The AI conversation has gotten loud and strangely quiet at the same time. Loud with spend, pilots, decks, and declarations. Quiet where it counts. Inside teams. Inside calendars. Inside the unbilled hours nobody reports because they already feel behind. This is the phase of tech news where intelligence is everywhere and productivity keeps slipping through the cracks. Not hypothetically. Operationally. Culturally. Expensively.
MIT Media Lab research shows 95% of organizations see no business return on enterprise generative AI investment. McKinsey reports 80%+ of companies using AI are not seeing meaningful earnings gains. BetterUp Labs and Stanford’s Social Media Lab traced the bleed further. AI-generated work that looks finished, sounds confident, and quietly costs ~2 hours per instance to clean up. $186 per employee per month. $9M annually in a 10,000-person organization. Add trust erosion to the balance sheet and you start to see why this moment has weight. This is not a tooling failure. It is a systems failure wearing a polished interface.
That is why February 18, 2026 carries gravity. The Productivity Cost of AI is not a futurist sermon or a demo parade dressed up as thought leadership. It is a reckoning. The kind that surfaces in middle managers, inbox debt, and decisions nobody wants to revisit because the upside already shipped in slide 7. In a cycle dominated by tech news about scale and acceleration, this conversation slows the frame long enough to ask who is actually benefiting.
Bryan Hancock enters with scar tissue, not slogans. As one of the leaders of McKinsey & Company’s talent practice, Bryan Hancock has spent years studying how work really moves, how incentives distort behavior, and how technology collides with culture after the rollout applause fades. As co-host of the McKinsey Talks Talent podcast and co-author of Power to the Middle, Bryan Hancock has been consistent about one thing. Productivity is not software. It is design, accountability, and clarity under pressure.
Barbara Kellerman approaches from the other flank. Leadership, followership, responsibility. As a Fellow at Harvard Kennedy School’s Center for Public Leadership, its founding Executive Director, and a co-founder of the International Leadership Association, Barbara Kellerman has built a career examining power and ethics when accountability gets abstract. AI accelerates abstraction. Barbara Kellerman drags it back into the room where decisions have names attached.
This conversation lives in the uncomfortable middle where strategy meets behavior. Where output is mistaken for impact. Where speed turns into noise. In a market flooded with tech news about what AI can do, this moment focuses on what it is costing. Not in theory. In hours, dollars, and trust. February 18 is not about slowing innovation. It is about deciding who is actually getting faster, and who is just carrying more weight quietly.
