Should Leaders Be Hands-On with AI? Rethinking Leadership in a Transformative Era

Organizational leaders often make their most significant impact not through what they can do with specific tools but through their ability to vision, delegate, make strategic decisions, and hold teams accountable. This is a hallmark of leadership. The higher you move up the organizational chart, the less hands-on technical work you find and the more focus there is on decision-making and oversight. Conversely, as you move lower in an organization, employees tend to make their impact through what they can do—writing code, building systems, analyzing data, or solving problems.

Take, for example, a senior vice president who may struggle to format slides in PowerPoint or to connect his/her laptop to a projector, but excels in steering cross-departmental collaboration, managing risk, and ensuring accountability for high-stakes deliverables. On the other hand, a mid-level manager or specialist might make his/her impact by delivering the work itself—whether that’s running financial models, writing software, or troubleshooting issues. Both roles are critical, and this division of responsibility has long been accepted as a functional truth.

However, generative AI may be challenging this narrative. The rapid evolution and transformative potential of AI have raised an important question: Should high-level leaders remain detached from the technical understanding of AI, or does this technology demand that leaders have at least a foundational grasp of its capabilities and mechanics?

Why AI May Require a New Leadership Mindset

AI is unlike many other tools leaders have encountered in their careers. It’s not just another technology; it’s a paradigm-shifting force capable of disrupting industries, automating processes, and changing how we think about work itself. This is particularly true for generative AI, which is already impacting a huge range of fields including marketing, education, healthcare, and software development.

The concern arises when leaders with significant positional authority are tasked with making decisions about AI adoption and policy without any hands-on experience or understanding of the technology. A 2022 survey from MIT Sloan Management Review found that while 85% of executives believe AI will transform their businesses, only 20% report a high level of comfort working with AI (source: https://sloanreview.mit.edu/projects/the-cultural-benefits-of-artificial-intelligence-in-the-enterprise/?utm). This gap highlights a troubling reality: Many leaders are making decisions and policies about a transformative technology they don’t understand.

This isn’t to suggest that leaders need to become AI experts or take on the technical responsibilities of their teams. But given the stakes, leaders may need a baseline understanding of what AI can and cannot do. Without it, they risk making overly restrictive, under-informed, or overly optimistic decisions that can cary gigantic consequences.

The Power of Foundational Understanding

Why does this matter? Leaders who lack even a cursory understanding of AI may inadvertently stifle innovation or allocate resources poorly. We have generally accepted the "I'm not really techy" vernacular from established leaders for years, and I suppose it is mostly inconsequential (though it always bugs me to sit in a board room and watch multiple leaders with 6 or 7 figure salaries struggle to select HDMI 2 for their laptop connection). However, with pace of change in generative AI, I wonder if this trend will ultimately stifle growth for some and create a wild competitive advantage for others. For example, a leader who views AI solely as a cost-cutting tool may miss opportunities to enhance creativity or improve customer experiences. Similarly, leaders who overestimate AI’s capabilities may overpromise results, leaving their teams to grapple with unrealistic expectations.

On the flip side, leaders with a foundational understanding of AI are better equipped to ask the right questions, evaluate opportunities, and empower their teams. They don’t need to write code or build models themselves, but they probably do need enough knowledge to assess risks, allocate resources wisely, and set realistic goals.

Consider this example: In a Deloitte survey on AI adoption, 40% of respondents identified the lack of AI expertise among decision-makers as a significant barrier to implementation (source: https://www2.deloitte.com/us/en/pages/consulting/articles/state-of-generative-ai-in-enterprise.html?utm. Organizations that train their leaders to understand AI’s potential—and its limitations—are more likely to succeed in harnessing the technology effectively. For a long time, many leaders have gotten away with general apathy toward learning emerging (and sometimes basic) technologies, but AI may disrupt this idea fundamentally.

Does This Mean the Leadership Model Needs to Change?

Generative AI raises another intriguing question: Is the traditional hierarchical org chart still fit for purpose? Historically, technical expertise has generally been concentrated lower in the hierarchy, while decision-making authority sits at the top. But as technologies like AI increasingly influence strategy, organizations may need to rethink this dynamic.

One possible approach is to build more collaborative leadership models where technical experts and high-level leaders work closely together. In this model, leaders remain focused on vision and decision-making but also engage deeply with their technical teams to understand how tools like AI can align with organizational goals.

Another possibility is encouraging "curious leadership." Leaders don’t need to master the tools, but they should actively engage with them. Experimenting with generative AI tools like ChatGPT or Perplexity , for example, can provide leaders with firsthand insight into the technology’s strengths and limitations.

Moving Forward: Questions Worth Asking

As I wrestle with these ideas, I find myself coming back to a few key questions:

• How much technical understanding should leaders have to make effective decisions about AI--especially when crafting organizational policies?

• Can curiosity and collaboration bridge the gap between technical expertise and leadership?

• Does generative AI signal the need for a fundamental shift in how organizations approach leadership?

I don’t have definitive answers, but I’m curious to hear your thoughts. As leaders, innovators, and educators, how do we ensure that organizations can thrive in this transformative era of AI?