AI

He's been an account manager for two decades, and he told me at the gym that he thinks he's becoming expendable. His department is merging into Customer Success, he isn't confident with AI, and nobody has told him what he now owns. The data on AI and job loss is more nuanced than the headlines, and it points somewhere uncomfortable for leaders. Here's what employees are genuinely accountable for, what leadership owns, and the four things every leader has to get right during a

AI is transforming healthcare, but are we overlooking one of its most important costs? We measure ROI, implementation costs, and adoption risk, yet almost no AI business case considers environmental impact. Here's why that needs to change and how healthcare leaders can make better AI decisions before regulations catch up.

A 5-mile hike with no cell service turned into a question: AI was supposed to give us shorter workdays, so why are so many teams working longer than ever? This issue digs into the data on vacation, disconnection, and the 996 overwork culture spreading through AI startups, then into what decades of productivity and health research actually show. The takeaway for leaders is not “work less.” It is that sustainable performance is an operational design problem, and the teams that

At a graduation party, a retired fisherman told me I was the first person ever to thank him for 30 years of work. It stuck with me. So I asked the research a question: does a simple, authentic thank-you still matter for customer loyalty and growth in the age of AI? The psychology is clear and freshly confirmed. The business case is real. And the part everyone is asking about, whether AI can replicate it, is exactly where the data runs out. Here is what the evidence says, and

The U.S. honored its more than 1.1 million war dead yesterday. The same day, thousands more lost their jobs to a downsizing playbook 40 years of research shows doesn't work. From Klarna's failed AI rehire reversal to MIT's 2025 NANDA findings on enterprise AI struggles, the data keeps revealing the same pattern: when companies forget the knowledge their people carry, the cost compounds for years. So why do we keep repeating the same mistakes, and what does it take to actually

35% of new websites are AI-generated. 37% of customers walk away the moment they realize they're talking to AI instead of a human. The noise is real, and most of what's being published in 2026 is workslop. So how do healthcare, SaaS, and Customer Success leaders cut through it? Four data-driven principles (and three operating moves) for being heard without becoming part of the slop.

Healthcare doesn't have a revenue problem. It has a Revenue Operations identity crisis. Two completely different systems run in parallel — SaaS RevOps on one side, hospital Revenue Cycle on the other. Both call themselves "revenue." Neither is complete. And AI is accelerating the collision. Inside: where the $48B blind spot lives, why payers are pulling ahead, and what healthcare leaders need to do before the gap widens further.