Climbing the High Summits: Why Every Leader Must Master Human Skills to Get the Most Out of AI

Diane Belcher

November 10, 2025

At Machu Picchu’s Sun Gate, a clear view of the citadel can vanish in minutes. Skies that seem calm turn quickly into downpours, leaving the path slick with rain and the descent treacherous. Those prepared for the unpredictable weather are glad to have their rain jackets, but gear alone is not enough. What makes the difference is the ability to adapt and stay resilient as conditions change.

Today’s organizations are climbing into their own unpredictable conditions, an era of relentless disruption, technological advances, data security threats, volatile markets, and geopolitical risk. Within view is an unprecedented capability to reimagine strategy, accelerate performance, and unlock value at scale and speed. But reaching the summit requires something more than high-tech gear.

It requires every member of the organization—from the CEO and C-suite to managers, frontline teams, and technical experts—to master the complementary human strengths that no machine can replace. In the face of unexpected turns, humans bring a kind of adaptability, judgment, and creativity that technology can’t yet match. And it’s these capabilities that make the difference between stalling short of the peak and reaching it.

The Gear Is Critical, but It’s Not the Guide

Artificial intelligence (AI) is the modern expedition’s gear: precise, powerful, and more functional than anyone could have imagined only a few years ago. But the gear is not the guide.

The guide’s role is to read the mountain, adjust the route to conditions, set the pace, make safety-critical decisions, and ensure the team’s resources, skills, and morale are all there. Teamwork and resilience make all the difference, just as in business. Rapid, continuous change exhausts even very capable workforces. Leading through it takes leaders with strong social and emotional intelligence, the ability to create psychological safety, and a genuine interest in people’s well-being.

The most successful AI adoption comes from a distributed leadership model. The CEO sets the tone and embeds AI into the business strategy, but the chief information officer, chief operating officer, functional heads, and line managers must all take responsibility for integrating AI into workflows, decision making, and customer experiences. Without that shared commitment, AI doesn’t get scaled to its full potential.

That’s why AI literacy for everyone matters too. Ensuring that every team member understands both AI’s capabilities and its blind spots helps them know when to trust the model and when to trust their instincts. In a truly AI-enabled organization, frontline employees aren’t just end users. Instead, they’re active contributors who spot risks, surface opportunities, and feed insights back into the system.

Reading the Signs Machines Can Miss

Even in clear weather, strong leaders question assumptions, reassess the plan, and prepare alternatives. They look for hazards the map can’t show and act before those hazards become crises. When crises do occur, they size up the problem with a sense of proportion and draw on their creativity to improvise solutions when necessary.

Just as in business, leaders must cultivate integrative thinking, which is the ability to hold competing perspectives, connect dots across functions, and generate new paths forward. As research from Harvard Business School has shown, the strongest creative ideas often emerge when humans and machines work together, combining human originality with AI’s ability to refine ideas and test their feasibility. This is what turns AI potential into transformative capabilities.

The Partnership That Gets You to the Top…and Back Home

The most successful digital transformation strategies rely on constant coordination between people and technology. Despite detailed plans, it’s the team who decides when to deviate to avoid danger, preserve energy, or seize an unexpected break in the weather.

For high-performing organizations, the C-suite, product leads, operations managers, legal teams, human resources departments, engineers, customer-facing teams, analysts, and even administrative staff learn to collaborate with AI tools in ways that elevate both their work and the organization’s overall performance.

Leading at Extreme Altitude

The companies that succeed won’t just be the ones with the most advanced AI tools. They’ll be the ones that have deliberately elevated the human capabilities that give those tools purpose and given everyone a role in finding new ways forward.

They will:

• Enhance human strengths, developing emotional intelligence, ethical judgment, resilience, creativity, and integrative thinking in every role.
• Build widespread AI literacy, so every employee can partner effectively with AI.
• Share ownership of creating the organization’s future, engaging the leadership team and broader workforce in seeking ideas to leverage AI, not isolating it within a single function.

Reaching the summit involves building a digitally literate workforce, whose human capabilities have also been sharpened. When leaders at every level champion these complementary elements, the organization doesn’t just climb higher, it becomes more capable of navigating whatever terrain lies ahead.

The post Climbing the High Summits: Why Every Leader Must Master Human Skills to Get the Most Out of AI appeared first on Harvard Business Impact.

Full-Immersion Learning: Building Confident and Capable Leaders

Abbey Lewis

October 13, 2025

When you land in a new city, a familiar challenge emerges—how do you see, taste, and experience the most in the little time you have?

For every city, there are hundreds of guides and itineraries, each promising the “best” way to explore. Following one offers structure but also keeps you on rails, walking someone else’s path rather than discovering your own. The experience offers an easier way to explore, but it falls short of being truly transformative.

The alternative is immersion: stepping into the streets, wandering without a script, and experiencing the city in its true state. In those unplanned moments—trying local food, navigating side streets, asking strangers for directions, you begin to understand the city as it really is.

Leadership development is no different. Leaders must master new skills faster than ever. Yet they identify lack of time as the single greatest barrier to mastery. To make the most of scarce time, organizations must shift from traditional learning methods to full-immersion learning.

Full-immersion learning places leaders in business-relevant contexts such as simulations, role play, or real-world challenges where they apply knowledge in real time. It is experiential, contextual, and designed for speed, engagement, retention, and confidence. Among the most effective forms are practice-first learning and action learning projects, which demonstrate how immersion accelerates both skill and impact.

Practice-First Learning: Learning by Doing

Arriving in a new city can feel overwhelming. There are countless streets to explore and hundreds of foods to try. Reading a guidebook may help, but you won’t know which meals you love until you taste them. By exploring first, you discover the city through your own senses—and then deepen your understanding with research.

Practice-first learning creates the same effect in business. Instead of starting with abstract instruction, employees engage directly with real work challenges, experiment, and learn by doing. They recognize their own gaps as they encounter obstacles, then reinforce their knowledge with the applicable research-based concepts. The results are higher engagement, faster skill development, and greater confidence—addressing the issues of low engagement in traditional learning.

Action Learning Projects: Building Skills While Driving Results

When you travel with a group, everyone has different priorities. One person hunts for the best food, another searches for history, and someone else wants art and culture. Without alignment, the group risks a fragmented journey.

Leaders and their teams face the same challenge. Action learning projects solve it by anchoring development in real business issues. Each member brings a different lens, but together they apply knowledge, solve problems, and drive outcomes that matter. The learning is immediate, motivating, and efficient. Skills grow at the same pace as results.

Research confirms this; immersive, contextual, team-based learning doesn’t just accelerate development, it also fuels innovation.

From Wrong Turns to Real-Time Feedback

Even in the best-designed learning experiences, leaders need feedback to know where they stand. Without it, they risk repeating mistakes or overlooking gaps. It’s much like exploring a new city; you may order a dish that looks appealing but doesn’t match your taste. In the process, you learn something new about your preferences.

One of the most persistent challenges in leadership development is identifying and addressing individual skill gaps. Artificial intelligence (AI) now provides the real-time feedback loop leaders have long been missing. By analyzing performance, it not only highlights the gaps but also delivers feedback that is immediate and contextual. Paired with immersive learning methods, AI accelerates the cycle of practice, feedback, and adjustment, enabling leaders to close gaps faster and with greater confidence.

Bottom Line

Travel reminds us that the richest experiences often come when we choose to immerse ourselves in our environment. The meals you remember, the neighborhoods you love, and the insights you carry home come from stepping off the itinerary and plunging into the life of the city.

Immersive learning delivers the same depth in the workplace. When leaders learn in real contexts, it accelerates skill development, fuels engagement, and builds confidence. Leaders are better equipped to apply new skills in ways that spark innovation.

