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 and David Rock According to Gallup, organizations could realize an extra 18% in profit and 14% in productivity by doubling the proportion of employees who feel that they have chances to learn and grow at work. Could AI be a possible solution for raising those numbers?  Picture a typical corporate learning program. A senior-level talent leader buys an online leadership course for 250 middle managers. The leader’s expectation is that people will go through the training, retain the information, and then apply the insights to their work so that team performance goes up over time. What’s wrong with this picture? For starters, the traditional learning experiences are slow, impersonal, and don’t really build new habits. In most digital experiences, whether synchronous or not, people quickly get bored, tune out, and few apply what they learn. As a result, organizations leave enormous amounts of unrealized potential and profits on the table.  To address the “bored and tuned-out” problem, many organizations try to deepen people’s focus by bringing them together in small groups, to experience a program in-person. While this has obvious benefits, scaling this to 250 managers is incredibly complex, expensive, and cumbersome, so firms end up back where they started, with something digital that in theory should work, but in practice does not deliver consistent results. Or they develop 25 of the 250, but not the rest. The clever use of artificial intelligence in any kind of learning experience can address each of these drawbacks: It can dramatically accelerate learning; it can personalize the learning to each individual learner; it can achieve far greater results with building habits, and it can scale across an infinite number of team members. The results can be transformative. In a matter of months, not years, organizations can see meaningful behavior change that drives performance and healthier bottom lines. The good news is, such an AI is no longer the stuff of thought experiments. It exists, and it can begin accelerating learning within your organization today. And it can do this not just for leadership development, but for any kind of human skills an organization may need. Meet NILES All-purpose generative AI (GenAI) chatbots have exploded in popularity over the past few years, most of them inspired by ChatGPT’s functionality as an “everything AI.” These AI partners may help with everyday requests for recipes or workout ideas, but when it comes to the highly specific nature of developing better leaders, generic AI chatbots can’t get the job done by just telling them what to do. Neither can generic coaching AI partners, which often rely on formulaic approaches that are only slightly less generic than everyday chatbots. Given the complexity of modern leadership, organizations need highly specialized, highly trained AI partners that understand leaders better than they understand themselves. The need is similar to other industries needing tailored AI partners: law firms needing legal-specific AI, or medical providers needing health-specific AI. That’s why we created NILES, the Neuro Intelligent Leadership Enhancing System, the world’s most intelligent natural-language AI coach designed to understand leaders, their thinking, and help them sort through common organizational issues — whether in structured sessions or directly in the flow of work. NILES is trained on 26 years of research on the neuroscience of leadership and nearly three decades of client work within Fortune 500 companies. As a result, NILES has a robust layer of “neurointelligence”: Observing a leader’s speech in real time, it understands how the leader thinks, the dilemmas they face, and then hypothesizes what the person’s brain is doing moment to moment, to help improve the person’s thinking. In other words, NILES doesn’t just respond with a seemingly helpful answer, as is the case with generic chatbots. It generates a level of metacognition, analyzing users’ speech patterns and content from their sessions together to make high-level determinations about underlying assumptions and hidden biases, and it can offer feedback to lead the person to insights they may have never reached on their own. In that way, NILES becomes a generalized learning accelerator. Simply by engaging with NILES for one-time advice or feedback, or more involved coaching or role play, learners go through the necessary pathway to get unstuck — moving from impasse, to insight, to action, and eventually to habit. NILES is designed to help any learner, facing any problem, make these critical four steps toward richer clarity, deeper motivation, and stronger performance. It’s especially effective for developing leaders at scale, but can be applied to any kind of human-centric issue. Lastly, NILES can be used as a standalone thought partner, or it can be woven throughout your existing learning program to enhance the systems already in place. This represents a new kind of AI innovation for facilitating culture change: While some organizations may want a standalone platform that employees can access as needed, NILES can also become a core part of the learning operating system, enhancing any training solution it’s embedded within.  Let’s now see how NILES can deliver the three core benefits of AI-supported learning — speed, personalization, and scalability — across a mix of scenarios.   