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.

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.

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. ︎

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