As Harvard Business School’s Ranjay Gulati argues, now is the moment for organizations to lead with courage. By diving into learning head-on and embracing risk, they build leaders with the practical skills to thrive in disruption.

At Harvard Business Impact, we deliver innovative and immersive learning through HBR Spark, combining world-class content, AI-driven personalization, and hands-on experiences such as leadership labs to accelerate growth and performance.  In our blended learning experiences, leaders engage in immersive learning through simulations, business impact projects, and other high-touch methods that connect your business challenges directly and drive meaningful impact.

Full-immersion learning is not just about acquiring new skills—it’s also about transforming leaders into the innovators of the future.

The post Full-Immersion Learning: Building Confident and Capable Leaders appeared first on Harvard Business Impact.

Reinforcing Organizational Bridges: Four Elements That Strengthen Midlevel Leaders

Jeff Pacheco

October 1, 2025

Bridges don’t collapse overnight—they weaken in silence. Once-impassible valleys and rivers are crossed without a thought, carried by structures so reliable we forget what it took to build them. Yet, every bridge demands vision, resources, precise engineering, and ongoing maintenance. For a bridge to endure, every part must work together. If one part falters, the integrity of the whole bridge is threatened.

As we discussed in a previous perspective paper, midlevel leaders are those bridges—spanning the gap between strategy and execution, linking senior leadership’s vision to daily realities. They carry the weight of transformation, unite teams, and keep the structure intact under pressure. But like any bridge, their strength depends on deliberate construction, reinforcement, and support.

Four Elements That Support the Success of Midlevel Leaders

Midlevel leaders are operating under immense pressure. They are expected to deliver results, lead transformation, and keep teams engaged—all while navigating shifting priorities and constant change. When the structural supports they rely on are missing, that pressure strains their capacity to perform. Agility erodes, execution suffers, and burnout accelerates, putting both short-term performance and long-term transformation at risk.

Our research at Harvard Business Impact Enterprise identifies four structural elements essential to midlevel leader strength: autonomy, empowerment, psychological safety, and recognition. Each is as vital as any beam or cable in a bridge—remove one and the entire structure is at risk.

• Autonomy: Autonomy enables midlevel leaders to act decisively, adapt quickly, and drive innovation. Entrusting them with meaningful decision making strengthens the critical link between strategy and execution. In our research, midlevel leaders who reported having autonomy showed a nearly one-third increase—rising to 62%—in effectiveness at demonstrating agility and adaptability in fast-changing environments.
• Empowerment: Empowerment means giving midlevel leaders the resources, authority, and confidence to act. This requires intentional effort from senior leaders—investing in training, fostering clear communication, and including midlevel leaders in strategic decision making. The payoff is clear: Empowered midlevel leaders are stronger at supporting transformation and better equipped to influence, execute, and sustain momentum.
• Psychological safety: Harvard Business School’s Amy Edmondson defines psychological safety as a shared belief that it’s safe to take risks and express ideas without fear of negative consequences. It grows from clear, predictable, and fair expectations paired with open communication. In our research, nearly seven in 10 midlevel leaders who felt psychologically safe reported meeting goals and expectations—compared to just 43% of those who didn’t. This freedom fuels engagement, sparks experimentation, generates new ideas, and fosters a culture of curiosity and smart risk taking.
• Recognition: Recognition keeps midlevel leaders grounded in purpose and value. When their contributions are acknowledged consistently, it reinforces their commitment and resilience under pressure. Our research showed the difference is measurable—weekly burnout rates dropped from 80% to 66% when midlevel leaders felt recognized by senior leadership. Recognition isn’t a courtesy; it’s a stabilizing force that sustains engagement, strengthens commitment, and helps leaders perform at their best even in challenging conditions.

Like engineering a bridge, each structural element has its own value and measurable impact. But true strength comes when every part works in unison. For midlevel leaders, it’s the combination of these supports that enables them to perform at their best, sustain momentum, and lead the organization forward under any conditions.

Inspecting the Bridge: Stress Testing Your Midlevel Leadership

Even the most impressive bridge must prove its strength before being opened for use. Engineers test every joint, cable, and beam to confirm they meet standards, can bear the bridge’s load, and will endure. Organizations must do the same with their midlevel leadership.

An initial “inspection” means systematically evaluating their performance against the four structural elements and, just as critically, assessing how well the organization supports them. Measurement reveals strengths, exposes stress points, and directs resources where they have the most impact.

But inspections aren’t a one-and-done exercise and often surface evolving needs. In our study, nearly 60% of midlevel leaders pointed to three areas they needed more support in for their success: greater decision-making authority, stronger work-life balance, and more efficient technologies. Addressing these requires more than periodic check-ins; it also calls for continuous feedback loops. Helen Tupper and Sarah Ellis call this making feedback a team habit by embedding smart intentional questions into meetings to solicit a steady stream of insights from midlevel leaders.

Build the Bridges That Carry Your Business Forward

A bridge is only as strong as the investment in its design, construction, and upkeep. The same is true for midlevel leaders. Strengthening their autonomy, empowerment, psychological safety, and recognition produces higher engagement, lower burnout, and greater innovation. These leaders become catalysts—driving transformation, connecting strategy to execution, and sustaining momentum through change.

Neglect midlevel leaders and cracks will appear under pressure. With consistent investment, midlevel leaders become the reliable, resilient structures that carry an organization from where it is today to where it must go tomorrow. The future of your organization depends on the bridges you build now.

The post Reinforcing Organizational Bridges: Four Elements That Strengthen Midlevel Leaders appeared first on Harvard Business Impact.

Amplifying with AI: L&D’s Role in Scaling Collective Intelligence

Mark Marone, PhD

September 9, 2025

Artificial intelligence (AI) is poised to automate anything that can be measured, revolutionizing how work gets done. Even if AI innovation stalled today, the disruption would continue. That gives learning and development (L&D) leaders two urgent tasks: to help people use AI effectively and to use AI to enhance how people learn.

Business today demands learning that is faster, more personalized, and deeply contextualized. That’s where AI comes in. In a recent Harvard Business Review article by Marc Zao-Sanders that methodically ranks 100 current use cases for generative AI, both “enhanced learning” and “personalized learning” feature among the top 20.

This work of L&D today is critical. By combining AI’s capacity to scale insight with L&D’s ability to shape behavior, organizations can build their collective intelligence: the dynamic interplay between people and machines that enables smarter decisions, innovation, and better performance at scale.

The Amplification Imperative

According to Harvard Business Impact’s 2025 Global Leadership Development Study, 49% of L&D leaders expect AI to improve talent development outcomes this year. Even more expect it to enhance the scalability (50%) and adaptability (53%) of learning programs.

That promise is already being realized. Consider how Hilton Hotels rolled out an AI-powered virtual reality training program for front desk staff. Employees interact with a Guest Service Coach that delivers real-time feedback on tone, word choice, and service behaviors. What used to take four hours of instructor-led training now takes just 20 minutes, and the program has scaled to over 400,000 employees globally.

This kind of amplification is exactly what many organizations need, but speed and efficiency aren’t enough. The deeper value lies in AI’s ability to help organizations codify and share internal expertise, personalize development pathways, and create learning systems that adapt alongside the business and help it grow.

Three Ways AI is Already Amplifying Learning

1. Contextualized Knowledge at Scale

AI tools powered by internal data are helping organizations unlock and distribute tacit knowledge. For example, large language models can be trained on internal policies, playbooks, and best practices, enabling employees to ask context-specific questions and receive curated answers grounded in the organization’s way of working.

A multinational firm interviewed in our study represents a typical example. It deployed an AI coach that understands company values, ethical guidelines, and leadership principles, then delivers tailored coaching to first-time managers. This kind of amplification by AI is allowing organizations to streamline the work of middle managers and flatten organizational hierarchies.

