We believe leadership’s strength comes from human values, ingenuity, and connection. AI in leadership development accelerates and deepens our programs and research, but human expertise ensures what we create is trustworthy, ethical, meaningful, and actionable.

By combining rigorous research methods with advanced technology, we’ve designed an approach where humans and AI work together to reveal insights that drive lasting leadership impact and responsibly advance leadership development.

A Case Study: Using AI to Help Senior Leaders See Data About How They Lead

Our HiFi Conversation Analytics tool combines human expertise with AI in one of our leadership programs. HiFi helps senior leaders understand their behaviors in the Looking Glass, Inc.® simulation of our Leading for Organizational Impact program.

HiFi uses wearable technology to capture leaders’ conversations during the simulation, tracking data such as speaking time and interactions with others. While technology excels at measuring and detecting patterns we can’t easily see, humans provide essential context, judgment, and developmental guidance that AI can’t replicate. This collaboration exists at every stage — from designing the solution and validating data to implementing insights in a program — to ensure AI’s contributions to our leadership development program are accurate, ethical, and meaningful.

Here’s a deeper look at each stage.

Design: Human Judgment Guides AI Potential

Measurement without purpose is just noise. The most sophisticated AI tool isn’t useful if it doesn’t help leaders change what matters. That’s why our design process for integrating HiFi into our Leading for Organizational Impact program started with a human question: What behaviors, if measured and made visible, would help senior leaders grow?

The program helps leaders see themselves as part of an organizational system, not standalone actors. Our emphasis on system-wide influence is based in part on an AI-powered language model we use to analyze thousands of leadership challenges that senior leaders reported to us — 6 of the top 10 senior leader challenges involve working within a larger system.​

When we designed HiFi for this program, we prioritized metrics that reveal interdependence: Whose perspective did you seek? How did that impact your influence? Those questions came from decades of leadership research on systems thinking, the program’s learning objectives, and what we know drives behavior change — not from what AI happened to capture easily.

While AI enables breadth (capturing everything), humans provide focus (choosing what matters). We’re now leveraging advancements in AI to deepen HiFi’s analytical capabilities — but the same principle holds. AI expands what we can measure and analyze while human judgment determines whether those insights help leaders grow.

Validation: Helping AI Align With Human Expertise

Humans must supervise AI inputs and outputs. Before HiFi analyzes conversations, we validate it through a rigorous, cyclical process borrowed from decades of assessment science: content analysis.

Expert human coders review conversation transcripts line by line, making judgments based on well-supported leadership frameworks. For example, is this statement focused on making sure the group achieves its objectives, or on recognizing the contributions of individual members? When multiple coders agree on their assessments, those judgments then guide the AI on what to look for. The AI isn’t leading; it’s learning to replicate expert human judgment at scale.

This is supervised learning in action. We’re skeptical of deploying models without this kind of fine-tuning, because unsupervised AI can miss context and produce misleading results.

Even as we integrate more sophisticated AI capabilities into HiFi, the research perspective remains: human experts set the standard, and technology is evaluated against that standard. AI expands our capacity to analyze conversations at scale, but it earns that role by proving it can reliably mirror the judgments humans would make.

Implementation: Humans Make AI Insights Meaningful

The data itself doesn’t create change — the conversation about the data does. During their Leading for Organizational Impact journey, participants receive HiFi-generated behavioral feedback alongside peer ratings and collective impact data. But sensemaking is a deeply human process. What matters is whether leaders can understand the data, connect it to their experience, discuss it with their peers and facilitators, and see how to use it to guide their development. That’s where human expertise is essential for AI in leadership development to have real impact.

Our facilitators are deeply involved in observing what resonates with leaders, what confuses them, and what sparks insight. Their feedback shapes how we present the data: which visualizations work best, what language makes the data accessible, how much data is too much. These aren’t technical decisions an algorithm can make — they require human judgment about what helps people learn.

During one session, for example, the HiFi insights showed a leader had high speaking time and strong influence scores — technically good data. But the facilitator noticed something: every time this leader spoke, their counterpart went quiet. The algorithm saw influence. The leader saw they had taken space to the detriment of the group. That’s the conversation the group needed to have, and the facilitator in the room helped surface it.

