The rise of AI agents found in all corners of the workplace, including high-stakes conversations – where bots can sometimes represent as many as half the expected group – is sparking a fundamental question: When we opt to offload our work to AI, such as when we skip a meeting and read the AI-generated summary, what are we really losing? The convenience and attraction of cognitive offloading is undeniable. When we allow AI to take our place in thinking, it can both free up cognitive resources and speed up processing. However, this growing reliance on technology, especially given the widening productivity gap between those who use AI well and those who don’t, raises a critical concern: Are we sacrificing the “stuff of thought”—those unique cognitive processes that make human understanding and thinking rich and effective? In a recent article, published in the Harvard Business Review, David Rock presents evidence from a neuroscience perspective that argues for the key types of thinking that are crucial for deep learning and insight – all of which we risk losing if we overrely on AI. For example, defaulting to AI weakens the quality of our attention, diminishes spreading neural activation, and takes away our chances for insight—the very qualities that define good thinking. In our rush to embrace the speed of AI, we should learn to pause to reflect on what we’re handing over and find ways to preserve what makes us human—our attention, our deeper thought processes, and our moments of insight. The challenge isn’t whether to use AI, but how to use it wisely to amplify, not replace, our best thinking. Read the full article: “What’s Lost When We Work with AI, According to Neuroscience”, recently published in the Harvard Business Review to understand what we risk sacrificing in an AI-driven world and how to protect the “stuff of thought.”

By Chris Weller Following an insight-rich Day 1 of the 2025 NeuroLeadership Summit: Thrive Through Complexity, Day 2 explored further topics related to adapting to AI, developing smarter habits at scale, new data from NLI’s SCARF® Model, and much more. In many ways, this year’s Summit — NLI’s 20th conference in 18 years — reflects what’s always been true about leading well. The forces swirling around organizations may seem to change year to year, or even quarter to quarter, but the steadying factor has always been a focus on the brain. When leaders are unsure of where to anchor their intuitions, they can defer to the ancient machinery that guides our thoughts, feelings, and decisions. Below we’ve collected four big ideas from Day 2 to help you understand your own brain, and others’, more deeply, so your teams can thrive through complexity.   ‘Attention Density’ Forms Habits Faster Conventional wisdom says that habits take a long time to form, but research shows us a faster path to better behaviors. In the opening keynote session, “Toward a Real Science of Activation at Scale,” Emma Sarro, Ph.D., Senior Director of Research at NLI, explained how people can turn new behaviors into unconscious habits more quickly through a concept called “attention density.” “High attention density in the brain alters the neurochemical environment and strengthens the neural connections,” Dr. Sarro said. That is, when people are able to focus on certain information more deeply, and with greater frequency in a short period of time, the brain experiences stronger insights that spark new actions. This phenomenon takes place within a critical four-step process that NLI’s research has found can lead to the generation of new habits: a person moving from impasse (feeling stuck) to insight (an Aha moment) to action (conscious new behavior) to habit (automatic action). In this process, attention density lives between impasse and action.  When people can train the spotlight of their attention on a smaller number of items — and ideally, just one at a time — they can experience a strong insight around that object of focus and develop a new action right away. What normally takes people months can happen in weeks or days.  Culture Change Needs to Be Everyone-To-Everyone Imagine an organization of 10,000 people. Managers make up 10% of staff, or 1,000 people. If the organization wants to roll out a training program, they have two options: put 100 of those managers (1% of all employees) through an immersive workshop, or give all 1,000 a few light training videos, which few will probably watch. Whichever option they pick, neither scenario is very effective at shifting the culture among the full 10,000.  In the opening keynote session, panelists discussed a different way of changing culture: not a phased top-down model but a shared everyone-to-everyone model. NLI’s research and client work have found that habit activation at scale happens best when all 1,000 managers can go through the learning simultaneously and begin sharing it with the remaining 9,000 employees right away. “In terms of development, it’s not about a little content to a few people, but about fewer things to everyone, sharing with everyone else, all at once,” said NLI Co-Founder and CEO Dr. David Rock. Julie Loosbrock, CHRO of Blue Cross Blue Shield, shared how her 3,000-person organization has embraced habit activation at scale. An early priority was showing how the new habits could form in the flow of work, which aided the social learning component. Today, a few years after the rollout began, Loosbrock says materials live in a shared hub and any new training incorporates the “Why” of the science, which sparks deeper motivation among team leaders.   “The middle managers are embracing this consistently and pushing upward on their leaders to talk to them about what they’re learning,” Loosbrock said. “So the senior leaders think, ‘Uh oh, I better get going and get involved in some of this.’” Fairness and Autonomy Loom Large For People Much has changed in the world over the past 13 years, and the results of thousands of new SCARF® assessment responses bear out those changes. Between 2012 and 2025, NLI’s analysis of predominant SCARF® Model drivers shows a profound shift in what people find most valuable and motivating, from certainty and relatedness as the top two drivers to now autonomy and fairness ranking highest. Brigid Lynn, Ph.D., NLI’s Director of Research Design, explained that environmental factors have likely contributed to autonomy and fairness becoming more important for people. In particular, the shift reflects ongoing geopolitical and institutional instability, which shows up in the rise of fairness, and a post-pandemic climate of employee independence around hybrid and remote work, which may correlate with autonomy’s rise. At the same time, Dr. Lynn says prior drivers are now seen as boilerplate, perhaps due to shifting norms within organizations. “Certainty and relatedness transfer into baseline expectations for employees’ needs, no longer primary motivators for high-level engagement,” she said. People aren’t as rewarded by transparency or feeling a sense of belonging, in other words, because those qualities are now seen as non-negotiable compared to feelings of fairness and autonomy, which are still viewed as perks. According to Dr. Lynn, the findings should compel leaders to focus on sending more targeted reward signals around fairness and autonomy. While all SCARF® signals will make an impact for people, NLI’s research suggests these two will go a long way toward motivating employees the most.   AI Should Help Us Be (the Right Kind of) Curious According to Lisa Son, Ph.D., Professor of Psychology at Barnard College, an essential skill for using AI to get smarter, rather than reinforce laziness, is metacognition — or thinking about one’s thinking in order to deepen understanding.  In the afternoon keynote session, “Better Thinking with AI,” Dr. Son explained how there are two kinds of curiosity: explorative and exploitative. Explorative curiosity focuses on the why (e.g. “Why is the sky blue?”) while exploitative focuses on the what (e.g. “What year did the French Revolution start?”).

As artificial intelligence becomes embedded in our daily workflows, a critical question emerges: Are we just teaching people to use AI, or are we empowering them to think with AI? In a recent article, published in HRD Connect, NLI’s Rachel Cardero, Vice President of Consulting and Product, and Emma Sarro, Senior Director of Research, emphasize the importance of building an AI fluent workforce, and the cognitive foundation needed to get there. AI fluency isn’t a technical skill but a cognitive partnership— the ability to effectively partner with AI. However, creating fluency isn’t as easy as turning on a light switch. Cardero and Sarro emphasize that employees will be at different stages of a fluency continuum: from AI abstainers who avoid it and the AI-ambivalent who feel uncertain, to the AI-engaged who use it for basic tasks. The goal for organizations is an AI-fluent user, who does more than use AI – they partner with AI.  The first step towards AI-fluency is building the right cognitive foundation – fostering a set of core skills that will enable a workforce to embrace their future partnership with AI. This means creating a culture that embraces learning, practices adaptability, and understands the brain’s limitations by mitigating bias. By using these neuroscience-backed principles, leaders can move their teams beyond simple instruction and toward an intuitive, effective, and truly fluent partnership with AI. Read the full article in HRD Connect.