2. Personalized and Proactive Learning

In contrast to traditional training calendars, AI-powered systems can push microlearning or feedback precisely when and where it’s needed. Leaders can receive just-in-time nudges before key meetings. Teams can be prompted to reflect on recent challenges. Learners can navigate personalized development journeys based on evolving role requirements, skill gaps, and performance trends.

3. Learning Embedded in Workflows

The best learning doesn’t feel like training at all. AI makes it possible to integrate development directly into the flow of work, offering real-time guidance, simulations, and decision aids. Instead of stepping away to learn, employees learn as they work. This not only increases relevance and retention but also addresses one of the biggest barriers to learning: lack of time. Instead of logging in to a portal and searching for content, employees increasingly engage with intelligent assistants that deliver curated answers, personalized learning, and targeted support just when it’s needed.

Why Learning Needs to Lead

Right now, organizations need L&D as a strategic partner in developing collective intelligence to unlock the full potential of human-AI collaboration. Yet our study revealed an uncomfortable gap: only 36% of organizations believe their leaders fully embrace the mindset that AI must be central to strategy and operations. Just 42% describe their support for employee AI experimentation as strong.

Learning leaders have a critical role to play in helping to close these gaps. This includes not only helping leaders and employees become AI literate themselves, but also leading by example, incorporating AI into how learning is developed, delivered, and measured.

The most effective strategies blend AI’s precision with human insight, creating a loop where machine-generated guidance is continuously refined by people and returned to the system as collective intelligence. In this way, AI doesn’t just accelerate learning; it becomes part of a feedback loop that strengthens it.

The Learning Function as the Leverage Point

When business models, company workflows, and entire industries are being reshaped by intelligent machines, the ability to learn at scale becomes a competitive differentiator.

Amplifying learning with AI promises to increase the velocity and impact of learning across organizations. The challenge for learning leaders today isn’t whether to use AI, it’s how to use it well: ethically, strategically, and in the service of human growth as well as business growth.

With the guidance of talented L&D teams, AI can enable not just more learning, but better learning: learning that equips people to lead, adapt, and thrive in a fast, fluid, and future-focused world.

The post Amplifying with AI: L&D’s Role in Scaling Collective Intelligence appeared first on Harvard Business Impact.

Scale Innovation with Speed: The ABCs of Leading Innovation

Shruti Patel

August 21, 2025

In an era of constant disruption and complexity, innovation isn’t just a competitive edge, it’s a leadership imperative. That was the core message from Linda A. Hill, Wallace Brett Donham Professor of Business Administration at the Harvard Business School, during her powerful keynote at the Harvard Business Impact’s 2025 Partners’ Meeting.

Drawing from her upcoming book Genius at Scale: How Great Leaders Drive Innovation, Professor Hill challenged traditional notions of innovation as the responsibility of Research and Development (R&D) or a handful of creative thinkers. Instead, she framed innovation as a collective, organization-wide capability. One that can only thrive when leaders are equipped to foster collaboration, experimentation, and bold execution at scale.

Why Innovation Fails to Scale

Many organizations generate great ideas but struggle to implement them broadly. Professor Hill identified a critical gap: the ability to scale innovation with speed.

Whether it’s digital transformation or operational reinvention, scaling requires more than strategy, it demands leadership behaviors that mobilize cross-functional momentum.

The ABCs of Leading Innovation

Professor Hill introduced a powerful framework from her research: the three leadership roles required to innovate at scale.¹

1. Architects – Design the conditions, systems, and values that enable innovation across the enterprise.
2. Bridgers – Connect silos, build internal and external partnerships, and foster diverse perspectives.
3. Catalysts – Mobilize people to act on bold ideas and co-create solutions at speed.

Organizations that succeed in embedding innovation, Professor Hill explained, are those that develop leaders who can move fluidly across these roles, not just at the top, but at every level of the organization.

From Collective Genius to Genius at Scale

Professor Hill’s earlier book, Collective Genius: The Art and Practice of Leading Innovation explored how great leaders cultivate environments where innovation thrives. And Genius at Scale builds on that foundation, focusing on how to operationalize and embed innovation across large, complex organizations navigating transformation.

Her call to action: stop treating innovation as episodic. Instead, make it a continuous, scalable capability, supported by leaders who know how to design culture, connect systems, and ignite progress.

Final Reflection: Are You Building Bridgers?

Professor Hill shared a candid insight from a recent executive conversation: “We don’t have enough leaders who can bridge.” The immediate reaction? Replace them. Her response? “Not so fast.” If we aren’t rewarding collaboration, partnership, and ecosystem thinking, we’re not enabling leaders to bridge, we’re discouraging it.

Instead of replacing talent, we should be developing leaders with the mindsets and behaviors needed to lead across functions, markets, and sectors. Because in today’s environment, real transformation doesn’t just require innovation, it requires integration.

1. Hill, L.A., Tedards, E., Wild, J. and Weber, K., 2022. What makes a great leader? Mastering the ABCs of innovation at scale. Harvard Business Review, 19 September. Available at: https://hbr.org/2022/09/what-makes-a-great-leader ︎

The post Scale Innovation with Speed: The ABCs of Leading Innovation appeared first on Harvard Business Impact.

Why the Tortoise Doesn’t Win Anymore: Speed to Skill as a Competitive Advantage

Mark Marone, PhD

August 18, 2025

For 2,000 years, the fable “The Tortoise and the Hare” has offered a lesson in patience and persistence. “Slow and steady wins the race,” the story goes. Deliberate, methodical progress beats speed.

But in today’s business landscape, that moral increasingly feels outdated.

Welcome to an era where speed to skill—how quickly individuals and organizations can learn, adapt, and apply new capabilities—has become a defining competitive advantage. In fact, it may be the only sustainable competitive advantage left. The new race is to see who learns fastest, applies that learning in real time, and gets maximum ROI before the landscape and the skills needed to navigate it shift again.

The Hare Learns a Lesson

In the classic tale, the hare loses. The advantage of his natural speed is undermined by his arrogance and complacency. But imagine a different version: one where the hare has learned his lesson and recognizes there is no time for napping under a tree. Instead, he scans the terrain for the best way forward, learns from every misstep, and uses those lessons immediately to move ahead, smarter and faster.

That’s today’s winning strategy in business. Companies are now consciously improving their speed to skill, making them more agile and adaptive. And they’re pulling away from competitors, even those making slow but steady progress.

Institutionalizing Learning at Speed

On the cutting edge are companies like Google and OpenAI, which approach learning like an extreme sport. OpenAI, for example, has built systems that treat every launch as a learning opportunity. Nearly 100% of releases are A/B tested, and those insights feed back into rapid cycles of iteration, dramatically increasing what some call their “learning velocity.”

At Google, speed to skill is also measured with surgical precision, especially on engineering teams. Through its DORA (DevOps Research and Assessment) framework, Google tracks how long it takes teams to deploy new code, recover from failures, and iterate changes. These metrics reflect how fast teams learn from the real world and integrate that learning into the product.

Speed to Skill at Scale

Learning velocity isn’t limited to tech companies. Unilever has become a global model for what it means to build speed to skill at scale. Through its internal talent marketplace, employees can map their own career paths and identify the skills they’ll need. They can access relevant learning and apply their new capabilities immediately by volunteering for short-term internal gigs. For instance, a marketing professional can learn basic data analysis and then test that skill in a data-driven project in a time frame of just weeks.

This integration of learning, doing, and performing creates a virtuous cycle: faster skill acquisition, faster application, and a faster impact on the business. It’s no coincidence that Unilever consistently ranks among the most future-ready global companies.

Why This Matters Now

The half-life of skills is shrinking, quickly. The World Economic Forum predicts that by 2027 44% of workers’ core skills will be disrupted. AI is transforming job roles at a pace that is making some training programs obsolete before they can be completed.

