As the internet floods with machine-generated content, the promise of infinite, privacy-compliant synthetic data seems too good to pass up. But industry veterans warn that feeding AI its own output creates a dangerous feedback loop where causation is lost and bias is amplified.
Whilst industry discourse fixates on what's next, enterprise leaders are grappling with what went wrong. From endless proof-of-concept cycles to the mythology of scale, 2025 revealed critical missteps that turned promising AI initiatives into costly lessons. Five patterns—drawn from practitioners across banking, technology, and research—that defined why so many AI projects stalled, and what actually needs to change.
Foundation models are advancing quarterly, enterprise deployments are moving from pilots to production, regulations are reshaping compliance requirements across regions, and infrastructure investments rare...
For decades, the story of drug discovery has been one of painstaking, manual work—a scientist in a lab coat, screening thousands of compounds—a process...
Deploying an AI agent without continuous evaluation is a recipe for risk. Here’s a practical guide to building systems that ensure your AI is accurate, safe, and trustworthy.