Accelerate Everyone’s Learning Imagine a leader who wants to improve their presentation skills. Minutes before their next meeting, the leader asks NILES on their phone to “sit in” on the meeting and listen as the leader gives their presentation. Thirty minutes later, back at their desk, the leader asks NILES for feedback on how they did. NILES shares what seemed to go well, where the leader could improve, and concrete actions to practice and strategies to incorporate in their next presentation. Learning happened directly in the flow of work, without any disruptions to the other managers who have large teams to oversee. Or imagine a leader who’s nervous about giving a negative performance review to an employee they consider a friend. The day before the review, the leader begins a role-playing session with NILES to rehearse various strategies for delivering the tough news. The leader begins shakily, but

by Erin Wickham In an era defined by rapid technological shifts and increasing organizational complexity, leaders are asking the same question: How do we thrive through the complexity? This question was at the heart of the recent NeuroLeadership Institute Summit, where we explored the intersection of neuroscience, leadership, and the challenges organizations face. Chiefly, the need to understand how best to incorporate artificial intelligence (AI) into all aspects of work systems. The discussions moved beyond the hype of AI to uncover the real, actionable, science-based insights.  Here are the top five trends emerging from Summit that every leader and HR professional needs to know. 1. Metacognition is the Key to AI Fluency There is a growing fear that AI might make us lazy thinkers, but the data suggests there is nuance to this hot take. While an overwhelming number of studies indicate that using AI can lead to disengagement, a small percentage of users are seeing their performance skyrocket. Does this mean AI will act as a divider? Separating a few fluent users from the rest? The secret ingredient isn’t high IQ — it is metacognition, or the ability to think about your own thinking. To close the gap between basic users and fluent power users, organizations must cultivate specific cognitive habits that support AI integration, grounded in how we engage in metacognition. To help develop these skills, we introduced the AMPLIFY framework, which prioritizes: Humility: Ask “what do I know and not know”? Use curiosity, self-reflection, and a growth mindset to explore. Flexibility: Stretch cognitive flexibility and agility, and ask yourself, “what are all the ways of seeing this issue?” Vigilance: Challenge your assumptions and use mitigation strategies for cognitive bias to drive for excellence.  2. AI Agents Are in the Org We are rapidly approaching a reality where AI agents are not just tools, but recognized members of the team. Imagine an AI agent (like NLI’s NILES), capable of listening to meetings, offering feedback on inclusion, or coaching managers through difficult conversations. This shift allows Talent and L&D teams to scale their impact exponentially. A team of 10 people can move from supporting the top 100 executives to supporting 1,000+ managers by leveraging AI agents to handle the “heavy lifting” of daily coaching and support. 3. The Great SCARF® Shift: From Certainty to Fairness For over a decade, the SCARF® Model has helped leaders understand what drives us toward reward and away from threat. However, our new research, analyzing over 15,000 data points, shows a dramatic shift in what employees value most. In 2012, the top drivers were Certainty and Relatedness. Today, those have dropped in priority. The new top drivers for the modern workforce are Fairness and Autonomy. Why the shift? After years of global uncertainty, employees may have become desensitized to it. Meanwhile, the changing nature of remote or hybrid work has made equity and the ability to control one’s own work (Autonomy) essential. 4. Attention Density is the Key to Habit Formation, at Scale Organizations often rely on 30-day or 90-day programs to change behavior, but neuroscience tells us that time is less important than attention density. Attention density is the combination of the intensity and frequency of focus. High attention density can support habit formation in as little as 24 to 48 hours if the focus is strong enough. Moving forward, successful organizations will move away from vague “change management” and toward “habit activation,” using metrics like our Behavior Change Percentage (BCP) to measure exactly how effectively new habits are sticking across the population. 5. Neuro-Education as the key to Resilience  Finally, we learned that resilience is not a fixed trait you are born with — it is a skill you can build. Recent research highlights that simply understanding how the brain works acts as a clinical intervention. So, when leaders understand the mechanics of their own emotions and cognitive capacity, they can better regulate their responses, turning down negative emotional states and turning up positive ones. In a complex world, “neuro-education” is no longer a nice-to-have; it is a requisite for mental resilience. The future of work isn’t just about adopting new technology; it’s about upgrading our internal operating systems. Whether it is prioritizing Fairness over Certainty, or building the muscles of metacognition to think better with AI, the organizations that thrive will be the ones that understand the science of the humans running them.

