Are You a Leader in the Loop—or Are You Just Along for the Ride?

Are You a Leader in the Loop—or Are You Just Along for the Ride?

For the past few years, much of the conversation about AI at work has focused on the technology—what it can do, how quickly it is advancing, and where it can make us more productive.

But as the conversation shifts from adoption to broader deployment and integration of AI into our day-to-day, an equally, if not more important question is: What does good leadership look like when AI becomes part of everyday work?

There is no question that AI can make us faster. It can help us generate ideas, synthesize information, prepare for conversations, and get to a first answer remarkably quickly. But faster doesn’t always mean better.

One of the things that emerged in Blanchard’s research is how quickly and easily people appear to be moving from using AI to relying on it. An answer appears quickly, sounds confident, and generally makes sense. So we move on.

And that’s where leadership becomes critical.

Being “In the Loop” Isn’t Enough

The phrase human in the loop emerged quickly as a balm to early—and still very present—concerns about AI. “Don’t worry, there will still be a human in the loop.” And increasingly, we hear some version of leader in the loop.

But what does that actually mean?

If being “in the loop” simply means knowing what’s going on, that sounds suspiciously passive to me. A leader can know AI was used, see the output, and give it a quick nod of approval. They may technically be in the loop, but I’m not sure they are leading.

Maybe we need leaders to do more than be in the loop.

Leaders Need to Build the Loop

Good leaders have always made connections. They connect what a customer is saying to what a team is doing. They bring someone into a conversation because that person sees something others don’t. They connect what happened yesterday to the decision being made today and what it might mean tomorrow.

AI gives us more strands to work with—more information, ideas, possibilities, and recommendations. But someone still needs to weave those strands together. Leaders bring the context, experience, and different perspectives that turn all that information into something useful.

For example, imagine a leadership team trying to understand why sales have declined for three consecutive quarters. They give AI access to sales data, customer feedback, competitive information, and market trends. Within minutes, it identifies several likely causes and recommends shifting resources toward the highest-performing customer segments.

It’s a compelling analysis. But instead of simply approving the recommendation, the sales leader starts pulling on the threads. What are we hearing from salespeople in the field? Are we losing existing customers or simply winning fewer new ones? Is the decline concentrated in certain regions, products, or types of accounts?

That leads to conversations with frontline sellers and customers, and a new possible cause for declining sales emerges. A recent change in the sales process has made it harder for reps to respond quickly to opportunities. Now the team has a cause the AI analysis didn’t uncover.

AI helped identify patterns and possibilities. The leader made sure the team didn’t mistake a plausible answer for a definitive answer.

Leaders Need to Close the Loop

An AI-generated answer shouldn’t be the end of the thinking process. Leaders need to follow it through. What problem are we actually solving? What assumptions are we making? What might be missing? Does this make sense in our context? And if we act on it, what happens next?

Closing the loop means connecting information to judgment, judgment to action, and action back to learning. AI can contribute at every point, but the leader has to make sure those connections actually happen.

Leaders Need to Break the Loop

AI also makes it remarkably easy to keep feeding a line of thinking back into itself. Ask a question, get an answer, refine it, ask again, and receive something even closer to what you expected. Before long, we can find ourselves in a very sophisticated feedback loop with our own assumptions.

Sometimes leadership means interrupting that cycle. Bring in another person. Ask the opposite question. Talk to the customer. Look for evidence that challenges the recommendation. Ask what we might be missing instead of simply making the answer we already have better.

Leaders Still Own the Loop

None of this means leaders need to become AI experts. But they do need to remain active participants in the thinking.

That means asking people not just “what did AI tell you?” but also “what do you think?” It means making sure important context hasn’t been lost, creating a little friction before a consequential decision, and helping people use AI without slowly giving up their own ability to think through a problem.

Ultimately, it means owning what happens next. AI can analyze, generate, recommend, and challenge. The leader still owns the decision.

These aren’t new leadership capabilities. Judgment, curiosity, critical thinking, coaching, and accountability have always mattered. What AI changes is how deliberately leaders may need to use them.

So maybe the question isn’t whether our leaders are “in the loop.” Maybe we need leaders who know how to build the loop, close the loop, break the loop, and ultimately own the loop.

Because as AI gets better at doing the work, one of the most important questions for organizations may be:

What do our leaders need to get better at doing?