AI in practice. Evidence over hype.
Practical guidance on AI strategy, workflow design and adoption, with leadership reflections and live venture concepts.
Turning an AI Rollout into Everyday Use
Design an adoption plan around real tasks, credible champions, protected practice time and measures that show whether the new workflow helps.
Which Workflow Should You Automate First?
A practical scorecard for choosing an automation pilot, testing the business case, and separating released capacity from cash savings.
AI for Health Equity Requires Evidence and Oversight
Evaluate a specific barrier to care, distinguish administrative assistance from clinical decisions, and test who benefits, who is missed and who can seek help.
Who Owns an AI Workflow?
Assign practical decision rights for an AI workflow, from business outcomes and technical changes to exceptions, incidents and benefits tracking.
Prioritizing AI Use Cases with Evidence, Cost and Risk
Compare three hypothetical AI opportunities using operational baselines, uncertainty, total cost and explicit reasons to proceed or defer.
What Is Machine Learning? A Practical Buyer’s Guide
Choose between rules, predictive models and generative AI by testing the business task, data quality, cost and consequences of failure.
Effective Decision Making in the Age of AI
Clarify who decides, which evidence matters, when human review is required and how the result will be checked. A practical guide for AI-enabled work.