strategy, Transformation, or Fluency; use Quick Audit when the first need is an independent read on what is already running.