Strengthening the Leaders Who Power Transformation

Midlevel leaders are at the heart of every major shift in a business. They drive strategy forward, keep teams aligned, and make change happen. But the role has grown more complex—and most leaders haven’t been given the tools to keep up.

To meet the demands of constant change, these leaders need to think strategically, move quickly, and connect the dots across people, priorities, and tech.

Explore the full infographic to see how these leaders are stepping up to lead transformation.

View the infographic

The post Strengthening the Leaders Who Power Transformation appeared first on Harvard Business Impact.

The Leadership Imperative

Equipping Leaders and Organizations to Thrive Amid Accelerating Change and AI-Driven Transformation

Leadership today has undergone a fundamental transformation compared to just a few years ago. 

Effective leadership development ensures leaders at every point of influence are equipped to drive impact, align teams, and deliver results in a world of acceleration.

The post The Leadership Imperative appeared first on Harvard Business Impact.

Leveling Up Your Midlevel Leaders

Midlevel leaders are under more pressure than ever. They’re expected to deliver today and drive transformation for tomorrow. But many feel stuck in the middle—pulled between big goals from above and the needs of their teams below.

We surveyed over 600 global leaders to understand what’s really going on. The data shows a clear story: midlevel leaders are ready to step up, but they need the right support to do it.

Explore the full infographic to see what midlevel leaders are facing—and how to help them thrive.

View the infographic

The post Leveling Up Your Midlevel Leaders appeared first on Harvard Business Impact.

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.

Assessment: 10 Strategic Questions for the C-Suite

AI is transforming strategy, execution, and leadership at a pace that exceeds many organizations’ readiness. Yet only 36% of organizations say their leaders excel at embracing AI as a core part of strategy and operations.

Use these 10 questions to assess how well your organization is adapting in a fast-moving world.

Download the assessment

The post Assessment: 10 Strategic Questions for the C-Suite 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.

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.

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

In 2025, Harvard Business Impact conducted a comprehensive global study to survey more than 1,100 leadership development professionals, examining how their work fits into the jobs to be done for organizations today.

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

2025 Global Leadership Development Study: Research Findings

Harvard Business Impact’s 2025 Global Leadership Development Study surveyed more than 1,100 learning and development professionals, human resources leaders, and functional heads across 14 countries and multiple industries to understand how their work aligns with the jobs to be done for organizations today.

The post 2025 Global Leadership Development Study: Research Findings appeared first on Harvard Business Impact.

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. ︎

The post 5 Questions to Ask About Your Digital Transformation appeared first on Harvard Business Impact.

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.

View the infographic

The post 4 Keys to AI-First Leadership: The New Imperative for Digital Transformation appeared first on Harvard Business Impact.