And the pressure for speed is mounting. According to our 2025 Fast, Fluid, and Future-Focused study, 55% of organizations say that incorporating gen AI, AI, or machine learning into business practices is a top priority this year. It follows that nearly half also said there are significantly increased expectations of leaders to upskill their teams in AI.

Faster training delivery alone isn’t the full solution to the problem of accelerating speed to skill. Organizations must first understand the skills they will need, something that must go hand in hand with setting strategy. Second, the training must be effective and applicable. Third, it all needs to happen within an organizational culture that embraces the application of new skills—a change-seeking organization. It is a task for which many business leaders and organizations aren’t fully prepared.

A New Moral for a New Race

So what’s the takeaway for business leaders?

The lesson isn’t that speed always wins. It’s that learning speed wins in a world that rewards insight, agility, and action.

If you’re a leader, ask yourself:

• Is learning embedded in our C-suite strategy discussions?
• How quickly can our teams integrate new technologies, tools, or processes? How do we know?
• Knowing our strategy, do our people have the opportunity to help identify the skills they are going to need?
• Does our leadership create a psychologically safe environment that is conducive to applying new skills?
• Are our performance measurements and incentives aligned with accelerating our organization’s learning velocity?

To compete in this new race, organizations must design for speed to skill. It’s not just about training programs but also systems and environments that make learning continuous, contextual, and integral to performance.

When it comes to learning, it’s time to retire the old fable. The new one is being written every day by companies that are learning their way to the finish line—faster than ever before.

The post Why the Tortoise Doesn’t Win Anymore: Speed to Skill as a Competitive Advantage appeared first on Harvard Business Impact.

Breaking Through: People-Centered Transformation Powered by Learning

Patrick Voorhies, Ed.D.

August 13, 2025

This post is co-authored by Patrick Voorhies, Ed.D., Manager, Talent & Development, Motiva Enterprises, and Susan Douglas, Ph.D., Professor of Practice at Vanderbilt University and an executive and team coach.

Achieving higher-level results from learning and leadership development remains elusive for many organizations. As we discussed in our recent post, “Beyond the Survey: Design Learning Data for Real-Time Impact,” there are many complexities and challenges slowing progress.

For organizations working to drive real change and transformation, old models for learning measurement are too slow and ineffective. It takes systems, discipline, and transparency to achieve real results. We recommend a more adaptive and holistic approach where you consider each program’s unique goals and outcomes within the larger context of business objectives.

Here we share three examples of practical shifts organizations made to move toward human-centered inquiry, recognizing the power of learning analytics and metrics to fuel action and change behaviors aligned with the business.

Example 1 – Starting small and simple to show insights

We recently worked with a large multinational energy company where the approach to evaluating leadership development programs, especially those for frontline supervisors, was rooted in participant satisfaction, facilitator effectiveness, and Net Promoter Score (NPS). Following the global enterprise deployment of a new supervisor development program to several hundred frontline leaders worldwide, executive sponsors tasked program leaders to report on the success of the offering.

Previously, program managers and stakeholders had become conditioned to answer questions from executives about the effectiveness of learning programs to say that they had a nearly perfect NPS and received glowing e-mails from previous program participants. However, in the backdrop of a more competitive landscape, executive sponsors wanted more robust evidence of success: was there any chance that participants intended to change their behavior after participating in the program? Were they going to use the new skills they learned?

In our work with this organization, we collaborated with stakeholders to implement a streamlined evaluation form that was only 7 Likert scale post-learning questions that focused on self-perceptions of learning transfer: usefulness of content, applicability to their job, support on the job from their manager and colleagues, and opportunity to apply their newly acquired skills. The higher response rate for this more straightforward form from otherwise busy frontline leaders formalized feedback mechanisms and directly tied the feedback obtained from participants to the outcomes that executives cared about: learning and leader effectiveness tied to organizational outcomes.

“Start where you are.” By continuously guiding highly skeptical stakeholders to embrace an adaptive mindset, we began to shift their focus away from limitations around reporting and data and more toward the possibilities – even within imperfect systems.

There was value in fewer questions that were targeted and research-backed, beyond standard satisfaction, NPS, or facilitator ratings. Gaining buy-in required trust and a willingness to experiment. Skepticism only disappeared when this new approach delivered more actionable insights for stakeholders and executives. Most participants found they could apply the content on the job, use what they learned, and felt supported by their leaders. These insights helped us refine the program and strengthened executive confidence in our impact.

Example 2 – Embedding learning practices into existing operations and routines

While not a company we have directly worked with, we have both admired the way that Amazon utilizes data to inform practice, and how they could use this information to continuously improve business operations or safety training programs. From reviewing Amazon’s safety practices, they monitor real-time factors using data such as work-hour patterns to understand fatigue risks during peak shifts, and incident hotspots such as repetitive motion injuries in specific roles.

In our experience, we’ve observed that these kinds of practices could alert managers to take real-time actions if employees exceed exposure times or if certain patterns are likely to happen. Safety training programs for both managers and employees can help account for behaviors to address issues related to a safety stand down or the focus of the next safety briefing. This can rely on data already tracked as part of business operations, such as driving, equipment use, or order fulfillment.

Amazon’s practices are an excellent example of the kind of participatory feedback loops we are recommending. They do not feel like ‘extra’ work for employees and partners. Organizations can measure the frequency of leadership check-ins, quality of post-incident briefs, and perceptions of leadership commitment to safety through iterative data collection. These practices can be embedded in the evaluative mindsets of the team through continuous challenging of assumptions, feedback to those who can act in near-real time, and iterative improvement.

Data-driven adaptations and leadership engagement are hallmarks of the types of embedded evaluation approaches we advocate for within systems, learning programs, and organizations. We’ve observed that practices like these have yielded a reduction in recordable injury rates and lost time incidents.

Example 3 – Humanizing data at the point of care

Have you ever answered questions about how you’ve been sleeping or feeling in the waiting room of your doctor’s office? That is measurement-based care, grounded in measures completed by patients. Unlike measuring blood pressure with a cuff or doing a blood test to assess A1C levels, in mental health care, we don’t have many tools that can give us access to meaningful metrics. When patients complete brief measures on their problems and concerns, and their providers review and talk about the results in that visit, the research shows that care is more effective and efficient. When the questions spark curiosity and the answers are used to guide collaboration, people’s symptoms improve faster.

We can apply lessons from this to learning and development. By collecting data throughout an intervention, learners encounter smaller bite-sized sets of questions that are less burdensome. Questions that are directly relevant to the experience at hand provide a valuable moment for reflection-in-action, a core component of learning that influences behavior. When the data becomes part of the dialogue in the moment, you’ve transformed a learning experience into a multi-modal strategy of engaging with learners. This both increases learning transfer and can reinforce the connection to larger goals. And, when you use readily accessible technology to capture and display data, you gain the advantage of real-time data for immediate use and aggregation for long-term organizational learning.

So, how do you embed measurement within people’s daily jobs?

Three Practical Steps to Shake Up Your Organization’s Approach to Learning Measurement

1. Start where you are
   No system or tool is perfect and most organizational data is messy. Don’t let this reality block progress toward program improvement. If you already collect “happy sheets” or other forms of participation data, it’s easy to switch out questions in existing tools and forms to more evidence-based questions such as those that assess learning transfer. Sharing findings and recommendations from the data you collect is what will drive the desire for learning and improvement by bringing voices of stakeholders from the frontline to the C-suite. We think that this cadence of sharing the perspectives of the organization with decision makers creates the desire for more program improvement supported from the top down.

2. Go to where the data is
   Embed in existing programs, systems, and tools. Most interventions are plagued with low response rates for the surveys and instruments that they deploy. This makes sense when you consider the constant state of overwhelm most knowledge workers find themselves in, not to mention deskless workers, such as those in remote or field operations. Formative check-ins on critical levels of change such as motivation, intent to apply, or cognitive load during the progress of a program can open up opportunities to course-correct even as the program is delivered.

