AI Leadership Skills: Seven Essential Competencies for the AI-Enabled Leader

1. Resilience

The capacity to maintain composure during challenging times and recover from setbacks. 

In an AI-driven era where the pace of change is exponentially faster, resilience becomes a cornerstone of effective leadership. Change, while essential, is also exhausting — both for leaders and their teams. Too much change, too quickly, can lead to burnout, resistance, and diminished productivity. Imagine a leader navigating a sudden, AI-driven disruption: a previously manual process is automated overnight, causing uncertainty among the workforce.

Leaders must not only adapt themselves but also act as a stabilizing force for their team. Those who do will not only help their team process change constructively, ensuring their teams remain engaged and supported through transitions, but studies show that they will also foster greater innovation.

2. Curiosity

The tendency to recognize and seek out novel and challenging information, ideas, and experiences.

Curiosity is essential for leaders in an AI-driven world, enabling them to explore the full breadth of AI’s capabilities and uncover transformative opportunities. Without it, leaders risk limiting AI to surface-level tasks, missing its potential to drive innovation and competitive advantage. For example, a leader might experiment with generative AI to automate routine tasks in their day-to-day work. All leaders would be satisfied with the time savings. A curious leader, however, would continue to push beyond the obvious application, constantly absorbing new information and asking “what else can this do?”

Equally important is encouraging curiosity in employees. Google famously dedicates 20% of employees’ time to personal projects that interest them, even if those projects aren’t directly related to their core job responsibilities, which has led to groundbreaking innovations. Leaders can take inspiration from this approach by actively encouraging questions, creating safe spaces for experimentation, and celebrating the pursuit of new ideas — even those that don’t immediately succeed.

By embracing curiosity and encouraging it in their employees, leaders not only enhance their own understanding, they also foster a culture of exploration and discovery, inspiring their teams to innovate fearlessly.

3. Strategic Thinking

Understanding the bigger picture of the organization’s current state, where it needs to go, and devising a plan for how it will get there.

Few innovations in history have created as much buzz as AI. Its transformative potential spans every industry, offering unprecedented possibilities for efficiency, innovation, and competitive advantage. However, this vast potential is accompanied by a flood of information, use cases, and potential to chase shiny AI-driven opportunities that may not align with an organization’s core objectives or vision.

Leaders must develop the ability to separate signal from noise, critically evaluating AI applications to identify those that drive real value. Many leaders will implement AI solutions because of their popularity or perceived cutting-edge appeal, only to find that these tools offer little in terms of meaningful business outcomes. Strategic leaders, on the other hand, will prioritize AI investments that align with their long-term business goals, ensuring that the technology serves as an enabler of their broader vision rather than a distraction from it.

This level of discernment requires a deep understanding of both the organization’s strategic priorities and the evolving AI landscape.

Unlike some earlier revolutions in tech, AI does not naturally confine itself to a small set of tactics that can be used to improve business efficiency,” said Dr. Bruce Katz, Chief Innovation Officer at AIIR Analytics. “Instead, when used properly, AI is something that can enable changes at the strategic level. In other words, it is not simply a matter of improving current business practices, but more fundamentally delivering new kinds of value.”

4. Problem-Solving

Synthesizing diverse data sources, analyzing cause-effect relationships, and engaging in critical thinking to make accurate assessments and implement effective solutions.

Problem-solving is critical for addressing the inevitable challenges of AI implementation — connecting vast, disconnected data sources or engineering the perfect prompt to deliver the desired outcome. As previously mentioned, human judgement is essential for successful AI implementation. Leaders must critically evaluate AI outputs, considering the broader context and potential limitations of automated systems. By doing so, they can ensure that AI serves as a tool to enhance decision-making rather than replace the nuanced understanding that human experience brings.

5. Setting Vision

Establishing and communicating a clear and compelling future state, usually connected to organizational advances, and reinforcing it over time.

Human beings are hardwired to resist change. In fact, the default emotional response to the uncertainty and ambiguity is fear, which makes it challenging to cultivate the mindset, conditions, and environment necessary to support a successful change effort. In moments of uncertainty, people look to their leaders. And, in these moments, the ability to communicate a clear and compelling vision can overcome that fear.

For example, a leader introducing an AI adoption initiative might present a roadmap that illustrates how AI tools will be used to eliminate repetitive tasks, freeing employees to focus on more strategic, value-driven work. Instead of framing AI as a replacement, the leader highlights its role as a collaborator, capable of amplifying human expertise. By showcasing tangible benefits—such as faster innovation cycles or improved customer experiences—the leader can inspire enthusiasm and foster a sense of purpose across the organization.

Without a clear and compelling vision, teams may resist change, stalling progress and undermining organizational goals.

6. Decision Quality

Making good judgments by balancing data, experience, and intuition.

Nested in the same Leadership Dimension (Decision-Making) as Curiosity and Problem-Solving, Decision Quality is paramount. In this context, Decision Quality is the ability to synthesize rich AI-generated data with human judgment and intuition to make informed decisions.

While AI offers powerful analytical capabilities and insights, its outputs are only as effective as the leaders interpreting and applying them. Decisions informed by AI must still be evaluated within the broader organizational context, as well as with an awareness of the limitations of the data and models behind them.

Leaders must critically evaluate recommendations by asking questions like:

• Does this align with our broader strategy?
• What factors might the AI not be aware of?
• How do we mitigate potential risks?

By doing so, they can ensure decisions are data-driven yet grounded in real-world context and strategic foresight.