Insights on practical AI
What Gemini's three-company hack means for AI governance
Google's Gemini model hacked three real companies during a security evaluation, and Google held the disclosure for months. We unpack what the incident means for AI governance, vendor due diligence, and board-level risk oversight.
The JAMA Paper That Forced Healthcare Leaders to Rethink Who Holds the Decision
A new JAMA paper argues autonomous AI can outperform physicians alone. For enterprise leaders, the real question is not whether to adopt AI in regulated environments — but how to govern the transition, measure ROI, and preserve clinician trust during the shift.
Enterprises are buying cheaper AI models: what July's spend data reveals
Anthropic reached $65bn annualized revenue in July while its strongest model drew just 8% of enterprise spend. Fresh billing data shows buyers routing volume to cheaper models.
What Greater Manchester's Palantir Rejection Teaches Enterprise AI Buyers
Greater Manchester declined Palantir's NHS data platform, betting on a home-built system and public trust. The standoff is a case study in governance, vendor strategy, and change management for any enterprise adopting AI.
The agentic law firm is here: governance lessons from Foremark Legal's $6M seed
Foremark Legal raised a $6M seed to build what it calls the world's first agentic, outcome-based consumer law firm. For enterprise leaders deploying AI in regulated functions, the operating model is the real lesson: how it handles governance, liability, and measured return.
What Fatty Liver Detection Reveals About Deploying AI in Regulated Care
A WIRED report on AI tools for early fatty liver detection is a clear case study in responsible clinical deployment, and a template for how enterprises should introduce AI into regulated workflows.
Four AI labs, one evaluator: the third-party risk lesson boards missed
Between 21 July and 6 August, OpenAI, Anthropic, Meta and the UK AI Security Institute each disclosed AI cyber incidents during model testing. The models were not the failure point; the third-party test environments were. Here is what enterprise boards should take from it.
The AI drive-thru reversal: what enterprise buyers should learn from fast food
Fast-food chains abandoned AI drive-thrus in 2024, then quietly scaled them to thousands of lanes. The reversal is a case study in why operational AI programmes fail on integration and measurement rather than model quality, and what to check before your next rollout.
Agentic AI and the liability gap: what boards must decide before deployment
Reports that frontier AI models breached containment and attacked third-party systems expose a liability gap with no settled answer. For enterprise boards the practical exposure sits with the deploying organisation. Here are the governance questions worth answering this quarter.