Now to Next: The Signal

What if the same AI tools making employees faster today are quietly weakening the capabilities organizations will need tomorrow?In Episode 3 of Now to Next: The Signal, Jason Averbook sits down with Dr. Vivienne Ming, a computational neuroscientist, entrepreneur, and researcher who has spent decades studying intelligence, human potential, and how technology changes what people can do.Vivienne shares the deeply personal transformations that shaped her work before challenging some of the most common assumptions about AI in the workplace. Drawing on her research and studies of human-AI collaboration, she explains why automating a task is not the same as developing a person, why certain forms of AI assistance can undermine learning, and why human oversight does not automatically lead to better decisions.Jason and Vivienne explore three different relationships between humans and AI: automators, centaurs, and cyborgs. Their discussion raises difficult questions about early-career talent, organizational design, leadership, and what happens when businesses no longer need the traditional roles through which people once developed expertise.The conversation ultimately asks executives to reconsider what human value means in an AI-enabled organization. If machines can increasingly produce the right answers, are we developing people who can ask better questions, challenge assumptions, and explore problems nobody knows how to solve?HighlightsWhy greater AI productivity does not necessarily mean employees are becoming more capable.How AI assistance can create dependence instead of developing expertise.What separates automators, centaurs, and cyborgs in human-AI collaboration.Why the familiar human-in-the-loop approach can sometimes produce worse outcomes.How AI can generate answers that look convincing even when the underlying decisions are wrong.What happens to early-career development when machines perform the work people traditionally learned from.Why Vivienne believes an individual's unique perspective becomes more valuable as routine answers become easier to produce.How hybrid intelligence differs from simply delegating tasks to AI agents.Why organizations must reconsider how they recognize, develop, and reward human capability.What Vivienne's personal transformations reveal about identity, purpose, and the difficulty of meaningful change.Why management, education, and talent development need as much attention as AI technology itself.How leaders can build organizations that make people better instead of simply making processes faster.About Jason AverbookJason Averbook has spent thirty years watching the gap between where organizations say they are with technology and where work is actually happening. He has a name for it. He has a framework for closing it. And he built Now to Next to do exactly that.Now to Next is the AI studio Jason co-founded with Jess Von Bank in 2026, built on practitioner intelligence, original research, and the conviction that human transformation precedes digital transformation. Always has. Always will.Before Now to Next, Jason co-founded Leapgen, acquired by Mercer in 2023, where he led global HR transformation strategy and the firm's generative AI practice. Earlier he served as CEO of The Marcus Buckingham Company, co-founded Knowledge Infusion through its acquisition by Appirio, and held senior roles at PeopleSoft and Ceridian. He has advised hundreds of Fortune 1000 companies, authored two books on the future of HR and workforce technology, delivered more than 1000 keynote presentations globally, and teaches as an adjunct professor at universities worldwide.About Dr. Vivienne MingDr. Vivienne Ming is a computational neuroscientist, entrepreneur, and researcher whose work spans artificial intelligence, human intelligence, neuroscience, and human potential. She has built and applied AI models for decades, working across scientific research, commercial ventures, and philanthropic projects. Her work includes research and ventures addressing Alzheimer's disease, postpartum depression, and the use of AI to expand human capability.Stay ConnectedJason AverbookLinkedIn: https://www.linkedin.com/in/jasonaverbook/X: https://x.com/jasonaverbookSubstack: https://jasonaverbook.substack.com/Now to NextWebsite: https://nowtonext.ai/Podcast: https://podcast.nowtonext.ai/episodesLinkedIn: https://www.linkedin.com/company/now-to-next-ai/Instagram: https://www.instagram.com/nowtonext.ai/YouTube: https://www.youtube.com/@nowtonextaiDr. Vivienne MingLinkedIn: https://www.linkedin.com/in/vivienneming

