AI is giving organizations the ability to move faster, create more and rethink work in ways that were difficult to imagine just a few years ago. But the speed of the technology creates a leadership challenge: organizations can implement change faster than their people and systems can necessarily absorb it. For executive teams, that makes AI a much bigger conversation than choosing tools or encouraging employees to use them.
That’s why, on this episode of Life + Leadership, I’m joined by John Harden, founder of Lemhigh and a seasoned technology operator who has spent more than 17 years building software and businesses. Today, much of his work focuses on helping organizations navigate AI transformation.
John is also a systems thinker, which made this conversation particularly interesting to me. His perspective is that successful AI transformation requires leaders to look across the organization at the connections between strategy, people, processes, technology, data and change. We talk about why organizations should be thinking beyond AI adoption, what executive teams may be overlooking as they move quickly to implement AI, and why leadership communication becomes even more important as the technology accelerates.
One of the most useful distinctions John makes is between AI adoption and AI absorption. Most organizations understand adoption. We introduce a technology and then look for signs that people are using it. With AI, that might mean measuring licenses, usage, prompts or participation in training. John challenges leaders to think more deeply about the outcome they are trying to create. He describes absorption as AI becoming naturally integrated into the way people work. Employees begin to understand where AI can help, where it has limitations and where human judgment needs to remain in the process.
That changes what success looks like.
An organization could have high AI usage and still have employees using it for low-value work, dozens of disconnected experiments underway and little connection between AI activity and the business strategy. John sees this happen even in highly technical organizations. Because AI lowers the effort required to create something, people can suddenly pursue far more ideas. The executive who once had the capacity to pursue two or three priorities may now feel capable of pursuing ten or twenty. The problem is that starting more work does not eliminate the need to prioritize it. Leaders still have to decide which problems matter, which experiments should scale, which should stop and where the organization should focus its attention.
This is where a systems-thinking approach becomes useful. AI does not enter an organization in isolation. It interacts with existing strategy, priorities, workflows, roles, data and people. The question for executive teams becomes less about how much AI people are using and more about whether AI is improving the way the organization works.
John shared a story about a sales enablement leader who initially tried AI and came away convinced it simply wasn’t for her. With training and support, that changed dramatically. Within six months, she had become one of the strongest AI users in the organization and was building agents that improved the business.
I love this example because it illustrates how easy it is for leaders to misdiagnose resistance. Someone who appears resistant to AI may need training. They may need a use case that feels relevant to their work. They may not know which tools they are allowed to use. Or they may be wondering what will happen to their role if they show leadership that AI can eliminate hours of their current workload.
That last concern deserves serious attention from executive teams. Employees are experiencing AI against a backdrop of constant headlines about automation and workforce reductions. If leaders ask employees to identify processes AI could automate without explaining the organization's intentions, people will fill in the missing information themselves. And fear changes behavior. Employees may protect processes, withhold ideas or avoid experimentation because becoming more efficient feels personally risky.
John emphasizes the importance of executive communication throughout an AI transformation. Employees need to understand the organization's strategy, what leadership expects of them, what they are allowed to do and how AI fits into the future of the business. Leaders may not have every answer yet. Saying that clearly can still provide employees with more confidence than leaving a vacuum.
"The more powerful AI becomes, the more consequential executive leadership becomes."
Executive teams also need to create practical conditions for experimentation. John has used AI hackathons to give employees protected time, clear guardrails and meaningful business problems to solve. He intentionally brings together people who do not normally work together, allowing engineers, marketers, executives and newer employees to approach problems from different perspectives. That cross-functional learning matters because AI capability does not necessarily emerge where leaders expect it to. The person who needs support today may become the person who discovers a powerful new way of working tomorrow.
The further our conversation went, the clearer it became that many of the most important decisions around AI are connected. Take data governance. John gives a simple example: imagine your organization changed its mission, vision and values three years ago, but old versions still exist throughout your systems. An employee asks AI to create something based on the company's mission and values. The AI finds an outdated version and uses it. Then another employee creates something based on that new document. Another AI workflow uses that output. Soon, outdated information has traveled much further through the organization than anyone realizes.
Permissions create another challenge. Historically, information could be technically accessible to an employee while remaining practically difficult to find. AI changes that equation because it can search and retrieve information at tremendous speed. These may sound like technology issues, but they have broader organizational consequences. Executive leaders need to think about the entire system surrounding AI: the quality of the data, who has access to it, which workflows are changing, who owns decisions, how employees are being trained, how experimentation is shared across functions and where human judgment needs to remain. They also need to consider how much change the organization can carry.
John points out that AI can dramatically increase the velocity of work. Organizations can build more, automate more and experiment more quickly. People still have a finite capacity for change. Your employees may be navigating AI alongside a restructuring, a new strategy, leadership changes, new systems, shifting customer expectations and the everyday demands of their jobs. The fact that AI makes ten new initiatives possible does not mean the organization should launch all ten. Part of executive leadership in the AI era will be deciding what to pursue, what to stop and what to deliberately delay.
The more powerful AI becomes, the more consequential executive leadership becomes. Strategy gives AI activity direction. Clear priorities protect the organization from chasing every possibility. Communication gives employees the confidence to experiment. Governance establishes boundaries. Cross-functional learning helps good ideas travel. And thoughtful pacing gives people enough capacity to integrate new ways of working. These elements influence one another, which is why I believe a systems lens is so useful for executive teams navigating AI.
The technology will keep moving. Executive leaders have to create an organization capable of moving with it in a way that is strategic, sustainable and valuable.
Listen to the full episode of Life + Leadership for my conversation with John Harden about AI adoption and absorption, systems thinking, governance, experimentation and the leadership practices that can help organizations turn AI potential into enterprise value.