We spoke about this particular time in business when nobody can say with much confidence what the future state looks like. The technology keeps changing. The use cases keep changing. And our understanding of how the work itself will change is still evolving.

For Pat and Danielle, that doesn’t make change management less important. It makes the human questions harder to avoid.

How do you ask employees to help redesign their jobs when they are wondering whether those jobs will still exist?

What does adoption mean when people are still deciding how much they trust the technology?

What does leadership look like when leaders themselves don’t have the answers?

Dealing with Constant Uncertainty

Traditional change management often assumes some kind of destination. You know where the organization is today. You have a reasonable picture of where it needs to go. Then you help people make the transition.

And while organizations have already learned to manage iterative change through agile methods and continuous releases, AI takes the uncertainty further.

“We have to get comfortable with the fact that with AI, we may not have a perfectly defined future state,” Pat says.

Unlike an ERP implementation, where the destination is reasonably visible, AI is “nebulous.” New capabilities and use cases emerge constantly.

“And frankly, our understanding of how work will change is evolving as well,” adds Pat. She says she finds herself spending less time describing a fixed destination to clients and more time talking about the journey: What are we trying to accomplish? What is the roadmap? How do we help people through it?

The answer to uncertainty may not be greater certainty. It may be getting better at moving without it.

Bigger Questions are Emerging

You can teach someone how to use Copilot or ChatGPT. But that does not mean they understand where AI belongs in their work, when they should use it, when they shouldn’t, or how their job needs to change around it.

“Our clients often mistake adoption for training, but training is not adoption,” says Pat.

“What makes things different now is the human behavior psychology behind it,” adds Danielle.

You can give people tools. You can give managers instructions. But before many employees can fully engage with AI at work, they are processing much bigger questions about what it means for their lives.

“Employees are asking what does this mean for me?” Danielle asks. “How do you be part of AI? And then how do you take that to the workplace? And then what does that mean?”

She pauses at the answer.

“Nobody knows. Literally no one knows.”

Both Danielle and Pat think uncertainty is one of the most important facts for leaders to acknowledge.

Danielle describes a recent client conversation in which the client asked for something she had not heard quite so explicitly before: Help us figure out where AI should be used and where it shouldn’t. What struck her was the willingness of her client to say, in effect, we don’t know either.

The client was saying, “Help us understand how we do it too, because we know nobody knows, but let’s figure this out together.”

AI makes the fundamental choices leaders have always made much more visible.

“How you choose to lead, I think that’s where the person comes back in,” Danielle says. “Do you choose to lead with empathy and learn the tool? Leadership must start making a choice about who they are and…what kind of leader they want to be.”

Danielle believes people can tolerate a surprising amount of uncertainty when they believe the person leading them is genuinely invested in them. “If there is a really strong leader who is trusted and just believed in, and employees don’t know where they’re going, but have a leader who believes in them, I think they are comfortable saying, “I’m okay figuring it out with them.”

The North Star Matters; Purpose Matters

Pat remembered working in pharmaceuticals, where the connection to the mission was unmistakable.

“It didn’t matter what we did, if we were an accountant or someone cleaning the floors. Our mission was to save lives.”

“I think it’s going back to the basics,” she says.

Danielle adds, “If we’re still clear on our mission, then the rest will fall in place because it’s all about the mission.”

Pat and Danielle don’t pretend that mission answers the practical questions about AI. It doesn’t tell an organization which agent to deploy, which role will change or how quickly a workflow should be automated. But it does answer a different question: What are we ultimately trying to do?

Pat describes it as a North Star. She also admits how easy it is to lose sight of it amid constant new capabilities and use cases. “The line of sight to that vision is very blurry.”

Near the end of the conversation, the discussion about mission turns into something even more human.

Danielle adds one more word.

“Purpose.”

“Because that’s what we’re all seeking, right? We’re seeking purpose.”

AI may add to that noise. It may also strip away enough of the work we once used to define ourselves that organizations and individuals are forced to confront those basic things again.

What is my contribution?

What is worth my time?

What is this organization here to do?

What am I here to do?

Pat and Danielle believe human beings are more capable of living with change than we sometimes remember. Pat calls it the need to “survive, adapt, thrive.”

Danielle puts it even more simply, “Isn’t that the root of the human race – to adapt? We’re constantly adapting.”

They both agree that perhaps that is part of the higher purpose too: not preserving work exactly as it has been and not changing it simply because technology allows us to, but helping people find their place, their value and their purpose as the work changes around them.