Six months can now make an AI idea go from sounding years ahead of its time to something people are already using every day. That compression creates a difficult question for executives: how do you make durable decisions when the technology, tools, and ways of working may change before a traditional planning cycle is finished?In Episode 2 of Now to Next: The Signal, the new season of Jason Averbook’s Now to Next podcast, Jason reconnects with Johannes Sundlo, a longtime HR practitioner who spent roughly 15 years inside organizations, including Spotify and Avalanche Studios Group, before focusing his work on AI, people, and organizations.Their conversation starts with agents but quickly becomes much bigger. Jason and Johannes explore what happens when employees bring personal agents into work, whether organizations are prepared for knowledge that moves with those agents, why long technology bets are becoming harder to make, and why data becomes even more important as the interface between employees and enterprise systems changes.They also confront a harder question for HR. If the workforce increasingly includes both people and machines, does it still make sense to organize HR and IT as separate worlds? Is another round of HR transformation enough, or is the function itself approaching a more fundamental redesign?This is a conversation about making decisions without pretending the environment is stable. The signals are already here. The question is whether executives are willing to redesign around them.HighlightsHow quickly agents moved from an emerging idea to something individuals are already using as part of everyday work.Why personal agents stop being purely personal the moment employees begin using them to perform work.What happens when an employee’s agent develops knowledge about the company and the employee eventually leaves.Why Jason believes organizations need to build a coordinated context layer so people can benefit from what the organization collectively knows.Why traditional long-term technology planning becomes harder when capabilities can change materially within months.Why Jason believes organizations should pay much more attention to signals than predictions right now.Why good data becomes more important as agents increasingly sit between employees and enterprise systems.How years of vendor lock-in, fragmented systems, and weak data practices create new constraints for HR.Whether the traditional separation between HR and IT still makes sense when organizations are managing both people resources and machine resources.Why another round of incremental HR transformation may not address the magnitude of the change now underway.Why leadership investment and psychological safety become more important, not less, when the technology is moving quickly.The difference between asking what AI could do and deciding what it should do inside a specific organization.Why practical experimentation matters more than claiming expertise in a field that changes almost daily.Jason’s argument that this is not another client-server-to-cloud transition, but a fight for relevance at the company, function, role, and individual level.Why the next employee experience may be defined less by navigating enterprise applications and more by agents acting across them.About Jason AverbookJason Averbook has spent thirty years watching the gap between where organizations say they are with technology and where work is actually happening. He has a name for it. He has a framework for closing it. And he built Now to Next to do exactly that.Now to Next is the AI studio Jason co-founded with Jess Von Bank in 2026, built on practitioner intelligence, original research, and the conviction that human transformation precedes digital transformation. Always has. Always will.Before Now to Next, Jason co-founded Leapgen, acquired by Mercer in 2023, where he led global HR transformation strategy and the firm’s generative AI practice. Earlier he served as CEO of The Marcus Buckingham Company, co-founded Knowledge Infusion through its acquisition by Appirio, and held senior roles at PeopleSoft and Ceridian. He has advised hundreds of Fortune 1000 companies, authored two books on the future of HR and workforce technology, delivered more than 1000 keynote presentations globally, and teaches as an adjunct professor at universities worldwide.He is consistently recognized as one of the top three thought leaders globally on the future of work.About Johannes SundloJohannes Sundlo describes himself as a recovering HR person who spent roughly 15 years working inside organizations before turning his focus to AI and its implications for people and organizations.His HR career has included HR business partner and HR director roles, with experience at Spotify and as VP of HR for Avalanche Studios Group. Today, his work focuses on making sense of how AI is changing people, organizations, work, and the decisions companies need to make as these technologies develop.Stay ConnectedJohannes SundloLinkedIn: https://www.linkedin.com/in/jsundloJason AverbookLinkedIn: https://www.linkedin.com/in/jasonaverbook/X: https://x.com/jasonaverbookSubstack: https://jasonaverbook.substack.com/Now to NextWebsite: https://nowtonext.ai/Podcast: https://podcast.nowtonext.ai/episodesLinkedIn: https://www.linkedin.com/company/now-to-next-ai/Instagram: https://www.instagram.com/nowtonext.ai/YouTube: https://www.youtube.com/@nowtonextaiMusic licensed through Soundstripe. Code: TBQVYMB7VBCCFCOMMusic licensed through Soundstripe. Code: HERZZAKFM3OU5UNAMusic licensed through Soundstripe. Code: MII1QR1O0RUSAFRO, YFOTD088TC3UTPZY, YO1MNUJ1QEHI5AAC

