What Capabilities Do Leaders Need in the Age of AI?

Organizations everywhere are evolving. Artificial Intelligence (AI) is disrupting the workforce and reframing operations at almost every level.

Even leadership is changing, which means senior leaders’ current capabilities may not be enough to support organizations moving forward.

Leaders who don’t evolve risk becoming bottlenecks to innovation and losing ground to those leaders and organizations that learn, adapt and unlock new opportunities and benefits.

In this article, you’ll learn how to identify and develop the skills and capabilities you need to future proof your organization, and your career, in the age of AI.

What’s Different About Leading in the Age of AI?

AI constantly presents new, often complex, opportunities and limitations. As a result, leaders will need to balance a good understanding of AI’s capabilities with a confident approach to problem solving.

This approach is crucial given the host of AI-related concerns that many teams are facing, which range from job displacement to ethical dilemmas. [1]

Leading in the age of AI also comes with the responsibility of managing blurred human-machine boundaries. Those at the forefront can uphold this responsibility by fostering human skills within their teams. AI is much more effective when it complements these skills rather than replaces them. [2]

Additionally, leaders now have access to huge amounts of data and information, which can help with decision making. But it can also complicate it. Facts and figures now vie for attention with human, ethical and moral considerations. [3]

The Core Capabilities of AI-Era Leaders

Various capabilities set leaders apart in the AI era. Here are five capabilities, with real world examples, that you can develop now and for a technology-informed future:

1. Digital Curiosity

AI developments aren’t slowing down, so you need to stay abreast of technological changes and encourage your teams and people to explore ways to test and use AI in ways that could benefit your organization.

Example

Amazon founder, Jeff Bezos, demonstrated digital curiosity through his “Day 1” philosophy, and he credits much of Amazon’s success to this approach.

His “Day 1” approach advocates constant innovation, and everyone treating every day as if it was the company’s first day. [4]

2. Strategic Thinking With an AI Lens

AI may be able to analyze data at scale, but this analysis alone isn’t enough to make the decisions that will inform an organization’s future. With data analysis in hand, leaders can make the decisions that best support an organization’s customers, business model, and competitive advantage.

Example

In 2014, Microsoft CEO Satya Nadella took action based on data that revealed the company's declining PC sales and rising cloud adoption.

He led the company’s pivot to the “mobile-first, cloud-first” strategy, that has retained its position at the forefront of the global tech industry. [5]

3. Ethical and Responsible Leadership

Use of AI raises a host of ethical questions, whether surrounding data privacy, fair hiring practices, or job displacement. As a leader, staying aware of ethical risks and being poised to counter these is paramount.

Responsible leadership practices might start with open conversations about AI’s ethical implications in your organization. You might then be able to delegate the management of these risks to team members, evolving their roles for an AI-ready future.

Example

Sundar Pichai inherited the responsibility of managing numerous ethical considerations when he became Google’s CEO.

He listened to employee concerns, particularly over Google’s involvement in a military AI initiative. He then developed AI ethics guidelines that have protected the company’s technology from being used for harmful purposes. [6]

4. Collaboration Across Boundaries

Leaders are responsible for seamless team collaboration, which can be challenging when some team members are well-versed in AI and others are not.

It may be helpful to use common language and frameworks, so technical and non-technical teams can communicate effectively. This will be especially useful in cross-functional teams, where individuals with diverse expertise support each other.

Example

Former IBM CEO, Ginni Rometty, is known for having nurtured collaboration between technical AI teams and traditional business teams.

She has advocated for technology complementing humanity, encouraging collaboration not just within organizations but also with the government, innovators, and think tanks. [7]

5. Empathetic Communication

AI may be advancing, but it can’t offer empathy as it isn’t capable of intuition, lateral thinking, or emotional intelligence. [8]

Each of these skills contributes to a supportive leadership style that involves connecting on an emotional level to build trusting relationships. These relationships are critical to staff retention and the development of a team that blends humans and AI without fear.

Making empathetic decisions has always been a vital part of effective leadership. However, emotional intelligence carries even more weight in the context of AI leadership, where teams rely on machines as well as humans.

Example

Mary Barra is known for her empathetic leadership at GM Motors. She applies her mantra, “Do the right thing – even when it’s hard,” to every leadership decision, big or small.

She focuses on the consequences of each decision beyond the bottom line, which proved influential when she pivoted the company to an all-electric future. [9]

Skills Signalling Strong Leadership in an AI Context

Beyond the above capabilities, here’s a range of approaches you can put into practice to prepare yourself for AI-ready leadership:

• Ask the right questions.
• Encourage experimentation.
• Stay calm and curious in the face of complexity.
• Model continuous development.

Ask the Right Questions

Whether you’re querying data, bias, or unintended consequences, asking the right questions can stimulate conversations on how to use AI responsibly.

Strong questions can also uncover the relevance and potential of specific AI tools most suited to your organization. [10]

First, ask what could go wrong to identify any blind spots or potential risks. Next, ask how AI could help with the task at hand.

A concrete answer will confirm a genuine need rather than a desire to follow a trend. Lastly, ask how the team could measure success to ensure you can track return on investment.

Encourage Experimentation

AI solutions are unlikely to be their most productive when first implemented, especially if an organization integrates AI into an outdated system or model. [11]

Allowing your team time and resources to iterate will help them to strengthen their proficiency and comfort using AI tools.

This will be key as they gradually uncover the processes that will improve day-to-day operations. [12]

Stay Calm and Curious in the Face of Complexity

AI is likely to deliver setbacks and surprises. When you stay focused on solutions under pressure, you set the tone for the rest of your team.

Nurturing a culture of resilience can help inspire others to figure out problems with you. [13]

Spend a little time reflecting on your current mindset. What are you curious about AI-wise? Where are you resistant to AI change?

Both of your responses should give you ideas to research, discuss and act on as you problem solve with your team.

Model Continuous Development

Given AI’s ever-changing nature, there will always be more to learn. When you model continuous development, you can inspire your team to do the same.

Additionally, you can pass learnings on to others in the organization, whether you’re attending workshops and conferences or reading industry publications. And sharing your learning goals can embed a culture of curiosity and ongoing development.

Consider committing to one personal development goal this quarter to strengthen your understanding of AI. This goal might be related to AI awareness, learning or applications.

You might also join AI or cross-functional learning groups to pick up new information and awareness.

Leadership That Learns Is Leadership That Lasts

Adapting for the AI age might not be easy, but it can be reassuring to know that the latest technologies won’t take your leadership role.

Instead, AI will reward those who evolve by embracing change, continuously learning, and leading with humanity. These are the leaders who will shape the future.

Frequently Asked Questions

What’s the single most important capability for leaders facing AI-driven change?

There’s no single skill, but developing digital curiosity and strategic thinking with an AI lens are critical starting points.

How can leaders encourage teams to embrace AI instead of fearing it?

Normalize experimentation, reward curiosity, and reframe AI as a tool to enhance, not replace, human skills.

How should leaders balance AI data with human intuition in decision making?

Use AI insights to inform decisions, not dictate them. Factor in ethical, emotional and strategic perspectives alongside the data.

What’s a simple first step for a leader wanting to become more AI-ready?

Commit to a personal learning goal this quarter: whether it’s attending a webinar, reading a report, or piloting a small AI project with your team.