3. Embrace measuring outcomes over activities
   Participation and reaction data is the easiest data to collect, but it is also the least helpful in evaluating program outcomes. Discovering participants do, or do not, attend your program or if they like it has no bearing on how that training may improve organizational effectiveness or performance. In times of economic uncertainty, some learning experiences may appear to be a luxury, such as expensive leadership development programs. Your C-suite may feel that you are the events planning team rather than understanding your strategic role in driving the performance of the organization. To change this perception, you must present data that demonstrates how this kind of development is a critical lever to performance. If you can’t make that case, desired culture change will remain elusive.

Learning evaluation and measurement for leadership development programs can feel like a monumental, impossible task where data is elusive and participants are unwilling captives to your ploys to collect data. By starting small and taking an iterative mindset to evolve over time, and in a way that is already part of the operational rhythm of your program or business, you can build the momentum and credibility needed to embed evaluation in a way that establishes actionable insights and builds an organization of data-informed decisions.

The post Breaking Through: People-Centered Transformation Powered by Learning appeared first on Harvard Business Impact.

Beyond the Survey: Design Learning Data for Real-Time Impact

Susan Douglas, Ph.D.

August 5, 2025

This post is co-authored by Susan Douglas, Ph.D., Professor of Practice at Vanderbilt University and an executive and team coach, and Patrick Voorhies, Ed.D., Manager, Talent & Development, Motiva Enterprises.

What if your learning data didn’t just prove that people liked your programs, but actively fueled curiosity, conversation, and change – while learning was happening?

In a highly competitive and volatile global market, organizations have become increasingly attuned to the need for evidence-based or data-informed decision making. Organizations analyze and report on a host of measures, delivering data to senior executives to guide their decisions. Advancements in technology, such as generative AI, are making data capture and use increasingly accessible to better build the evidence base.

While it is widely recognized that leaders must demonstrate how their investments drive organizational objectives, the challenge lies in consistently meeting this expectation. Talent development leaders already understand how important it is to:

• Articulate the story of how learning and development programs contribute directly to business outcomes.
• Establish clear learning and development metrics, targets, or benchmarks to use to assess the value of learning to the organization.
• Efficiently deliver learning and talent analysis and insights aligned with organizational goals and objectives.
• Leverage data to evaluate workforce skills, competencies, and capabilities in relation to business needs.

The struggle comes when organizations try to show the close connection between learning interventions and business objectives. In our examination of how organizations measure learning and leadership development, we frequently encounter three myths that drastically disconnect learning metrics from organizational learning.

Myth #1

Measuring satisfaction with a learning program tells us something important.

Measures related to how much learners enjoyed an experience don’t usually connect to the real impact on the business unless they are wildly dissatisfied. In fact, research on learning suggests that challenging experiences that lead to growth often don’t earn the most positive ratings from participants. We have too often seen measurement focus on what can be measured rather than what is meaningful to measure.

Attendance and net promoter scores have their rightful place in a comprehensive learning evaluation system. They help you understand the whole picture of what is going on with a program and provide useful insights for quality control and benchmarking. But they are not the best way to answer stakeholders’ and executives’ concerns around a program’s ability to influence business outcomes. They are only the first step in what we call a “chain of outcomes” that integrates a series of measures of how a program was experienced. This approach applies theory-based measures of short- and long-term changes in knowledge, skill, and behavior that predict real business impact.

Myth #2

Connecting learning experiences to behavior change is too difficult.

We acknowledge that survey fatigue is real and that attempts to collect data in the weeks and months after a learning program are minimally useful. Self-reporting provides only a limited slice of a leader’s behavior. Research methods that control for all the reasons why people change that have nothing to do with training are costly and lack fit with the dynamic business context.

Instead, imagine what data might result if you drop the surveys altogether (most of them anyway) and instead embed micro-data collection points throughout a learning experience. This creates opportunities for feedback loops that can be part of the learning experience itself. This is a better fit for how adults learn and even better, it transforms the why behind data collection.

Our research on measurement-based health care shows that people are much more willing to answer questions that directly impact their experience, which ultimately contributes to more and higher quality data that can then be aggregated to benefit organizational learning.

Myth #3

We evaluate to know what something is worth.

Okay, we recognize this is a controversial thing to say when we are talking about evaluation. Now that we’ve got your attention, here’s what we mean. Collecting data to evaluate the merit or worth of something is always a political activity. Emerging leaders want the program to continue because it will enhance their chances of promotion. Senior leaders believe in the program because it enhances the company’s reputation as a place that develops leaders. And yes, talent development leaders want to prove that their programs matter and enhance the business. When these things are true (and even when they are not?), organizations tend to hold learning data too close.

While talent development leaders may share metrics with HR or management, they often don’t put the data in the hands of those who can make the biggest difference with it. It’s not enough to generate data dashboards and produce reports. What’s needed is to integrate real-time relevant data into a dialogue about what we can do more of, do less of, or do differently to increase impact.

It’s time to move beyond using data only to prove something. The real power of data comes when organizations use it as a catalyst to spark curiosity, fuel shared learning, and inspire collective action—an essential shift too many still overlook.

From Transaction to Transformation: Embedding Micro-Data for Real-Time Learning

Too often, learning data collection is treated as a post-training transaction — a quick survey asking participants to rate their experience after the fact. The result? Feedback becomes an afterthought, disconnected from the actual learning. And learning and development (L&D) teams are left relying on goodwill for insights that may never arrive.

We propose a shift: from evaluation as an endpoint to evaluation as a learning strategy. When you embed data collection directly into the learning experience, you turn insight into action — in the moment.

This approach puts learning data closest to where decisions are being made. Here’s how to start:

1. Ask Questions that Shift the Learner

Design questions that do more than gather opinions – ask ones that evoke insight, awareness, or behavior change. These questions should feel like part of the learning itself, not a separate task. When data collection deepens the learner’s engagement, you get useful data for evaluation and training impact.

2. Share Results in Real Time

As data is collected, make results visible immediately within the learning experience. Show participants how their responses are shaping the journey. This not only builds trust but creates opportunities for real-time customization – even in asynchronous settings.

3. Build a Feedback loop that Powers Learning

When questions are well-designed and responses are used immediately, you unlock a powerful feedback loop. Data isn’t just captured – it is applied. Even self-reported outcomes, gathered throughout the experience, can be woven into a story of change that speaks to both learner and organizational impact.

This model requires more intention from L&D teams but delivers far greater value to learners and to the organization. Measurement of leadership development that is embedded in the “Why?” – what we call the theory of change – creates a pathway that shows impact across individual leaders, teams, and the organization. Visualizing the path, as shown below, helps create the connections forward when planning leadership programs and backward when measuring crucial outcomes.

Pathway to Embedding Leadership Development Outcomes

The key question isn’t “Can we do this?” but “How might we?”

The real magic happens when we use data as a starting point to explore, engage, and evolve together. Ultimately this is about strengthening a culture that values execution, which is about getting it right, and learning, which is about getting it better. Creating data transparency and pushing information access to the people who can drive the change requires shifts in candor, as it changes how we lead, listen, and design programs.

Our model is more than a method – it’s a mindset. If you’re ready to move beyond proving value to creating it in real time, we invite you to experiment, reflect, and learn with us. In our next post, we will showcase organizations that are taking a human-centered systems thinking approach with learning measurement.

The post Beyond the Survey: Design Learning Data for Real-Time Impact appeared first on Harvard Business Impact.