Facilitation also means tailoring programs to fit the client’s context. We implement HiFi through custom programs in partnership with clients, integrating it with other solutions to achieve specific goals. For example, when an organization wanted to improve how leaders give and receive feedback, we combined HiFi with our Situation – Behavior – Impact (SBI) feedback model. A knowledgeable facilitator who understood the client’s culture and challenges collaborated closely with them, leveraging an AI-supported tool to co-design a better experience.

What This Means for Your Organization

AI expands what’s possible in leadership development. But it’s human expertise in design, validation, and implementation that ensures those possibilities help your leaders’ development become a reality. As you explore AI-enabled tools for your organization, and consider where deploying AI in leadership development programs might fit, ask yourself: Who’s making the critical decisions about what gets measured, what it means, and how it helps leaders develop?

Ready to Take the Next Step?

At CCL, we’re exploring how human expertise can shape the use of AI in leadership development. Our Leading for Organizational Impact program leverages insights from HiFi, plus 360 assessments and executive coaching, to help senior leaders become more strategic and effective in their organizations.

The post The Power of Both: Integrating Human Expertise & AI in Leadership Development appeared first on CCL.

• Direction is agreement within your organization on overall goals
• Alignment means coordinated work in your organization
• Commitment is a feeling of mutual responsibility in your organization

Together, these 3 elements are the outcomes of leadership, and they’re essential to tackling any challenge — including the one of integrating AI (artificial intelligence) into organizational workflows and culture.

Strengthening DAC isn’t optional — it’s an imperative for individuals, teams, and organizations to be able to thrive amid complexity, uncertainty, and change.

And our research suggests a strong correlation between high levels of DAC in an organization and high levels of AI maturity or adoption.

The 4 Stages of AI Maturity

To better understand the potential connections between AI maturity and leadership, we turned to MIT’s CISR Enterprise AI Maturity model, which depicts 4 stages of organizational AI maturity:

Stage 1: Discovering (Experiment & Prepare)

At this stage, organizations are curious about AI and have started to reflect on the human implications on AI. Organizations in this stage focus on educating the workforce on AI, establishing acceptable use policies, improving data accessibility, ensuring data-driven decision-making, and identifying where human input is necessary in processes.

Stage 2: Adopting (Build Pilots & Capability)

At this stage, organizations recognize AI’s relevance to their strategy and are starting to experiment and integrate. This includes simplifying and automating processes, creating use cases, sharing data via APIs, leveraging a coaching and communicative management style, and using both traditional and generative AI models to enhance work.

Stage 3: Transforming (Develop AI Ways of Working)

At this stage, organizations are fully aware of how AI impacts their work and are building new workflows and process for effective AI integration. This involves expanding process automation efforts, adopting a test-and-learn approach, designing for reuse, incorporating pre-trained models and exploring proprietary AI models, and investigating the use of autonomous agents.

Stage 4: Differentiating (Become AI Future-Ready)

At this stage, organizations are recognized as leading the way in AI transformation and are imagining and prototyping new methods of using AI. This involves embedding AI into decision-making and processes; developing and offering AI-augmented business services; and integrating traditional, generative, agentic, and robotic AI.

AI Maturity & Leadership: Our Research Findings

For our research, we created a survey based on MIT’s AI Maturity model to create a survey that measures AI adoption /AI maturity and leadership outcomes (levels of DAC) within an organization. We hoped to learn:

• What do organizations seek to gain by using / integrating AI? (This gets at shared Direction.)
• How will organizations and teams work together to effectively leverage AI? (This suggests group Alignment.)
• And how will organizations foster the trust and psychological safety required to achieve the buy-in to integrate AI? (This signals shared Commitment.)

After surveying 406 respondents based in APAC, EMEA, and the Americas, we found that DAC was a strong and significant predictor of higher levels of AI maturity. In other words, it’s fair to suggest that organizations need high levels of shared leadership to progress along their AI maturity journey, from Stage 1 to Stage 4.

