Pillar
Practical AI adoption for operators, not hype
21 articles • Page 1 of 3
An AI audit for a business is an inventory plus an evidence check on every AI system in use. IBM found 92 percent of organizations hit by an AI-related breach in 2026 had no AI access controls. The inventory is where the surprise is.
AI pilots stall before production for boring reasons: no owner, no definition of done, no data path, no kill switch. CISA 1 May 2026: start low-risk. NIST AI RMF: Govern, Map, Measure, Manage.
AI agent governance fails when you approve each click and miss the path. Five in-policy refunds can still empty the drawer. Store intent, cap the run, gate pay/send/delete.
A non-human identity for an AI agent is a unique login for the agent itself, not a copy of yours. NIST 2026: own identifier, credentials, entitlements. If it uses your password, that is impersonation.
An MIT study found 95 percent of AI pilots showed no bottom-line impact. The AI last-mile problem is why: the demo is easy, and the integration into a real workflow is the whole project.
You cannot bolt intelligence onto a business with no clean, connected data underneath. Why “just add AI” quietly fails, and what AI-ready data actually means.
Over 80% of workers use unapproved AI tools, and nearly half keep using personal accounts even after a ban. Govern shadow AI by enabling it: make the sanctioned path easier than the workaround.
How AI assistants decide which businesses to recommend is a naming event. AI Overviews hit 2 billion monthly users in 2025. About 1.2 percent of local businesses get named in ChatGPT. Entity plus a specific page wins.
Eighty-eight percent of AI agent projects fail before reaching production. The gap between AI agents in production and agents that demo well is not a quality problem. It is a design problem rooted in compound failure math that most teams never calculate.
