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.

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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.

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The Future Is Fluent: Why AI Demands a New Kind of Leader

Leadership roles are shifting to align more closely with the demands of AI initiatives.
Organizations that foster an AI-ready culture through leadership will see faster, more sustainable transformation.

Explore the full infographic to uncover more insights.

View the infographic

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3 Strategies to Meet the Moment: Building the Collective Intelligence of Humans and Machines

AI-driven change is making learning more vital than ever. Predicting what will need to be learned and by whom is urgent.

In our 2025 Global Leadership Development Study, responses from 1,159 learning professionals and functional heads across 14 countries revealed three key strategies learning and development is using to meet the moment.

View the full infographic to learn the strategies for building the collective intelligence of humans and machines.

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3 L&D Priorities for Building the Collective Intelligence of Humans and Machines

In our 2025 Global Leadership Development Study, responses from 1,159 learning professionals and functional heads across 14 countries revealed three critical objectives guiding learning and development strategy this year.

Organizations must build the collective intelligence of humans and machines by acting on these three imperatives to unlock the opportunity to lead, transform, and thrive in an AI-driven world.

View the infographic

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Learning Through Experimentation: Why Hands-On Learning Is Key to Building an AI-Fluent Workforce

Jeff Pacheco

June 23, 2025

Whether it’s Serena Williams dominating the tennis court or Magnus Carlsen orchestrating moves on a chessboard, one of the key things that elevate them to elite status in their fields is practice.

Without hands-on practice, learning stays in the realm of theory—important, but inert. Practice transforms knowledge into skill. It’s what makes learning real.

The same holds true for AI. Although some organizations offer courses, workshops, or resource libraries, these alone may not suffice. Organizations must provide employees with space to experiment to build true AI fluency. Employees must test, learn, practice, and apply AI tools in ways that directly relate to their work.

Recent research from Harvard Business Publishing Corporate Learning, in partnership with Degreed,¹ highlights this point clearly: AI-fluent employees differentiate themselves by engaging in experimentation. They don’t just study AI—they engage with it actively.

The business case for cultivating an AI-fluent workforce is clear: organizations that empower employees to learn by doing are better positioned to adapt, innovate, and grow in a rapidly evolving landscape.

So how do we get there? It starts by understanding what AI fluency looks like—and what sets fluent learners apart.

What Is AI Fluency and Why Does It Matter?

In our study we defined AI fluency as those who were frequent users of gen AI in their daily work and had a strong understanding of its capabilities.

AI fluency isn’t just a buzzword—it’s also a key driver of stronger business performance. The study found that AI-fluent respondents were far more likely to report stronger outcomes at both the individual and team levels. Among them, 81% said they were more productive, 54% were more creative, and 53% were better prepared to solve complex business challenges.

Embedding AI fluency across the organization extends its impact beyond individual productivity. Teams move faster, collaborate more effectively, and unlock new ideas with AI fluency. Organizations with fluent workforces are better positioned to adapt to change, solve problems creatively, and drive meaningful innovation. In today’s environment, AI fluency isn’t just nice to have—it’s foundational.

How AI-Fluent Respondents Learn Differently

AI-fluent respondents distinguish themselves not just by knowledge, but by their learning behaviors. Their commitment to self-directed learning drives their fluency.

They are engaging in self-directed learning at least weekly, with a third of respondents saying they participate in its daily. This learning often includes quick, ad hoc bursts of content and real-time application. The real differentiator in learning comes from experimenting and incorporating gen AI into daily workflows. AI-fluent respondents were two times more likely to say that they learned about generative AI through experimentation compared to all other respondents.

AI enables learning through active engagement, making it an exceptional learning tool. In the game of chess, when a player moves a piece, the piece does not offer real-time feedback as to why their move was the right one or not. Instead, a player must move the pieces and study the outcomes. Mastery comes through repetition and study.

AI operates differently: it executes a move, clarifies the logic behind it, and often proposes a better one. This is what sets AI apart. It’s not just something you learn about—it’s something you learn with. AI becomes both the subject and the teacher, guiding you as you experiment, adapt, and grow.

Making AI Learning a Team Effort

Lack of organizational support, rather than employee motivation, is the biggest barrier to scaling AI fluency. Most workers want to learn, but lack time, guidance, and access to meaningful opportunities to practice. While AI-fluent employees thrive through hands-on experimentation, many others are stuck waiting for permission, resources, or a roadmap that never comes.

Access alone doesn’t suffice—it’s also about culture. Many organizations still view experimentation as optional, rather than as an essential part of everyday work. Building AI fluency requires systematic integration into real workflows, team projects, and business priorities. People learn best by solving real problems together.

A recent Harvard Business Review article² demonstrated that integrating fine-tuned AI models as active members of the team led to significant gains in efficiency and accuracy. The organization began by mapping out their workflows to identify where AI could add the most value—and how. Through continuous experimentation, they refined both their models and their processes, ultimately turning AI into a strategic advantage.