Recommendations for Building Stronger AI Maturity With DAC

While the research doesn’t show causation (we can’t say for certain that increasing your organization’s DAC will automatically make AI integration easier), we can say that without high levels of shared Direction, Alignment, and Commitment at your organization, your chances of successfully moving up the stages of AI maturity are much lower.

So, how can leaders help their organizations foster strong DAC, particularly as it relates to improving their organization’s AI maturity?

• To increase shared Direction: Clearly communicate how AI will empower the business strategy through value creation, innovation, and impact across the organization. Seek out ways to help teams leverage both AI and soft skills to help them thrive.
• To facilitate more Alignment: Ensure leaders, teams, and systems coordinate in how to leverage AI, creating shared priorities and eliminating silos. To do this, explore what method of governance would work best for your organization. For instance, you could explore a shared decision-making model where overall AI usage across your organization is governed by a cross-functional team. Or, you could have shared policies but a decentralized AI governance structure, where individual functions oversee their own AI usage but align to shared organizational policies.
• To support greater Commitment: Foster psychological safety, continuous learning, and a growth mindset to empower your organization to embrace AI-driven change. Consider how AI and culture impact each other. By helping your organization embrace a culture that prioritizes continuous learning, you can help shift your organization to one that can best embrace and leverage what AI can enable.

Embracing Leadership for Greater AI Maturity

Leveraging AI within organizations requires more than just technological adoption; it demands robust leadership, characterized by our Direction – Alignment – Commitment (DAC) framework. Our research underscores the critical role DAC plays in progressing through the stages of AI maturity, revealing that high levels of DAC are strongly correlated with advanced AI integration.

Furthermore, MIT found that financial performance generally improves as an organization moves through the 4 stages of AI maturity as well, which further emphasizes the value of strong shared Direction, Alignment, and Commitment in navigating AI transformation.

By clearly communicating AI’s value, ensuring coordinated efforts across teams, and fostering a culture of psychological safety and continuous learning, your organization can not only enhance DAC levels and strengthen the outcomes of leadership, but increase in AI maturity — and thrive in an era of complexity and uncertainty.

Ready to Take the Next Step?

Ready to help leaders at your organization understand how to become more effective in setting direction, building commitment, and creating alignment to support greater AI maturity? Partner with us to craft a customized learning journey using our research-based modules. Available leadership topics include Boundary Spanning, Communication, Conflict Resolution, the DAC Framework for Effective Leadership, Emotional Intelligence, Listening to Understand, Psychological Safety, and more.

The post Why Leadership Is Important for Organizational AI Maturity appeared first on CCL.

The key to successful AI integration across the organization is helping leaders at all levels understand how their use of AI connects to the organization’s collective mission. One way to achieve this is by assisting leaders in understanding the soft skills required to thrive during AI transformation.

Why Soft Skills Are Important for AI Transformation

In the context of leadership, soft skills are key qualities like empathy, compassion, and authenticity that help us form strong connections with others. These skills are just as important, if not more so, than technical skills, such as the ability to use AI. It’s tempting for organizations to think leveraging AI and soft skills means helping all their employees understand how to use AI to work more efficiently in their roles. While AI advancements can significantly enhance operational efficiency and help individuals uncover new insights, they cannot replace the uniquely human aspects of leadership.

At CCL, we believe that human leadership will guide and shape the future. The true strength of leadership lies in the uniquely human qualities that AI cannot replicate, such as empathy, vision, curiosity, and the ability to inspire others. Understanding the relationship between AI and soft skills is crucial for leaders to effectively harness AI’s potential while maintaining a human-centered approach.

The value of leadership soft skills extends beyond individual interactions; they’re essential for navigating complex challenges, fostering innovation, and building resilient teams. Collectively leveraging these soft skills across the organization is an essential factor in successfully navigating AI transformation. While leaders at all levels require a foundational understanding of AI, and some soft skills are important regardless of level, the essential soft skills for leveraging AI look different at different levels.