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?”).

by Chris Weller Today marks Day 1 of the 2025 NeuroLeadership Summit: Thrive Through Complexity, a two-day event dedicated to a theme that so many leaders and organizations have been facing through geopolitical upheaval and AI-fueled technological change.  This year’s Summit marks the 20th conference since the first event was held on a hillside in Asolo, Italy in 2007. A lot about the working world has changed since then, but what has stayed the same is the need for brain-friendly leadership that supports how people think, feel, and behave in their jobs.  As leaders grapple with accelerating change, below we’ve captured three big insights from Day 1 that can help everyone feel equipped and empowered to navigate change successfully. Secure your spot at Day 2 of the 2025 NeuroLeadership Summit: Thrive Through Complexity An AI-Assisted Workforce Elevates Human Skills   In the opening keynote session, “Thrive Through Complexity,” panelists reflected on the challenges brought by AI, shifting global politics, and natural disasters. A key takeaway from the discussion was that organizations hoping to use AI to improve productivity must double down on the human element. Dr. David Rock, Co-Founder and CEO of NLI, introduced the continuum of AI fluency, from resistors in Zone 1, to the ambivalent in Zone 2, to general users in Zone 3, and the AI fluent in Zone 4. It’s between Zone 3 and Zone 4 where most organizations waste their investments in AI, Dr. Rock said, because the general users use AI to replace their thinking, which makes them uncritical thinkers, but the fluent users use it to enhance their thinking. The goal, Dr. Rock said, is to get all employees to fluency.   Lindsay-Rae McIntyre, Chief Diversity Officer at Microsoft, said the way to bring people along requires a reinforcement of what makes people feel included at work. “For leaders, people have always wanted to feel heard, seen, and valued at their core, and all humans want to be supported,” she said. “So customized leadership, the ability to be a high performance coach, is super critical.” AI provides this opportunity to customize leadership development beyond typical coaching. That’s also why NLI created NILES, the Neuro Intelligent Leadership Enhancing System, a natural-language AI coach trained on decades of NLI’s research and practice. NILES helps leaders go from feeling stuck to experiencing insight, which motivates them to take action and build new habits.   NILES even made an appearance in the opening keynote, reflecting on a story told by Steve Powell, CEO of Southern California Edison, the largest utilities provider in Southern California. NILES offered the insight that psychological safety is critical for performing under pressure because it allows teams to hold together rather than fall apart — an observation that itself reflects how an AI like NILES can support, rather than replace, human cognition.   Resilience Isn’t a Personality Trait — It Can Be Trained When life (or work) gets hard, how do most people respond? As Lyndsay-Rae McIntyre noted in the opening keynote session, “brains that are hot don’t perform well.” Stress leads to more stress, which causes emotional, irrational thinking and hampers clear-headed rational thought.  Fortunately, University of California, Irvine, neuroscientist Dr. Golnaz Tabibnia, Ph.D., shared that how we respond to stress can be changed through active effort. Resilience, she said, isn’t a personality trait — rather, it’s a skill that anyone can learn. The trick is developing coping mechanisms that cause “coping neurons” to fire at the same time as our stress response, reducing our sense of threat while raising our feelings of reward. For instance, employees going through organizational change can acknowledge the discomfort, reappraise it as a chance to learn something new, and practice “transcendent” techniques such as mindfulness, going for a walk, and creating psychological distance from the stress that all combine to help them re-engage their prefrontal cortex and feel more in control of their situation.    “Stress might beget more stress if we passively ignore it,” Dr. Tabibnia said, “but it can also make us stronger if we decide to do something about it.” If AI Isn’t Making You Smarter, You’re Using It Wrong In the Day 1 afternoon keynote session, on the future of AI in talent management, UCLA cognitive neuroscientist Matt Lieberman, Ph.D., observed that leaders’ primary fear with AI is that in the long run, they’ll end up with people who just aren’t as interested in being engaged and informed. With AI at their fingertips, employees will surrender their intelligence and critical thinking. For this reason, Dr. Lieberman says that AI’s chief role at work must be to make people smarter. NLI’s own research suggests that organizations only benefit from AI when people become fluent users, not passive users hoping to offload everyday tasks.  Brian Kropp, VP of Global Insights at executive search firm Heidrick & Struggles, added to the point, saying AI must be woven into an overarching talent strategy. It can’t be a gimmick technology in search of a problem. “For companies that are able to identify major positive breakthroughs that drive revenue, it’s not going to be around improving individual task performance with AI,” he said. “It’s going to be around increasing collaboration.”  NLI’s own efforts to develop NILES began with this intent to support organizations and their existing talent management. Increasingly, the future of AI looks like sophisticated platforms embedded into recruiting, hiring, onboarding, coaching, development, promotions, and retirement planning. But be careful, Kropp says, because organizations need to be intentional about who benefits from these returns. Will it be only the shareholders, or will employees have a stake in the upside? “If you don’t have an answer, it will default to shareholders and make employees and leaders less willing to experiment and try new things,” Kropp said. In other words, to preserve employees’ growth mindset around AI, leaders may want to take employees’ expectations into account if their AI talent strategies produce the best-case scenario. If AI creates massive upside, who deserves to benefit? What’s most fair given the work involved, and what

