Insights on practical AI
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.
When Competitive Intelligence Costs $129/Month, What Is Your Strategy Team Actually Doing?
AI-native competitive intelligence tools collapse a week of analyst work into a single report. Here is what enterprise strategy teams should keep, automate, and own in 2026.
When the model is the attack surface: enterprise AI risk in 2026
Prompt-injection attacks on Microsoft 365 Copilot reveal that the AI itself is now an attack surface. Boards need governance frameworks built for models they cannot patch.
When a Smaller Model Beats a Frontier One: What Heidi Health's Clinician Reward Function Tells Enterprise AI Teams
Heidi Health matched a frontier model on clinical evidence in six weeks with a fraction of the parameters. The lesson for enterprise AI teams is that the metric you pick decides the winner, and most programs never pick the right one.
Why AI Regulation Needs to Look Beyond the Model
Regulating AI requires looking at the full system — models, harnesses, skills, and connected tools — not just the algorithm. A framework for enterprise decision-makers navigating compliance.