Senior Leaders: Guiding Ethics, Innovation & Vision

Senior leaders are often charged with designing a strategy in alignment with their organization’s mission, vision, and values. They’re also guiding the organization through uncharted territory. AI transformation is causing rapid change, and senior leaders play a key role in helping their organizations both navigate this change and thrive amidst it. Here are 4 key soft skills senior leaders need to guide their organizations through AI transformation.

• Communication: Senior leaders must drive clear and transparent communication about AI initiatives, goals, and integration. Such transparency helps foster a culture of psychological safety and builds commitment throughout the organization by helping the employees (or everyone) understand the collective vision of why AI transformation is essential.
• Trust: Senior leaders build trust by explaining the benefits, limitations, and implications of AI to stakeholders. This vulnerability can signal to the rest of the organization that “we’re in this together” and build buy-in for key initiatives.
• Ethics: Senior leaders must champion ethical AI practices in their organizations, and they serve as the role models for the rest of the organization’s leaders to follow. Organizations that lack clear ethical guidelines for AI risk eroding trust, inviting bias or misuse, and undermining both employee and public confidence in their leadership and decisions.
• Learning Agility: Senior leaders must cultivate a culture of continuous learning and innovation within their organizations by modeling the traits and behaviors they seek. By creating opportunities for skill development and recognizing learning-oriented behaviors, they also influence others in the organization to experiment and innovate, further shifting organizational culture.

Managers: Translating Strategy & Execution

Managers bridge the gaps between strategic direction and operational reality. Middle managers often find themselves pulled in multiple directions — upward toward senior leaders, sideways toward peers, and downward toward direct reports — so interpersonal skills like clear communication, influence, and collaboration become as critical as technical competence. Here are the 4 skills managers need to best leverage AI transformation.

• Collaboration: Managers need to navigate organizational politics and structures to connect AI potential with strategic goals. To achieve this, they must lead with empathy and adaptability, understanding their organization’s AI strategy, how it will reshape workflows and operations, and foster collaborative and productive working relationships between and across teams and functions.
• Communication: Managers must ensure clear communication about AI’s role and implications to employees to build trust and psychological safety. Serving as the bridge between individual contributors and senior leadership, they help foster understanding and collaboration across organizational boundaries.
• Learning Agility: Managers must continually identify opportunities for their teams where AI can enhance efficiency and productivity. By being adaptable, and helping model that adaptability for their teams, they can quickly integrate AI into existing workflows or spot opportunities for creating new workflows.
• Influence: Managers must encourage teams to explore AI tools and foster a psychologically safe environment for innovation. They should leverage their influence to build consensus and drive commitment toward adopting AI technologies in an ethical and productive way.

Individual Contributors: Innovators Inspired by AI 

Individual contributors are at the frontline of AI transformation. They’re often the first to integrate AI into their everyday work, and they’re experimenting with ways to do more and do better with AI. Individual contributors play a vital role in shaping strategy and executing AI initiatives, yet they often lack the communication, influence, and self-awareness skills required to translate their expertise into broader impact. Here are the key soft skills individual contributors need to best navigate AI transformation:

• Learning Agility: Individual contributors need to invest in personal AI literacy — understanding AI’s capabilities, AI tools, and how to take full advantage of AI to enhance their current role. Embracing learning agility can give these contributors the versatility, adaptability, and growth mindset to best leverage AI.
• Creativity: Individual contributors can immediately leverage AI to enhance their creativity. For example, they can use AI to augment problem-solving, facilitate brainstorming, and spur innovative thinking by exploring new ideas (or working with AI to challenge existing thinking).
• Resilience: While AI can be empowering, it can also be a threat in terms of replacing roles. For individual contributors, building a resilient mindset can help navigate this uncertainty — they can do this by leveraging AI to amplify their own skills as well as helping others remain resilient and be ready for what AI trends emerge next. This requires individuals to challenge and refine AI-generated output to ensure relevance and reliability.
• Collaboration: Individual contributors can serve as educators in their organizations, helping others understand terminology, promote ethical usage, and identify when to and when not to best leverage AI in the flow of work. Turning AI into a collaborative tool in your organization can enhance impact at multiple levels: individuals, teams, and the organization.