Artificial intelligence is rapidly becoming a fixture in the modern workplace, promising unprecedented efficiency and innovation. But as we delegate more routine tasks to algorithms, we face a critical question: Will genAI elevate our thinking and more deeply engage our key brain networks, or will it encourage us to default to cognitive shortcuts, effectively dampening our cognitive output? At NLI, we believe that as AI takes hold in organizations, the most effective teams will need to become more, not less, human in how they operate. This means doubling down on the unique skillsets of the human brain—our capacity for deep thought, critical thinking, creativity, and metacognition, or the awareness and regulation of one’s cognitive processes. AI can be a powerful partner, but only if we’re the ones making the decisions. Using it irresponsibly has the potential to cost your organization thousands, if not millions, in flawed strategies and missed opportunities. “Use It or Lose It”  Ensuring AI is a tool for better thinking begins by understanding what better thinking actually means for the brain. Key here is the principle of neuroplasticity— the brain’s ability to reorganize itself by forming new neural connections. The cognitive skills we engage in are the ones that strengthen, the ones we outsource, weaken. And as research begins to emerge from the academic labs, we can see early signs of this occurring when we rely on genAI. Studies show that improper use of generative AI has a dulling effect on our cognition. One landmark study, in particular, showed that when individuals used chatGPT for essay writing, over 80% of users remembered nothing of a topic they wrote about when they used the tool, compared to just 11% of those that wrote the essay without support. Furthermore, using GenAI as a replacement for critical thinking has been shown to produce generic, homogenous ideas – reduced variation in the ideas generated. Finally, studies have linked relying on genAI to eventual boredom and a reduction of intrinsic drive towards the work. Multiplied across an entire organization, poor GenAI habits risk coalescing into cultures of mediocrity and boredom. These studies also indicate the potential for a longer-term negative impact on brain structure itself, a neural atrophy often summarized by the neuroplasticity principle of “use it or lose it.” However, the solution isn’t to abandon AI, but to build out the skills needed to use it effectively. Here are three brain-based ways to ensure your team is using AI to enable, not inhibit, their thinking. Instill a culture of metacognitive thinking  Metacognition is the skill of stepping outside your own thought process to examine it – having an awareness and regulation of our own cognitive processes. When we engage in metacognition, we intentionally activate our prefrontal cortex, the brain’s executive center responsible for planning and self-awareness. Tip: Before turning to AI, consider why you’re facing a particular challenge, how you’ve tried to solve it in the past, and how you’ll think about any AI-generated output. Foster the habits of critical thinking While genAI models can process vast amounts of data, they don’t possess true understanding and self-regulation and motivation to solve the problem at hand. This requires critical thinking, an intrinsically human skill. People that think critically are motivated to find, reflect on and evaluate information from diverse sources in order to solve new problems, reach goals and learn. Leaders can help to foster these habits by both role modeling them and maintaining environments that are both psychologically safe and remain oriented towards a growth mindset. Tip: Treat AI outputs as a first draft, never a final product.  Hold space for creative moments. In order to maintain our ability to be creative, we may need to schedule time for it. That’s because creativity is enabled when your mind wanders. This allows the engagement of the brain’s creative networks, such as the default mode network. And while AI can serve as a useful tool to kickstart the creative process by generating initial concepts, it’s important to recognize that while these ideas can be helpful starting points, they may not be truly groundbreaking.  Tip: Structure your creative process into two distinct phases. First, use AI to generate a wide array of starting points. Then, walk away and allow your mind to wander. The rise of AI doesn’t diminish the need for human intelligence; it amplifies it. The critical question of how to use AI for better thinking is a central focus of the 2025 NeuroLeadership Institute Summit, Thrive Through Complexity.  In our final keynote session of the event, Using AI Toward Better Thinking, we’ll explore what better thinking actually means for the brain and how leaders can build the habits to use AI for this purpose. The path forward is clear: we must seize the opportunity to become more thoughtful, more creative, and more intentionally human. Register today for the 2025 NeuroLeadership Institute Summit: Thrive Through Complexity.