Why Psychological Safety Is the Hidden Engine Behind Innovation and Transformation

Michelle Bonterre

July 29, 2025

Last month, I had the privilege of attending Harvard Business Impact’s annual Partners’ Meeting, where Amy C. Edmondson, Novartis Professor of Leadership and Management at Harvard Business School, delivered an energizing keynote on psychological safety. Her session, “Psychological Safety: The Essential Underpinning of Successful Transformation,” left a lasting impression and a renewed sense of urgency about the environments we create for our teams.

At its core, psychological safety is the belief that a team is safe for interpersonal risk-taking, that you can ask a question, admit a mistake, or challenge an idea without fear of embarrassment or retribution. And while the concept isn’t new, Amy reminded us that in today’s VUCA world, it’s more essential than ever.

Interpersonal Risk Translates Into Business Risk

Amy told a story about a company poised to lose billions that stuck with me. No one wanted to admit what wasn’t working. It wasn’t until one leader dared to speak up that the floodgates of honest problem-solving opened.

It underscored her key point: Interpersonal risk translates into business risk. When employees are afraid to speak up, we miss out on insights, preventable mistakes go unchecked, and opportunities for innovation are lost.

High-Quality Conversations Are a Leadership Skill

So how do we create the conditions for psychological safety?

Amy broke it down into three simple leadership behaviors:

• Frame the Work: Reframe challenges as learning opportunities, not tests of competence. For example, “We’ve never done this before, and we’ll need everyone’s input to get it right.”
• Invite Participation: Ask good questions—like “Who has a different perspective?”—to signal that dissent is not only welcomed but needed.
• Respond Productively: React with appreciation and forward-thinking, even when the news is hard. Instead of “How did this happen?,” say, “Thanks for that insight. How can we help?”

Psychological Safety and High Standards Are Not Opposites

One of the most powerful insights from the session was that psychological safety and high standards aren’t in tension; they are both required for high performance.

Without safety, teams may appear agreeable but remain silent. Without standards, teams may feel comfortable but lack rigor.

The sweet spot? A culture where it’s safe to speak up and where everyone is committed to excellence.

Reflection: What Kind of Environment Are You Creating for Your Employees?

Amy asked us to reflect on our own behavior:

• Do people around you feel permission to be candid?
• Do your meetings make people smarter or quieter?
• Are you actively listening for the idea that was never shared?

These aren’t soft skills. They’re leadership imperatives in a world that demands constant learning, experimentation, and course correction.

Final Thought

Psychological safety isn’t a policy; it’s a climate. And as Amy reminded us, it’s not the goal itself but the necessary foundation for everything that matters: innovation, quality, resilience, and transformation.

If we want our organizations to thrive in uncertainty, it starts with creating space for people to speak up, think differently, and learn boldly together.

The post Why Psychological Safety Is the Hidden Engine Behind Innovation and Transformation appeared first on Harvard Business Impact.

The Fluid Future of Work: Rethinking Roles in the Age of Intelligent Machines

Mark Marone, PhD

July 25, 2025

As AI advances, human employees’ roles are evolving in unpredictable ways. Organizations must now anticipate and prepare for nonlinear role shifts, where job responsibilities fragment, fuse, or disappear altogether. The ability to proactively adapt leadership, learning, and development strategies to this new reality is emerging as an important competitive differentiator.

To meet this challenge, learning and development (L&D) must not only close current skills gaps but also forecast future ones. This means redefining how we think about jobs, how we develop talent, and how we support leaders who are navigating uncharted organizational terrain.

The Need to Prepare for the Nonlinear Evolution of Roles

In an AI-transformed world, job roles are being rapidly reshaped. Traditional workforce planning models aren’t enough to get the job done. Organizations are faced with the need to rethink their approach to workforce planning and development.

This imperative, which we call predicting the nonlinear evolution of roles, was identified by global leaders as one of the three most urgent objectives in our 2025 Global Leadership Development Study. Alongside the rise of digital labor and the acceleration of AI, it is changing not just how work gets done but also who does it and what capabilities they need to succeed.

For decades, workforce planning has typically followed a relatively linear and role-based approach: define the roles needed to support strategic goals, identify the skills and experiences required for each, and create structured career paths to build proficiency. That model no longer works. Today, leaders must anticipate role changes before they happen and equip teams to adapt in real time.

In our study, 44% of respondents said their organization is placing greater emphasis on upskilling and reskilling within leadership development. And almost half (45%) said expectations are rising for leaders to actively support their own teams’ AI upskilling.

These trends highlight the fact that leaders themselves are seeing their roles change, sometimes dramatically. Moves that create entirely new leadership roles, such as merging IT and HR departments, are making headlines.¹ Leaders are increasingly valued as sense makers who can deal with complexity and guide AI-enabled systems rather than as decision makers and subject matter experts. They are navigating new responsibilities that may not have existed a year ago, and that may change again in six months.

AI Is Driving and Redefining Role Evolution

As AI tools grow more sophisticated, they are no longer simply assisting with tasks. Increasingly, they are performing end-to-end processes autonomously. In many companies, AI has already evolved from the role of helpful assistant to agent.

One multinational company we interviewed shared their use of a “4B” framework to determine how work gets done in the future: Will a task be handled by human talent that is bought, built, or borrowed? Or will it be transferred to a bot or button (AI)? This type of thinking, which was once rare, is becoming common across industries and functions.

In some cases, AI orchestrates entire workflows. Take UBS, for example. Since 2024, the financial firm’s AI-driven service approves loans without human intervention. Credit officers didn’t disappear, but their responsibilities changed. Today, they define parameters, conduct scenario testing, and coach AI systems rather than make each decision themselves.

That kind of shift has implications for how we design leadership development. L&D teams must prepare leaders to take on new responsibilities, some of which may not be clearly defined yet. This requires not only technical upskilling but also a rethinking of leadership identity, agency, and capability.

What’s at Stake: Leadership Pipelines and Capability Gaps

The nonlinear evolution of roles affects more than just current job holders; it upends the traditional leadership pipeline. In industries where AI displaces entry-level roles, organizations may lose the proving grounds where future leaders once developed. Without action, this will create serious capability gaps down the road.

That’s why the most forward-looking companies are redesigning development paths to reflect the new reality. They are investing in tools to model likely role changes, analyze skill adjacency, and forecast future workforce needs. Crucially, they are embedding learning earlier and more broadly to build readiness, not just at the top but across the enterprise.

What L&D Can Do Now

So how should L&D leaders respond? Start by shifting the question from “What does this role require now?” to “What will this role likely become?” Then, work backward. What experiences, knowledge, and capabilities must be built today to support success tomorrow?

Effective teams are:

• Building dynamic role profiles that adapt as new technologies and business models emerge
• Integrating AI into workforce planning tools to simulate different futures and surface new opportunities
• Redesigning development programs to account for lateral moves, hybrid roles, and new leadership expectations
• Supporting leaders through transitions, helping them redefine their contributions as machines take over more routine tasks

This is not about predicting the future perfectly. It is about being prepared for many possible futures and helping people adapt and thrive in any of them.

The Bottom Line

Static job descriptions are a thing of the past. The future requires leaders who recognize that human and digital roles will be frequently reimagined. To lead in this world, people must be trained not just to perform but to pivot.

The role of L&D is no longer to close skills gaps. It is to help organizations anticipate them. And to do that, L&D leaders must be fast, fluid, and relentlessly future-focused.

Now is the time to rethink not just what we teach but why we teach it and whether it’s what’s needed to prepare people for what lies ahead in the world of work.

Explore further insights by downloading our 2025 Global Leadership Development Study: Fast, Fluid, and Future-Focused.

Research Report

2025 Global Leadership Development Study: Fast, Fluid, and Future-Focused

Download

1. Bousquette, Isabelle, “Why Moderna Merged Its Tech and HR Departments,” CIO Journal, May 12, 2025. https://www.wsj.com/articles/why-moderna-merged-its-tech-and-hr-departments-95318c2a?utm. ︎

The post The Fluid Future of Work: Rethinking Roles in the Age of Intelligent Machines appeared first on Harvard Business Impact.

