Insights
Practical writing on whether AI changes the work, what it costs across the full workflow, where human judgment still matters, and how to prove the result.
AI activity is easy to count. Business improvement is harder to prove. These notes focus on the decisions around implementation: where AI belongs, what changes when it is introduced, and what evidence should determine whether it stays.
Workflow Economics · 5 min read
A faster team or cheaper task does not automatically mean the business improved. AI can move cost, delay, risk, or rework somewhere else.
Read insight : A Local AI Win Can Still Be a Bad Business OutcomeProof · 6 min read
Deployment is not proof. The useful question comes after implementation: what changed, compared with what, and was the result worth keeping?
Read insight : Did AI Actually Improve the Work?Governance · 5 min read
AI can be useful inside a workflow without being ready to run that workflow. Capability and authority are different decisions.
Read insight : AI Suitability Is Not Automation SuitabilityReadiness · 6 min read
Using AI and being ready for AI are not the same thing. The basic questions stay surprisingly consistent from one person to a large organization.
Read insight : The AI Readiness Gap: Same Problem. Different Scale.