By Sinead Kinney, PHR and Rebecca Norberg, SHRM-CP It seems like everyone is talking about AI in hiring. You’ve seen the stats: nearly 70% of companies are expected to use AI in recruitment by the end of 2025. Why the massive AI adoption? Simple: AI solves fundamental human limitations in recruiting, especially when dealing with high-volume applications. But here’s the secret: merely using AI won’t guarantee you’ll get the right candidates. Are you even sure you’re sourcing the right candidates? In 2023, iTutor was on the hook for hundreds of thousands of dollars for age discrimination because their AI tool automatically rejected over 200 qualified women over 55 and men over 60.   To truly nail talent acquisition, you need a smart strategy that pairs AI’s muscle with disciplined human oversight and proven methods for checking our own unconscious biases. Think of it as a dynamic duo: AI handles the heavy lifting, streamlining and automating tasks at speed, while acting as a thought partner for the human who knows how to wield its power and verify its accuracy. In turn this frees up precious time and energy for tasks requiring human thought, creativity, diligence and experience. The pair? Unstoppable. Separate? Flawed and fallible. Talent acquisition on AI autopilot  Right now, most organizations are using AI to automate and sort through applications, cover letters, emails, and job boards all while thinking they are finding qualified candidates, making data-driven decisions, and managing massive applicant pools. AI does an amazing job during these first levels of the funnel, but still requires a human to review and make decisions.  Zeroing in on top talent AI tools are fantastic at cutting through the noise. AI-driven sourcing and matching systems can instantly scan hundreds of millions of professional profiles, analyzing job requirements against skills, qualifications, and experience. Your recruiting team doesn’t have to waste time manually scouring job boards; instead, they get a ranked list of top candidates prioritized by how well they match the must-have and nice-to-have criteria. This frees them up to focus on what truly drives success: communicating and building relationships with those candidates. Boosting quality and catching misrepresentation AI doesn’t just speed things up; it improves the quality of the candidates who move forward. Research shows that the use of an AI-assisted pipeline resulted in a 20% increase in the pass rate for final-stage human interviews, proving AI identifies higher-quality talent. Even better, AI helps detect the widespread problem of skill inflation (or lying on a resume).  In 2025, BetterHealth found that 78% of job seekers have considered misrepresenting their resumes, and Business News Daily reported that  60% have admitted to actually lying about their proficiency in skills they did not have. By implementing conversational AI assessments organizations can identify the roughly one in five candidates who claim technical skills they cannot actually demonstrate.  Data-driven decisions Forget guessing. Predictive analytics is the ultimate tool for evidence-based hiring. It analyzes patterns from your most successful current employees to forecast future needs and pinpoint characteristics correlated with high performance. This strategic approach provides objective data points that complement human evaluation, moving beyond intuition or gut feelings. Plus, by focusing on skills and objective indicators, AI helps mitigate unconscious bias and aids in increasing diversity by widening the talent pool.  With 70% of candidates using AI to apply for jobs (for everything from matching their resumes to skill requirements on job postings, completing applications, to even answering questions during interviews), organizations need to be smarter, faster in order to stay ahead. Breaking the cycle of bias, the first step is awareness  In AI, bias is defined as a systematic error in judgements, which can develop in AI systems due to the humans inputting and training the algorithm. Then as humans and AI continue to interact without this understanding, they create an echo chamber, which can magnify their biases. Our brains are hardwired with unconscious biases, the NeuroLeadership Institute (NLI)’s SEEDS Model® refers to these as Similarity, Expedience, Experience, Distance, and Safety biases. Similarity Bias – our brain’s preference for people who are similar to us or share common goals, like opting for a candidate that graduated from our alma mater. Expedience Bias – our brain’s way to conserve energy by relying on mental shortcuts based on intuition or