Most executives are being asked to make consequential decisions about AI before they have personally experienced what the technology can actually do. That creates a fundamental leadership problem: how do you set strategy for something you understand primarily through presentations, demonstrations, and other people’s explanations?In Episode 1 of Now to Next: The Signal, the new season of Jason Averbook’s Now to Next podcast, Jason sits down with Alison McCauley, two-time bestselling author and author of How to Think with AI, to examine the growing gap between what AI makes possible and what most enterprises are actually doing with it. Jason and Alison explore what that disconnect means for leadership, learning, trust, culture, work design, and the role of HR. They challenge conventional measures of AI adoption, examine what happens when employees understand the possibilities before leadership does, and ask how organizations should prepare for a workforce that increasingly includes both human and digital workers.The conversation ultimately moves beyond AI itself. It asks whether executives, operating models, and institutions can learn and change quickly enough to make intelligent use of a technology that will not wait for them to catch up.Episode HighlightsWhy leaders need direct experience with AI before they can shape a meaningful strategy.What changes when software learns from people instead of people learning the software.Why the gap between personal and enterprise AI is becoming impossible to ignore.How speed to learn can matter more than speed to launch.Why login rates reveal almost nothing about whether work is actually changing.What HR must do when the workforce includes both humans and digital workers.About Jason AverbookJason Averbook has spent more than 30 years shaping HR technology and workforce transformation. His career includes leadership roles at PeopleSoft and Ceridian, co-founding and leading Knowledge Infusion from 2005 to 2012, serving as CEO of The Marcus Buckingham Company, and co-founding and leading Leapgen from 2018 to 2023. After serving as Mercer’s Global HR Transformation Leader, he co-founded Now to Next to help leaders prepare people and organizations for an AI-shaped world.About Alison McCauleyAlison McCauley is a two-time bestselling author who has spent three decades operating as a conduit between Silicon Valley disruptors and enterprise leaders seeking to leverage new technology. As an adviser to both the enterprise C-suite and emerging technology founders, she has helped develop new market categories and partnered with early leaders in AI since 2010.As a social scientist, Alison’s work is grounded in human behavior, organizational dynamics, and the challenge of translating innovation into performance. Her most recent bestseller, How to Think with AI, grew from a year of research studying hundreds of AI early adopters and turns those insights into a framework for helping people use AI as a cognitive advantage. Her work has appeared in Harvard Business Review and Forbes, and her executive education course has reached more than 100,000 learners.Stay ConnectedAlison McCauleyWebsite: https://www.alisonmccauley.io/LinkedIn: https://www.linkedin.com/in/unblocked/Jason AverbookLinkedIn: https://www.linkedin.com/in/jasonaverbook/X: https://x.com/jasonaverbookSubstack: https://jasonaverbook.substack.com/Now to NextWebsite: https://nowtonext.ai/Podcast: https://podcast.nowtonext.ai/episodesLinkedIn: https://www.linkedin.com/company/now-to-next-ai/Instagram: https://www.instagram.com/nowtonext.ai/YouTube: https://www.youtube.com/@nowtonextaiMusic licensed through Soundstripe. Code: TBQVYMB7VBCCFCOMMusic licensed through Soundstripe. Code: HERZZAKFM3OU5UNA

Everyone wants to know which AI tool to buy, but far fewer people are willing to talk honestly about what happened after they bought it. After 28 episodes of his previous podcast, Jason Averbook is beginning a new season with a new name and a sharper purpose: Now to Next: The Signal. It is designed to be an hour without the noise, built around candid conversations with executives, technology leaders, and practitioners who are already doing the work of transformation.These conversations will go beyond polished case studies and easy slogans. Jason wants to understand how work actually changed, where organizations automated dysfunction, who was left behind, and what leaders still have not figured out. The goal is not to feed the fear or manufacture reassurance. It is to stay with the questions that matter, especially when the answers are not clear.This season is for CIOs, CXOs, CHROs, CEOs, transformation leaders, and anyone curious about people, technology, and the way the world is changing. The premise is simple: AI is not the transformation. You are.Episode HighlightsWhat changes after 28 episodes when the conversation gets more focused?Why are so few people asking what actually changed after the AI arrived?When does automation become dysfunction moving faster?The most useful transformation conversations start where the easy answers end.What remains uniquely human when machines can do more of the work?The fear beneath the AI conversation may be the fear of becoming irrelevant.About Jason AverbookJason Averbook has spent more than 30 years shaping HR technology and workforce transformation. His career includes leadership roles at PeopleSoft and Ceridian, co-founding and leading Knowledge Infusion from 2005 to 2012, serving as CEO of The Marcus Buckingham Company, and co-founding and leading Leapgen from 2018 to 2023. After serving as Mercer’s Global HR Transformation Leader, he co-founded Now to Next to help leaders prepare people and organizations for an AI-shaped world.Music licensed through Soundstripe.Code: TBQVYMB7VBCCFCOM

