The Shift No One Is Managing (Yet)

Dr. Marilyn Carroll explores how AI is shifting organizational decision-making from human authority to emergent system behavior. She argues that weekly check-ins are becoming necessary to monitor risks that traditional strategy can no longer control. Let’s write a post description to go with the Podcast 

#Artificial Intelligence

#Technology Integration

#AIinEducation

#AIforProductivity

#Digital Transformation

#Workforce Development

#Future of Work

Transcript

SPEAKER_00 0:00

So, Maren, I've been thinking about this idea of weekly AI check-ins, right? And the the host of the podcast, Dr. Marilyn Carroll, she talks about how it's not just about agility, which is what most people are seeing. She's saying it's something else entirely. What do you um what do you make of that?

SPEAKER_01 0:18

Yeah, it's it's interesting because on the surface, it absolutely looks like agility. I mean, you've got these short conversations, 15 minutes, sometimes less, where teams are asking, what did the system do this week? Where did it perform well? Where did it drift? What surprised us? And you'd think, oh, okay, that's just, you know, being being more agile, being more responsive. But she makes this really crucial point that it's organizations trying to keep up with systems that are no longer behaving in predictable ways. Does that does that make sense?

SPEAKER_00 0:50

Mm-hmm. Yeah, it's it's almost like um we've had this framework for so long where, I mean, we treat transformation like a project. You define the strategy, you roll out the tools, you train people, you measure adoption. And she's saying that model worked when systems were stable, but AI systems aren't. They're they're changing based on data inputs, usage patterns, context, how people interact with them. So the moment you finalize a strategy, you've already lost alignment. It's like trying to hit a moving target with a with a fixed arrow.

SPEAKER_01 1:24

Exactly. That's a great point. And that's why these weekly check-ins are emerging. They're not replacing strategy because leaders don't care about strategy, they're replacing strategy because strategy alone can't keep up anymore. We what we're seeing is that the systems themselves are dynamic, and our traditional uh strategic planning cycles are just too slow to adapt to that.

SPEAKER_00 1:46

Right. And then she gets to what she calls the real issue, which I found really profound. It's not about learning, it's not about productivity, it's this question: do we still understand how decisions are being made inside our own organization? Because once AI starts influencing recommendations, approvals, prioritization, customer interactions, you start to lose visibility. And when that visibility drops, authority starts to shift, not intentionally, not maliciously, but structurally.

SPEAKER_01 2:18

Yeah, from humans to systems, from explicit rules to probabilistic outputs, from governed decisions to emergent behavior. And that's uh that's a huge shift that I think a lot of people are maybe not fully grasping the implications of yet.

SPEAKER_00 2:33

Yeah, I mean, it's almost like you're you're you're giving away the keys to the car without realizing you've done it. And then she goes into this gap that nobody talks about. She says weekly check-ins create awareness, but awareness is not governance. Could you uh unpack that a little bit?

SPEAKER_01 2:50

Yeah, she says it again. Awareness is not governance. And it's because asking what happened this week is very different from asking, was that decision authorized to happen in the first place? And most organizations cannot answer that second question. Not in real time, not at the moment of execution. So you can be aware of what the system did, but that doesn't mean you have control over whether it should have done it or if it was within the boundaries you set.

SPEAKER_00 3:17

It's like knowing what your kid did after they've already done it versus actually setting the rules beforehand. And this isn't happening in isolation. She points out signals everywhere: leadership teams and middle managers not aligned on AI priorities, education systems experimenting with AI policy from the ground up, research showing promise, but still very limited evidence. All of this points to one thing. We are integrating AI into decision making faster than we are redesigning authority.

SPEAKER_01 3:47

Yeah, and that gap is where the real risk lives. That's that's the core problem here. We're putting these systems in place that are making decisions, influencing decisions, but we haven't updated our framework for who has the authority to make those decisions or what those boundaries are.

SPEAKER_00 4:04

So what does this mean for leaders? I mean, she says weekly AI check-ins are a good start, keep them, but don't stop there. You have to move from observing system behavior to governing system behavior at the moment of action. What does that practically look like?

SPEAKER_01 4:20

That means asking who has the authority to act, under what conditions, what are the boundaries, and what happens when those boundaries change? Because if the system is making or influencing decisions, those answers have to be built into the system itself. It's not enough to just watch it. You have to embed that governance within the actual system's operation.

SPEAKER_00 4:41

So the organizations that win in this next phase of AI, it won't be the ones that learn the fastest, but the ones that govern learning as it happens. The weekly check ins aren't the destination. They're the signal that leadership is beginning to recognize something deeper. AI is not just changing work, it's changing decision authority. And that, uh, that's a leadership responsibility. It's a significant shift in how we think about um our roles.