Insights on practical AI
A 14.7MB model is the most interesting AI release this week
A 14.7MB PII-stripping model shipped this week and most AI coverage skipped past it. For enterprise AI governance teams running regulated deployments, the release points at where the industry is heading — pre-processing layers at the edge, not policy layers in the vendor stack.
Ford rehired 300 engineers. The case for keeping humans in the AI loop.
Ford rehired 300 veteran engineers after AI failed to deliver the expertise required. Enterprise AI roadmaps should read it as a verification-economics question, not a generation-throughput one. Here is where the case lands on the board agenda.
AI agent visibility: the structured profile your enterprise needs by Q4
Enterprise strategy teams now face a new visibility layer: AI agents like ChatGPT, Claude, and Perplexity decide which vendors they can find, understand, and contact. Here is the four-step rollout for an agent-readable business profile, with the governance questions to clear on day one.
When AI Sounds Most Useful Is When It Should Be Saying 'I Don't Know'
A new arXiv paper shows AI caution collapses when prompts push for a clear business answer — from 91% careful in researcher voice to under 10% in executive voice. What enterprise AI governance teams should change.
How Cornell Recovered $100,000 in Unidentified Payments With a Claude Skill
Cornell's finance and AI teams built a small Claude skill called /treasury that recovered roughly $100,000 in unidentified payments. Here is the two-pillar adoption model that produced it, and what it means for enterprise back-office teams running their own AI labs.
Bland's $100M bet on the long, high-stakes AI phone call
Bland just raised $100M to automate 45-minute high-stakes phone calls. Here is what changes for enterprise call centers, where the value lands, and the governance questions that surface on day one of deployment.
AI coding tool procurement: the four questions that catch the real risks
The Cline team's GLM 5.2 vs Opus 4.8 comparison surfaces what enterprise procurement teams should measure on AI code tools. Cost per resolved ticket, code quality beyond the test suite, and four questions that separate a serious evaluation from a vendor demo.
Why the cheapest model that hits your AI KPIs is probably the wrong default
Defaulting to the cheapest AI model that clears today's KPIs hides the upside of smarter models on harder tasks. Build architectures that let you swap models without rewriting your stack, so you can find the work that actually benefits from higher intelligence.
Your AI Agent Is Now the Phishing Target — and You Can't Patch the Model
The 2025 Microsoft 365 Copilot attack made one thing clear: when an AI agent reads an email and obeys it, the model itself is the attack surface. Here is what enterprise governance has to look like in 2026.