Navigating AI Transformation — A Leadership Imperative

To thrive amid AI transformation, leaders must embrace AI as an essential tool while cultivating the soft skills that define effective leadership. AI can certainly provide productivity gains and organizational efficiencies, but it’s not a substitute for the essential human qualities that make up good leadership. Whether you are a senior leader, manager, or individual contributor, understanding and developing these skills will enable you to navigate AI transformation by bolstering both your own individual performance and your organizational impact.

When leaders at all levels leverage soft skills along with AI capabilities, their organizations can best harness AI’s potential. Embrace this opportunity to grow and lead with AI, ensuring you and your organization are ready for the future.

Ready to Take the Next Step?

We have a number of leadership solutions to help you upskill your talent with soft skill development, in the format that’s best for your unique situation.

The post Essential Soft Skills to Lead Through AI Transformation appeared first on CCL.

The missing link isn’t technology, it’s culture: helping people understand how their individual contributions connect to the organization’s collective mission, especially as AI changes what work looks like.

But what do we mean by culture?

Culture is the self-reinforcing web of beliefs, practices, and behaviors that drive how leaders and organizations make decisions and the way things get done. In short, culture makes strategy happen. For AI integration, understanding the relationship between AI & culture change is vitally important.

Why Is Culture Change Necessary for AI Integration

Successful AI adoption across an organization requires a collaborative culture. When individuals use AI in isolation, productivity gains stay isolated. When teams use AI collaboratively — sharing insights, challenging outputs, building on one another’s work — the impact compounds. That shift happens when senior leaders intentionally shape the culture to support AI integration.

We’ve spent decades researching organizational culture change, and our experience has given us insight into how organizations can successfully move toward more interdependent, collaborative ways of working that are better positioned to leverage AI’s potential. Senior leaders play a critical role: they model the behaviors, set the expectations, and create the conditions where interdependent leadership culture takes root.

In previous research, we identified 5 principles that increase the likelihood of successful culture change. Here, we apply those principles to help senior leaders shift workplace culture to enable effective AI integration.

5 Principles for Shifting Culture to Effectively Integrate AI

1. Culture change is a guided, public-learning process.

Senior leaders architect the organization’s AI strategy and play a pivotal role in aligning AI efforts with the organization’s mission, vision, and values. But strategy alone doesn’t drive adoption; transparency does. As senior leaders adopt AI, they must embrace transparency, openly communicate what’s working and what isn’t, and learn from missteps.

AI adoption creates uncertainty. Workflows change. Roles evolve. People worry about relevance. When senior leaders publicly navigate that uncertainty — sharing their own experiments, setbacks, and adjustments — they signal that it’s safe for others to do the same.

What this means for your organization: Be transparent about how AI is adopted and used.

• Clearly communicate guardrails for AI use.
• Model behavior by being open and vulnerable about what you and your organization are learning about AI, sharing personal successes / failures with AI.
• Keep messaging about AI’s role aligned to your organization’s mission.

By fostering a culture of open experimentation and communication, you can both proactively model the culture change needed and create an environment where it can thrive.

2. Senior leaders must do the change work first.

Our research of nearly 300 leaders over 2.5 years showed that teams with high degrees of psychological safety reported higher levels of performance and lower levels of interpersonal conflict. For AI adoption, creating psychological safety at work is critical: people need to feel safe with experimenting with new tools, admitting when they don’t understand how AI works, and challenging AI outputs without fear of judgment.

Senior leaders create that safety by going first. When they model new behaviors — using AI transparently, sharing their learning process, admitting when they need to adjust — they signal that experimentation is welcome. The rest of the organization watches what leaders do, not just what they say.

What this means for your organization: Model psychological safety and drive change by emphasizing 3 key areas: resilience, experimentation, and accountability.

• For resilience, help your organization understand how to weather disruption, whether that’s because of the impact of AI or the leadership needed to navigate polycrisis — the web of interconnected, interrelated challenges we face today.
• For experimentation, create space for new and potentially wild ideas, fostering a learning culture that’s willing to take risks and learn from mistakes.
• For accountability, take responsibility for integrating AI throughout the organization and be willing to admit when adjustments are needed.