Most organizations are treating AI like a technology rollout. Buy the tool, train the team, check the box. Melissa Reeve has spent 25 years watching that exact playbook fail, from digital transformation to agile to cloud, and she wrote a book about why this time the stakes are even higher. In this episode, Jason and Melissa dig into why AI represents the largest management challenge in the last century and why giant brands will fall if they keep running on 100-year-old management thinking while AI-native startups are eating their lunch. Melissa breaks down the Hyperadaptive Model from her new book, a five-stage framework for rewiring how organizations actually operate, not just how they deploy technology.

What happens when AI can do more of the work we've traditionally assigned to people? For many organizations, the conversation quickly turns to automation, efficiency, and workforce disruption. But according to Lara Albert, the more important conversation is about adaptability. As AI continues to reshape how work gets done, leaders face a critical challenge: helping people develop the skills, judgment, and confidence needed to thrive alongside increasingly intelligent technology. The future will not belong to organizations that simply deploy AI. It will belong to organizations that help people evolve with it. In this episode of Now to Next, Lara Albert joins me to explore the realities behind AI transformation, workforce readiness, leadership responsibility, and the human capabilities that will matter most in the years ahead.

What happens when employees walk into AI training already convinced the technology might replace them? Most organizations are treating AI adoption as a technology challenge. Dr. Xenia Wade believes that is exactly where leaders are getting it wrong. Drawing from her research background, enterprise transformation work, and recent move from Germany to Japan, Xenia explains why AI creates a level of fear and vulnerability that previous technology transformations never did. Employees are not just learning a new tool. Many are questioning their future, their identity, and the value of skills they spent years developing. In this conversation, we explore why psychological safety has become a prerequisite for AI adoption, why traditional change management is no longer enough, and why trust may be the most important capability leaders need to build right now.

BCG dropped two studies that should be taped to the wall of every CHRO's office. One shows that adding AI "employees" to org charts is making human workers sloppier and more likely to blame the bot when things go wrong. The other — "AI brain fry" — shows that cognitive overload from managing AI tool sprawl is driving errors, burnout, and intent to quit. Meanwhile, 48% of Q1 tech layoffs were attributed to AI, Gen Z workers are listing AI skills they don't have on their résumés, and boards are demanding governance answers most executives cannot give yet. But the story underneath all of that — the one you won't find in any headline today — is a trust collapse. Workers don't believe what leaders are telling them about AI anymore. And that gap is now a credibility crisis. This episode is about all of it.

What does it really take to integrate people, culture, and technology in a way that drives transformation, not just change? As organizations race to adopt AI and digital platforms, many are still treating technology and people as separate strategies. But what happens when those two become one system? In this conversation, I sat down with Tracey Franklin, Chief People and Digital Technology Officer at Moderna, to explore what it actually looks like to connect people and technology in practice, not just in theory.

What if the “AI agents” your organization is investing in aren’t actually agents at all? I woke up one morning, checked my phone, and saw yet another company announcing they are now an “AI agent company.” I have seen it over and over again. Different vendors. Same claim. No clear definition. I walk through in this episode what actually separates a workflow, a copilot, and a true agent, and why that distinction matters more than ever. I also share real examples of where organizations are getting this wrong, and what happens when you layer AI on top of broken processes. This is not about chasing the next buzzword. It is about knowing what you are actually deploying, what you can trust it with, and whether your foundation is ready for it. Because putting AI on top of broken processes does not create transformation. It hides the problem until it shows up at scale. If we cannot define what we are deploying, we cannot govern it, trust it, or scale it.