Readiness Reimagined: How to Build a Change-Seeking Culture

Jeff Pacheco

July 21, 2025

For years, “change-readiness” has been a strategic imperative. Organizations have worked to cultivate cultures that adapt quickly and execute decisively. But in today’s fast-moving, AI-driven world, readiness is no longer enough.

Adaptability is still essential—but at a greater speed. The next evolution is already underway: building a “change-seeking” culture. Unlike reactive, change-ready organizations, change-seeking organizations proactively scan for opportunity, challenge assumptions, and move early—before disruption demands it.

Why “Ready” Isn’t Ready Anymore

In Harvard Business Impact’s “2025 Global Leadership Development Study,” 40% of senior leaders said their organizations are placing a greater emphasis on building change-ready cultures. But the data also revealed a shift: 71% now say the ability to lead through constant change is critical, up dramatically from just 58% in 2024. Four in 10 said leading transformation is even more crucial now than it was just one year ago.¹

This reflects a growing acceptance that the need for change is continuous and widespread. And in this environment, the ability to respond quickly is less powerful than the ability to anticipate and act early.

What Defines a Change-Seeking Culture?

Change-seeking cultures don’t wait for change—they initiate it. These organizations:

• Encourage curiosity and experimentation
• Proactively identify new ideas and unmet needs
• Create psychological safety for taking informed risks
• Integrate feedback loops that accelerate learning

They position learning and development not as a support function but as the neural network of transformation—circulating insights, capability, and culture across the enterprise.

How to Foster a Change-Seeking Culture

To foster a change-seeking culture, organizations must go beyond encouraging agility. They must design for it. That means:

• Preparing people. AI is reshaping the way we innovate, and employees need a solid understanding of the tools involved to participate. Our research shows that organizations embracing hands-on learning are more effective at building AI fluency across roles.²
• Democratizing experimentation. Organizations can learn faster by getting more people involved in testing ideas. Vastly increasing the capacity to conduct experiments is becoming more critical for making decisions based on data instead of intuition.³
• Aligning experimentation with strategy. Innovation should be guided by a clear set of strategic priorities that matter to the business. This helps avoid experiments that generate a lot of creative ideas but may fail to deliver meaningful efficiency, value, or growth.⁴
• Fostering psychological safety. If employees fear retribution for failure, they won’t experiment. Leaders must model learning behavior, reward well-intentioned risk-taking, and create space to reflect on and learn from setbacks.
• Embedding feedback loops. Organizations need mechanisms for collecting, sharing, and acting on learning so that successful experiments scale and less successful ones inform future actions.

A Case in Point: Moody’s Moves First

Moody’s—a legacy financial institution—offers a compelling example. In a traditionally risk-averse industry, its CEO, Rob Fauber, chose to go all in on generative AI, even as many peers hesitated due to regulatory uncertainty and technical risks.

As profiled in Harvard Business Review’s “How a Legacy Financial Institution Went All In on Gen AI,” Fauber focused not just on technology but also on learning and culture.⁵ His team launched the initiative with three guiding principles: Make everyone an innovator, build on new ideas, and deliver real business impact.

They started with learning. Moody’s invested in internal academies, upskilling campaigns, and broad-based AI fluency. The enhanced capability of the organization’s workforce created conditions for accelerated innovation.

By late 2024, Moody’s was deploying an AI agent capable of producing risk reports in just one hour—a task that previously required a full week of human effort. The result wasn’t just improved efficiency. It was a proof point for cultural transformation.

The Leadership Gap

Despite examples like Moody’s, many organizations remain stuck in “wait and see” mode. In our 2025 global leadership development study, 52% of respondents said their company is placing a greater emphasis on building an AI-ready culture. Yet only 36% felt their senior leaders fully embrace AI as a core part of strategy and operations.

This mismatch between aspiration and behavior matters. Cultures take shape not just through systems and programs but also through what senior leaders talk about, reward, and demonstrate. If executives want change-seeking behavior, they need to embody it—openly experimenting, learning, and adjusting.

Getting Started: Building a Change-Seeking Culture

Building a change-seeking culture isn’t about launching a single transformation program. It’s about instilling an ongoing top-down and bottom-up capability for sensing and seizing what’s next. Organizations can take these actions to begin:

1. Start with learning and make it visible. Innovation still starts with people, but given AI’s central role in innovation today, building AI fluency across the organization is essential.
2. Create systems that reward initiative, not just execution. Recognize teams for surfacing new ideas, identifying inefficiencies, and learning from pilots—even when those pilots fail. Normalize the idea that progress can start with anyone’s ideas and initiative.
3. Hold leaders accountable for culture. Make effectively leading change, encouraging innovation, and fostering psychological safety core performance expectations, not soft add-ons.

The Bottom Line

Many organizations still treat change-readiness as a strategic endpoint. But in a world of constant reinvention, it’s only the beginning. As technology rewires markets, roles, and operating models, the ability to initiate and lead change—not just react to it—is the goal.

The organizations that will succeed are those where everyone, at every level, is expected to help chart what comes next. Change-seeking is not a capability confined to innovation teams or digital labs. It is a cultural imperative.

Standing still is now the greater risk. The advantage belongs to those willing to move first.

To find out more about how we can help your organization build a change-seeking culture, contact us today.

1. Harvard Business Impact, “2025 Global Leadership Development Study,” 2025. https://www.harvardbusiness.org/insight/2025-global-leadership-development-study-fast-fluid-and-future-focused/ ︎
2. Harvard Business Publishing Corporate Learning, “Learning Through Experimentation: Why Hands-On Learning Is Key to Building an AI-Fluent Workforce,” Harvard Business Publishing, 2024. https://www.harvardbusiness.org/insight/learning-through-experimentation-why-hands-on-learning-is-key-to-building-an-ai-fluent-workforce. ︎
3. Iavor Bojinov, David Holtz, Ramesh Johari, Sven Schmit, and Martin Tingley, “Want Your Company to Get Better at Experimentation?,” Harvard Business Review, January-February 2025. https://hbr.org/2025/01/want-your-company-to-get-better-at-experimentation. ︎
4. Rogers, David L., “The Missing Link Between Strategy and Innovation,” HBR.org, March 18, 2024. https://hbr.org/2024/03/the-missing-link-between-strategy-and-innovation. ︎
5. Stuart, Toby E., “How a Legacy Financial Institution Went All In on Gen AI,” HBR.org, March 25, 2025. https://hbr.org/2025/03/how-a-legacy-financial-institution-went-all-in-on-gen-ai. ︎

The post Readiness Reimagined: How to Build a Change-Seeking Culture appeared first on Harvard Business Impact.

From Emotional Triggers to Values-Based Leadership: A Practical Framework

Craig Dickerson

July 1, 2025

This is the third post in our series exploring how the ladder of inference can impact and improve leadership effectiveness. In our first post, we introduced this mental model, which explains how we unconsciously leap from raw data to firm conclusions. Our second post expanded the framework to reveal how conclusions harden into beliefs that drive our actions. In this post, we’ll explore the emotional aspect of the ladder, showing how our inferences and stories become the source of our emotions and reactions, and how developing emotional agility can free us from these automatic patterns.

By connecting the ladder of inference with emotional agility, leaders gain tools to navigate complex interpersonal dynamics and foster productive conflict—elements crucial to human-centered leadership and the resilience required for today’s complex business environment.

Identifying the source of emotions

Before we dive into dealing with emotions, it might be helpful to spend some time thinking about their origin. Pause and think for a moment—what would you say is the source of your emotions? Many people believe that emotions are reactions to external events and, therefore, we have little control over them. Have you ever thought or said, “You are making me mad”? Is the other person solely responsible for creating the frustration welling up inside you? While external factors play a part in our emotions, psychological research points to our internal narratives as playing a significant role in how we feel about a situation.