limited information, like discarding resumes missing a college degree when it may not be a bona fide occupational requirement. Experience Bias – our brain’s assumption that our perceptions are an accurate, objective representation of reality, like restricting your talent pool to those who have worked within a particular industry. Distance Bias – our brain’s natural tendency to overvalue things that are near in physical proximity, time, or responsibility, like preference for the most recent candidate you interviewed even if they weren’t the most qualified. Safety Bias – our brain’s way of protecting against the unknown which is viewed as inherently riskier than the known, like not choosing to make an offer to a candidate because they “aren’t a culture fit.” Now that we can recognize the cycle of bias, we can partner with AI leveraging each other’s strengths, while mitigating our weaknesses.  Let’s explore what happens when we combine this awareness, The Neuroscience of Better Hiring® (NLI’s SELECT product), and AI’s processing power. The human touch—mastering bias mitigation to flip the script on AI in talent acquisition AI may handle the data, but ultimately humans make the final decision. That means we must actively guard against the biases hardwired into our brains. This is where NLI’s SELECT core habits come into play: Embrace the evidence, Follow a process, Challenge your thinking. Anchoring on the SEEDS model, they help recruiters and hiring managers interrupt and redirect thinking by breaking bias mitigation into habits to make better decisions. These can be super charged with the help of NILES, our Neuro Intelligent Enhancing System. NILES isn’t your standard GenAI. With expertise from over 200 PhD scientists and subject matter experts, and nearly three decades of business practice with Fortune 100

By Dr. David Rock Offers for digital, AI-powered coaching are starting to pop up in the inboxes of HR leaders everywhere, with lots of promises of cost savings for coaching. But how do you tell real from vaporware? Let’s admit this is a bit of a brave new world, sometimes exciting and sometimes terrifying. Even more so if you’re a decision-maker in an organization who’s expected to find a coaching AI that’s “best in show” for your organization and that’s going to “talk” to huge numbers of important people in your firm. This needs to be done responsibly, and not just be based on whizz bang tech, or how nice the salesperson was to you. There’s a certain “fit for purpose” issue to be clear about here. If you just want a digital coach to help people set goals and turn them into steps, there are dozens of options to choose from, many of them now quite low cost. However, the true value of coaching is not just goal setting — it’s making smart people smarter, efficiently. Doing this requires a more sophisticated tool than just a goal-setting coach.  The question here is, how do we measure this? How, as a responsible purchaser of AI, can you know you are getting the best possible solution available? The answer should not just be about the technical bells and whistles. In a short time, all AI coaches will be able to be integrated into any platform, have great dashboards, speak all languages, and be compliant with all the technology needs of your firm.  We believe there is one truly responsible way to differentiate the options. That is to do what we all do with other new technologies: speed test them. Only in this case, instead of actual processing speed, the test is to see how many people find true value in their coaching conversations with their AI coach. Not just that they liked the conversation, but that it significantly improved their situation or helped them solve their core challenge. We’re ready to throw down the gauntlet: We believe we have the smartest, most effective AI for managers and leaders, because we’ve been working on this challenge not for two years with a team of fresh-faced engineers, but for 26 years with a massive global community of cognitive scientists and business leaders. Our tech is not necessarily better or worse than anyone else’s, but the effectiveness of our coaching conversations is off the charts.  How do we know? Our AI is creating breakthrough insights that drive real change, in four out of five conversations, and faster, making it more likely for people to take action and in less time. Don’t believe us? Put us to the test. Book a demo of NILES to see it in action.  After all, if you’re going to give hundreds of thousands of managers someone to talk to, you should want those conversations to be as useful as humanly, or in this case digitally, possible.