A hospital system with twelve facilities and 14,000 clinical staff wanted to use large language models to assist with clinical documentation. Physicians spent an average of two hours per day on docume

Google's 2015 paper "Hidden Technical Debt in Machine Learning Systems" described a problem that has only gotten worse in the decade since. The paper's central observation was that the model itself is

AI systems fail differently than traditional software. A traditional software bug produces incorrect output deterministically. The same input always produces the same wrong output, and a fix eliminate

The majority of enterprise AI strategies are built on an implicit assumption: that the organisation's data is ready to support AI workloads. The assumption is almost always wrong. Data that is adequat

You have received a form letter. The salutation reads "Dear [Name]." The body discusses "your recent [transaction] at [location]." Somewhere near the bottom is a handwritten name and address, inserted

You have signed off on an AI initiative. Your team has a real workflow in mind. Say, triaging inbound operations tickets, drafting first-pass vendor reviews, or reconciling exception cases across thre

Professional services firms sell judgment, billed by the hour or by the matter. That makes them both the biggest winners and the most cautious adopters of AI. The upside is real: every firm carries ho

The pipeline runs every night at 2 a.m. Nobody fully understands it. The original author left in 2019. It is part SAS, part shell, part stored procedures, and part a spreadsheet someone emails in. It

The demo looked great. The model summarised the document cleanly, answered the test question correctly, and produced prose that read well enough to ship. Two weeks later it is in production, and the c