The Bottom Line

Leaders play a critical role. Leaders drive engagement with AI across teams by prioritizing exploration and signaling that AI experimentation matters. Scaling AI fluency requires teams to have opportunities to learn together. Teams must allocate time, create shared learning goals, and embed AI into their day-to-day collaboration. Teams must also encourage peer learning—where employees share use cases, learn from each other, and build on each other’s successes. Organizations must shift from isolated learning to collective experimentation to truly unlock the value of AI and drive adoption at scale.

To find out more about how to begin building AI fluency in your organization, contact us today.

1. Harvard Business Publishing Corporate Learning. 2025. “Gen AI Fluency at Work: How Organizations Unlock the Full Potential of an AI-Proficient Workforce.” Harvard Business Publishing. March 27, 2025. https://www.harvardbusiness.org/insight/gen-ai-fluency-at-work-how-organizations-unlock-the-full-potential-of-an-ai-proficient-workforce/. ︎
2. [ii] Harvard Business Review, “Teach AI to Work Like a Member of Your Team,” April 2025, https://hbr.org/2025/04/teach-ai-to-work-like-a-member-of-your-team. ︎

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5 Questions to Ask About Your Digital Transformation

Jeff Pacheco

June 16, 2025

Many leaders describe their organizations as “undergoing a transformation.” In today’s business landscape, claiming otherwise can appear to signal stagnation or falling behind.

However, when pressed with a more critical follow-up—“What is the actual plan to ensure its success?”—leaders often struggle to articulate a clear path forward. It is this question that more accurately reflects an organization’s true progress toward meaningful transformation.

True transformation demands more alignment, clarity, and accountability.

A recent joint study by Harvard Business Publishing Corporate Learning and Degreed uncovers an important relationship between organizations and AI-fluent workforces. Organizations that actively invest in AI support, infrastructure, and mindset are more likely to drive AI fluency among their workers. Our study defined people exhibiting AI fluency as those who use gen AI daily in their workflows and have a strong understanding of its capabilities.

For organizations well into their digital transformation journey, failing to critically evaluate the clarity, coherence, and feasibility of their strategy poses a significant risk. Leadership teams would be well served to pause and reflect on these five essential questions—before momentum outpaces alignment.

1. Is gen AI learning a core part of your organization’s strategic priorities?

A successful transformation starts with commitment, and commitment begins with learning. According to our workforce study, just 12% of organizations are making gen AI learning a strategic priority.

If AI capability-building is just an optional side project, your teams will sense the mixed signals. To lead effectively in an AI-powered world, organizations must treat gen AI fluency as a strategic priority—not a curiosity.

This starts at the top. Leaders need to articulate why AI matters; model their own engagement; and invest in clear, structured learning paths. As noted in the workforce study, two of the biggest barriers to upskilling in AI are a lack of guidance and a lack of resources.

Self-directed learning shouldn’t mean going about it alone. Leaders must be the bridge between intention and adoption—setting direction, removing barriers, and championing the culture that turns interest into capability.

2. Do you have the right tools, platforms, and infrastructure to integrate gen AI into your core processes?

AI infrastructure isn’t just about plugging in new tools; it’s about enabling responsible action. That means two things: access to the right technology and accountability for how it’s used.

First, the tech. Leaders must ensure that employees aren’t just aware of gen AI—they need curated tools that match the way work gets done. A recent Harvard Business Review article¹ showed that generic tools often fall flat because they aren’t embedded into core workflows. Effective integration requires organizations to start by mapping their workflows to align the right AI solutions to specific processes.

But even the best tools are useless without the right standards. Without clear policies, permissions, and ethical boundaries, teams may misuse AI—or avoid it entirely out of confusion or fear. A strong infrastructure includes governance that protects your people, your brand, and your data.

In one proposed model, Shelly Palmer ² outlines a governance framework with four key components and organizational structures for AI governance such as creating committees for strategic oversight, standards-setting, transparency, and accountability.

Whether organizations adopt this exact structure or not, leaders are responsible for ensuring the right AI guardrails are in place.

True transformation happens when tools and trust evolve together. Leaders must create environments where AI is not only available but also usable, safe, and aligned with the organization’s purpose.

3. Are your leaders equipped and willing to embrace gen AI in their daily work?

If gen AI is to scale meaningfully across the organization, alignment at the leadership level is nonnegotiable.

Everett Rogers’ Diffusion of Innovation ³ theory describes the chasm that often exists between early adopters and the early majority. For gen AI, this chasm can become a dangerous fault line—especially if some leaders fail to embrace the technology or remain disconnected from broader organizational priorities. If that gap widens, the ability to scale AI across teams, improve operational models, and drive innovation could stall entirely.

A recent Harvard Business Review article, “If You Want Your Team to Use Gen AI, Focus on Trust,”⁴ highlights trust as the foundation for building true alignment. When leaders view AI tools as reliable, capable, transparent, and humane, they’re far more likely to adopt them in their own work—and model that adoption for their teams.

Leadership alignment isn’t about blanket enthusiasm, it’s about conviction built on evidence. Leaders must adopt a mindset of learning about AI and envisioning its true value at scale across the organization. The more leaders trust AI tools, the more confidently they can lead others across the adoption curve.

