Episode #278
278: How to Take Innovation from Outcomes to Impact
What happens when AI makes it easier than ever to produce work, but organizations still struggle to turn that activity into meaningful results? In this episode of the Innovation Storytellers Show, I speak with Dr. Mik Kersten, creator of the Flow Framework and founder of Tasktop. Mik is the author of Project to Product, and Output to Outcome, about why the AI era demands a very different way of thinking about innovation. He has spent much of his career studying how ideas move through organizations, from his early work at Xerox PARC to building technology for value stream management. One lesson has stayed remarkably consistent: large organizations rarely struggle because they lack ideas or ambition. They struggle because their structures, incentives, approvals, and operating models slow the feedback loops required for innovation. AI is making that problem much harder to ignore. When knowledge outputs become cheap and plentiful, measuring productivity by documents created, code written, or tasks completed begins to lose meaning. Mik argues that organizations need to move toward outcome-driven operating models that ask a much more useful question: did the work improve something for the customer, employee, or business? We discuss why some large companies can take around 120 days to move an idea to a customer, while smaller teams can test ideas within weeks. We also examine what happens when AI accelerates a poorly designed system and simply produces more work for humans to review. Mik explains his concept of the outcome loop, where strategy leads to outputs created by humans and AI, followed by customer and employee outcomes that inform the next round of decisions. For leaders thinking about how to communicate AI transformation, this changes the conversation from productivity to effectiveness. We also talk about storytelling for C-suite leaders and why internal examples may become some of the most persuasive tools for winning AI stakeholder buy-in. Instead of telling employees that AI transformation is inevitable, leaders can show where new ways of working are already producing results, what failed along the way, and what the organization has learned. Our conversation also raises a bigger question about human-AI collaboration. Mik believes AI should support decision-making, but accountability must remain with people. His view is simple: humans should continue to report to humans, particularly when organizations are deciding which outcomes technology should pursue. As AI gives organizations the ability to create more, faster, the competitive advantage may no longer come from the volume of work being produced. It may come from knowing which outcomes are worth pursuing in the first place. How is your organization measuring the difference between AI activity and genuine impact?
