AI Field Notes: The Leadership Gap in the Age of AI

Most leaders who struggle with AI adoption are not struggling because the technology is too complex. They are struggling because they are approaching AI with habits that no longer fit the task. The gap is not technical literacy. It is leadership behavior.

In the first article of this series, I explored why AI feels like cheating and why fear of getting it wrong slows adoption. Those concerns are real. But once leaders decide to try AI, a second problem often appears. They use it the way they use Google, get shallow results, and quietly conclude that AI is not very useful after all.

That conclusion feels reasonable. It is also wrong.

When AI Is Used Like Google

Most leaders are very good at search. They know how to type a few keywords, scan results, and synthesize information on their own. Google has rewarded that behavior for decades. AI does not work the same way.

When leaders type short, search-style prompts into AI, they often receive generic, surface-level answers. Those answers lack focus. They lack context. They rarely reflect the leader’s real need. The experience reinforces an early belief that AI is overhyped or unreliable.

What happens next is predictable. Leaders revert to what they know. They go back to Google, spreadsheets, and manual synthesis. AI becomes something they tried once, not a capability they developed.

This is where the leadership gap begins.

The Real Gap: How Work Is Assigned

Two issues tend to appear together: weak prompt discipline and the absence of prompt patterns. Combined, they form the most common failure point I see.

Leaders do not intentionally assign AI a role. They do not define the task clearly. They do not provide meaningful context. And they expect the first answer to be the final one.

That is not how leaders work with people.

When a leader assigns work to a staff member, they naturally frame the request. They explain the goal. They clarify constraints. They answer follow-up questions. They refine direction as the work takes shape. AI responds to that same structure.

When leaders skip those steps with AI, they are not seeing the limits of the technology. They are seeing the limits of their own instruction.

Why Context Changes Everything

AI performs at the level of the guidance it receives. Assigning a role focuses the model. Providing context narrows the solution space. Clear questions define success.

For example, asking AI to act as an expert travel guide immediately shifts the quality of the response. Adding constraints such as preferred airlines, hotel brands, locations, and timing further sharpens the output. The result is not just faster than a search engine. It is more relevant to the leader’s actual decision.

This is not a trick. It is leadership translated into a new medium.

One-and-Done Thinking Holds Leaders Back

Another pattern reinforces the gap. Leaders expect the first AI response to be complete. When it is not, they assume the tool failed.

In reality, AI is designed for iteration. Follow-up prompts are not corrections. They are clarifications. Asking for refinement, additional context, or alternative approaches mirrors how leaders already think in conversation.

The uncomfortable truth is this: leaders who struggle with AI are often skipping the same behaviors they rely on every day when working with people.

Breaking the Cycle Safely

The fastest way to close this gap is not through high-stakes use cases. It is through low-risk practice.

Planning travel, organizing an event, or brainstorming ideas are ideal starting points. These tasks allow leaders to experiment with role assignment, context, and follow-up without reputational or operational risk.

Another powerful technique is to ask AI to ask questions first. Inviting the model to request clarification slows the interaction just enough to improve quality and confidence. It also reinforces the idea that AI is a collaborator, not a vending machine.

A Leadership Shift, Not a Technical One

AI does not reward better typing. It rewards clearer thinking.

Leaders who take time to define the task, provide context, and engage iteratively see better outcomes. Leaders who treat AI like a search box do not. The difference is not intelligence or experience. It is intentionality.

The leadership gap in the age of AI is closing for those willing to adapt how they assign work. For everyone else, the technology will continue to feel underwhelming.

The choice is not whether AI is useful. The choice is whether leaders are willing to lead differently.