4. Does your culture encourage experimentation with gen AI across teams and roles?

Learning and infrastructure are foundational, but they’re not sufficient. According to our workforce study ⁵, the key differentiator in organizations that build gen AI fluency is one thing: experimentation.

When teams are empowered to explore, test, and apply gen AI in their daily work, they don’t just grow their individual skills—they refine how AI tools integrate into the organization’s workflows. Experimentation fuels both personal proficiency and process innovation.

The problem is, in some organizations, experimentation is still seen as “playing with tech”—a distraction rather than a driver of value. Leaders who view it this way risk impeding progress and signaling skepticism to their teams.

To truly unlock AI’s potential, leaders must encourage experimentation. That means giving teams space to explore, spotlighting successful experiments, and pushing the boundaries of how AI can be used. The most impactful use cases often aren’t discovered in strategy decks—they emerge from the ground up, through trial, error, and curiosity.

5. Are you still prioritizing critical thinking skills alongside AI capabilities?

Amid all the excitement about digital transformation, it’s easy to lose sight of the importance of critical thinking.

Gen AI tools are powerful amplifiers. But without human judgment, they’re just that—amplifiers. The goal is not to outsource thinking but to augment it—freeing up time for deeper, more strategic work.

A recent Harvard Business School paper ⁶ explores this balance. It shows that teams using gen AI can enhance their individual cognitive abilities not by replacing human collaboration, but by elevating it.

Leaders must reinforce this mindset that AI should be a teammate, not a crutch. The future belongs to teams that can think with AI, not just through it.

Bottom Line

Digital transformation isn’t just about adopting new tools—it’s about transforming how organizations learn, lead, and think. That starts with asking the right questions.

Leaders must regularly step back and assess whether their organization is truly equipped to scale gen AI. Support, infrastructure, and mindset are the pillars that determine whether digital transformation efforts thrive or fail.

AI is reshaping the nature of work, but it has not made leadership any less vital. On the contrary, the demands on leaders are growing. Those who can foster trust, set a clear direction, model continuous learning, and create space for experimentation will determine whether their organizations thrive—or are left behind.

To find out more about how to navigate the difficulties of transformation as a leader, contact us today.

1. Harvard Business Review, “Teach AI to Work Like a Member of Your Team,” April 2025, https://hbr.org/2025/04/teach-ai-to-work-like-a-member-of-your-team. ︎
2. Shelly Palmer, “Who Owns AI?” April 20, 2025, https://shellypalmer.com/2025/04/who-owns-ai/. ︎
3. NAFEMS, “Diffusion of Innovation,” accessed April 2025, https://www.nafems.org/community/the-analysis-agenda/diffusion-of-innovation/. ︎
4. Ashley Reichheld, Aniket Bandekar, Ian Thompson, and Lauren Teegarden, “If You Want Your Team to Use Gen AI, Focus on Trust,” Harvard Business Review, January 24, 2025, https://hbr.org/2025/01/if-you-want-your-team-to-use-gen-ai-focus-on-trust. ︎
5. Harvard Business Publishing Corporate Learning and Degreed, Gen AI Fluency at Work: How Organizations Unlock the Full Potential of an AI-Proficient Workforce, April 2025, https://www.harvardbusiness.org/insight/gen-ai-fluency-at-work-how-organizations-unlock-the-full-potential-of-an-ai-proficient-workforce/.​ ︎
6. Fabrizio Dell’Acqua, Charles Ayoubi, Hila Lifshitz, Raffaella Sadun, Ethan Mollick, Lilach Mollick, Yi Han, Jeff Goldman, Hari Nair, Stew Taub, and Karim R. Lakhani, “The Cybernetic Teammate: A Field Experiment on Generative AI Reshaping Teamwork and Expertise,” Harvard Business School Working Paper, No. 25-043, March 2025, https://www.hbs.edu/faculty/Pages/item.aspx?num=67197. ︎

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Succeeding in the Digital Age: Why AI-First Leadership Is Essential

While AI makes powerful operational efficiencies possible, it cannot yet replace the creativity, adaptability, and contextual understanding that humans bring to strategic decision making.

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4 Keys to AI-First Leadership: The New Imperative for Digital Transformation

AI has become a defining force in reshaping industries and determining competitive advantage. To support AI-first strategies, leaders must progress through a deliberate journey: building foundational AI knowledge, cultivating an AI-first mindset, and honing AI-related skills, before confidently leading with AI.

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Learning to Lead in the Digital Age: The AI Readiness Reflection

As the race to integrate generative AI accelerates, organizations face a dual challenge: fostering tech-savviness across teams while developing next-generation leadership competencies. These are critical to ensuring that “everyone” in the organization is prepared for continuous adaptation and change.

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Gen AI at Work: Powering Culture & Performance

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HBR Author & Vice President, Capgemini Invent Management Lab

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HBR Author & Director of Content & Research, Capgemini Invent Management Lab

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Gen AI Fluency at Work: How Organizations Unlock the Full Potential of an AI-Proficient Workforce

For decades, artificial intelligence seemed more like a sci-fi fantasy than a practical tool in the workplace. However, the pace of digital transformation is accelerating beyond expectations. Generative AI (gen AI) has arrived, and the workforce is only beginning to explore its potential.

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