3. Developing vertical capability transforms your leadership culture.

Individual AI skills matter — knowing how to ethically use the tools, write effective prompts, and validate outputs. But organizational AI adoption requires something deeper: a culture where leaders think differently, not just work differently.

This is called vertical development. It means developing more complex and sophisticated ways of thinking, greater wisdom, and clearer insights. It involves gaining new perspectives and leadership mindsets needed to make your organizational strategy work.

Without vertical development, leaders optimize their own productivity but miss how AI could transform collaboration, innovation, or strategy execution across the organization. They see AI as a personal efficiency tool, not as a lever for organizational change.

What this means for your organization: Develop and encourage the mindset to ask bigger questions.

• How does AI change how we collaborate?
• How do we balance individual AI experimentation with organizational alignment?
• What does it mean to lead when AI is reshaping workflows and roles?

Vertical development gives leaders the capacity to navigate these questions, which is especially helpful during culture change — not with perfect answers, but with the sophistication to hold complexity and guide the organization through it.

4.  Leadership culture changes by advancing beliefs and practices simultaneously.

Real cultural shifts come from understanding how beliefs and behaviors shape and reinforce each other. New beliefs lead to new practices, which in turn reinforce or reshape beliefs, creating a continuous cycle. Senior leaders play a pivotal role in connecting and maintaining this cycle for their organizations.

What this means for your organization: You probably hear a range of beliefs about AI. Some leaders are skeptical, others see it as useful, and some view it as essential to productivity. Many leaders may already be integrating AI into their work and championing it to colleagues. But true cultural growth is unlikely to occur unless senior leaders harness the relationship between belief and action.

• Start with belief barriers: What explicit or implicit beliefs are holding your organization back? For example, does your organization have a culture of “not my problem” around certain issues or change initiatives? Do leaders view AI as someone else’s responsibility — IT’s job or the innovation team’s project — rather than a shared strategic priority?
• Then shift practices: If the belief is “AI isn’t my concern,” create practices that make it everyone’s concern. Require senior leaders to share how they’re using AI in team meetings. Build AI experimentation into strategic planning sessions. Make collective AI learning part of leadership development.

When beliefs and practices shift together, they reinforce each other. Leaders who experiment with AI develop new beliefs about its potential. Leaders who believe in AI’s strategic value create new practices to leverage it. The cycle compounds.

5. Managing culture change is a learn-as-you-go process, embedded in the work of the organization.

Organizations that want to adopt AI effectively need an agile, reflective approach to understand how AI is impacting the organization and what opportunities it creates. The same is true for culture change — it takes time, develops unevenly, and can’t be forced. Continuous learning is essential for navigating both, showing up at multiple levels:

• Individual: Leaders develop AI literacy, test and learn with new tools, and share insights.
• Team: Teams figure out how AI changes collaboration and innovation, and experiment with new processes.
• Organizational: The organization develops governance models, decides where to centralize vs. decentralize AI adoption, and adjusts strategy based on what’s working and what isn’t.

What this means for your organization: Ask questions and use the answers to derive deeper insights: 

• How will your organization adapt to the impact of AI at different levels?
• How will it adopt AI strategically?
• What governance models will it develop to effectively harness AI across functions?
• Will a decentralized approach, where each function best determines how to incorporate AI into its work, be more appropriate than an organization-wide model?

Organizations committed to continuous learning will be more prepared to tackle these questions, learn from successes and missteps, and apply those lessons to future decisions.

From Strategy to Action: Integrating AI for Organizational Impact

Effectively integrating AI in your organization requires a leadership development strategy that connects individual leader performance to collective achievement. By pursuing a strategic approach to leadership that adopts AI as a transformative tool across individuals, teams, and the organization, you can expand mindsets, foster innovation, and propel organizational success.

Ready to Take the Next Step?

If you and the rest of the senior leadership team are ready to start transforming your organization, partner with the experts in our Organizational Leadership practice to assess the effectiveness of the executive team, evaluate your current and needed future leadership culture, and ensure it supports your business strategy and priorities.

The post How AI & Culture Intersect: 5 Principles for Senior Leaders appeared first on CCL.