Imagine this scenario: Two managers review their team’s performance report indicating that half of their goals have been met. Five goals completed out of the 10 objectives, in total. While one manager sees it as a step toward achieving success and is excited by the progress made so far, the other manager views it as falling short on half of their targets and feels disheartened by what seems like a setback. The data is the same, but the emotion is different, because story and mindset are different. We all know the old adage “glass half empty, glass half full.” Intuitively, we know that it is our thinking that drives our emotions, and this is good news because it means that we need not be held hostage to our emotions. However, we often don’t act according to that knowledge.

Understanding that our emotions arise from the narratives we construct empowers us to examine those narratives and choose our reactions with intentionality, an essential skill for human-centered leadership.

Ladder of inference meets emotional agility

In our last post, we saw how our conclusions about people or situations can solidify into stories. Remember Alex and Javier’s meeting? Alex concluded that Javier was rude when he arrived late, and this story influenced how Alex felt and acted for the remainder of their interaction. The story “Javier is rude” made Alex feel disrespected and perhaps angry, which affected his behavior toward Javier.

Had Alex chosen a different story, perhaps “Javier might be dealing with a crisis,” he would likely have felt concern rather than anger, a more productive emotion. In their Harvard Business Review article on emotional agility, Susan David and Christina Congleton describe how leaders get “hooked by their negative thoughts and emotions” like “fish caught on a line.”²

According to them, when we are hooked, we lose the ability to respond thoughtfully. Instead, we react from a place of emotional intensity that often contradicts our values and how we want to show up in the world. Think again of Alex. How likely is it that he wants to be known as an angry person? Is it more likely that he values collaboration or empathy (or at least prefers to be seen that way by his superiors)?

Think about a recent time when you felt triggered at work. What story were you telling yourself about the situation? How did that story shape your emotional response? Did your actions align with your values, or did you find yourself behaving in ways you later regretted? Was your resulting behavior productive or counterproductive?

The challenge is that these emotional hooks feel incredibly real and justified in the moment. When we’re at the top of our ladder, our conclusion feels like an absolute truth, and the emotions that follow seem like the only reasonable response. But as we’ve learned, these conclusions are often based on incomplete data, assumptions, and biases.

Four ways emotional agility can help

In their article, David and Congleton outline four key practices that, when combined with our understanding of the ladder of inference, can help leaders unhook themselves from unproductive emotional patterns.

1. Recognize your patterns: The first step is noticing when you’ve been hooked. The authors note that “your thinking becomes rigid and repetitive” and “the story your mind is telling seems old, like a rerun of some past experience.” When you find yourself climbing the same ladder repeatedly—making the same assumptions about certain people or situations—that’s a signal you’re caught in a pattern.

2. Label your thoughts and emotions: Instead of saying “My coworker is disrespectful,” try “I’m having the thought that my coworker is disrespectful, and I’m feeling angry.” This simple act of labeling creates distance between you and your experience, allowing you to see your thoughts and emotions as “transient sources of data that may or may not prove helpful.”

3. Accept them: Acceptance doesn’t mean resignation or acting on every feeling. It means acknowledging what you’re experiencing without immediately trying to suppress it or act on it. Take 10 deep breaths and notice what’s happening. What story are you telling yourself? What assumptions did you make as you climbed your ladder?

4. Act on your values: Once you’ve created space between yourself and your emotional reaction, you can choose actions that align with your values rather than your triggered state. Ask yourself: Will this response serve me and my organization in the long term? Am I taking a step toward being the leader I most want to be?

In real life: resolving a generational conflict

Let’s look at another scenario to see how this might work in practice. Maya, a Millennial project manager, and Robert, a baby boomer senior analyst, are constantly at odds.

Maya sends Robert a Slack message asking for urgent input on a proposal. When Robert doesn’t respond for two hours, Maya’s ladder kicks in: Robert saw the message (data selection), he’s ignoring me because he doesn’t respect younger colleagues (assumption), he’s dismissive and stuck in his ways (conclusion), Robert is slowing down the team (belief). This story triggers frustration and resentment (emotions).

Meanwhile, Robert checks his messages at scheduled intervals to maintain focus. When he sees Maya’s message labeled “urgent” for what seems like a routine question, his ladder goes to work: Everything is urgent to Maya (data selection), younger workers have no sense of priorities (assumption), Maya is impatient and demanding (conclusion), millennials don’t understand professional boundaries (belief). This story triggers irritation and dismissiveness (emotions).

Both are hooked by their stories and emotions. But what if one of them practiced emotional agility?

Maya could notice her pattern; she often feels dismissed by older colleagues. She could label it: “I’m having the thought that Robert is ignoring me.” She could accept her frustration without acting on it. Then, she could choose to act from her values of teamwork and collaboration by asking Robert how he likes to communicate.

The power of “strong opinions, weakly held”

Leaders who understand the ladder of inference know their conclusions might be wrong. Stanford professor Paul Saffo calls this having “strong opinions, weakly held,” which is the capacity to reach conclusions quickly but discard them when encountering conflicting evidence.³ To go a step further, leaders should actively look for data that challenges their stories.

This posture naturally leads to curiosity about opposing positions. Instead of restating your position louder or in different ways, you ask questions like: “We seem to be coming to different conclusions while looking at the same data. Can you help me understand how you came to your conclusion?” This communicates both curiosity and inclusion.

Additionally, no one wants to have a debate with someone unwilling to be swayed by new information. Think about disagreements you’ve had with others. Are you more willing to engage with someone steadfast and unshakable in their position, or someone curious about your perspective? How does it feel to discuss something with someone who listens to understand versus someone who listens only to respond?

The trick is to genuinely listen—not to trap them or prove them wrong, but to understand. Find things you agree on. Ask questions until you can paraphrase the other person’s ladder back to them accurately. Then—and this is key—be willing to change your own mind based on what you learn.

Supporting leadership fitness through emotional agility

This integration of the ladder of inference with emotional agility directly supports what we call Leadership Fitness . Specifically, it enhances flexibility (the capacity to leverage new strategies and behaviors in response to changing circumstances) and balance (the ability to manage tensions between opposing forces and ideas).

When leaders can unhook from their automatic emotional responses, they create space for the metacognition necessary to challenge their subconscious encoding processes. They can intentionally choose leadership behaviors that serve their goals and values rather than merely reacting from triggered states.

The bottom line

The ladder of inference isn’t magic; people will still disagree, and emotions will still arise. But when leaders combine this awareness with emotional agility practices, they can transform potentially destructive conflicts into productive dialogues. By recognizing that our emotions stem from the stories we tell ourselves and that these stories are built on potentially flawed assumptions, we gain the power to choose our responses more intentionally.

The capability to create space between stimulus and response, to unhook from our stories and emotions, and to act from our values represents a fundamental shift in leadership effectiveness. In our complex, rapidly changing environment, leaders who master this skill build stronger relationships, make better decisions, and create cultures where productive disagreement and learning can flourish.

The next time you feel emotionally triggered at work, pause and ask yourself: What story am I telling myself? What assumptions led me here? And most important: How can I respond in a way that aligns with my values and moves us forward? In that space between trigger and response lies your growth as a leader.

1. Covey, S., 2017. Prisoners of Our Thoughts. Berrett-Koehler Publishers, Foreword; Also see: https://www.viktorfrankl.org/quote_stimulus.html ︎
2. Davis, S., and Congleton, C. 2013. Emotional agility. Harvard Business Review ︎
3. Saffo, P., 2007. Six rules for effective forecasting. Harvard Business Review ︎

The post From Emotional Triggers to Values-Based Leadership: A Practical Framework appeared first on Harvard Business Impact.