What fascinated me wasn’t just the technology. It was what Waymo made me rethink about AI, organizational design, and how organizations make decisions and work.
Last summer in Orlando, my wife, our two daughters, and I took our first ride in a Waymo.
For the first couple of minutes, I couldn’t stop watching.
Every turn. Every stop. Every pedestrian. Every lane change.
I found myself looking at the steering wheel, mostly because no one was holding it.
Then something unexpected happened.
Without even realizing it, I stopped paying attention to the car.
We talked. We laughed. We took pictures.
The technology quietly faded into the background. Not because it became less remarkable, but because it became normal.
A few days later, I found myself thinking about that ride again.
At first, I was interested in what it said about autonomous driving. Over time, though, I became much more interested in what it might tell us about organizations.
I now use Waymo as one of several examples in conversations with boards and executive teams about AI.
Almost every time, someone smiles and asks some version of:
“You actually put your family in one of those?”
So we spend a minute on autonomous driving and why I felt comfortable doing it.
But the car isn’t really the point.
The point is that we already have systems making decisions, responding to changing conditions, and taking action within defined boundaries.
A Waymo doesn’t stop to ask someone whether it should change lanes. It doesn’t wait for approval before every turn or stop.
People designed the system. They determined its objectives, boundaries, and what should happen when the system encounters something it cannot handle.
But once the car is operating, thousands of decisions happen without someone approving each one.
That makes something that can otherwise feel abstract much more tangible.
What happens when more systems inside organizations can do the same?
That question initially led me to think about decision-making.
What should people decide?
What can systems decide and act on?
Where should they stop?
When should something be escalated?
And who remains responsible for the consequences?
Those questions still matter. But the more I have used the Waymo example with senior teams, the more I think there are two distinct conversations we need to be having.
AI and strategy: What becomes possible now?
Most AI discussions still begin with existing work.
Where can we automate?
Where can we save time?
Which tasks can AI perform?
Those questions matter.
But they start with the organization as it exists today.
The bigger strategic question is whether new capabilities change what the organization could do in the first place.
What could customers come to expect that would be difficult for us to deliver with the way we operate today?
What could we offer at a scale, speed, level of personalization, or cost that previously made little economic sense?
Where might value shift in our industry if capabilities that were once scarce become widely available?
What would a new competitor build differently if it were starting today?
Those questions move AI out of the technology conversation and into strategy.
Because a new capability becomes strategically important when it changes an assumption that shaped earlier choices.
Maybe expertise was expensive.
Maybe personalization could not be done economically at scale.
Maybe thousands of routine decisions had to move through people because there was no other reliable way to make them.
Maybe coordination required layers of management because information moved slowly and unevenly.
When one of those constraints changes, the question is no longer simply:
“How can we use AI?”
It becomes:
What choices would we make differently if this constraint no longer existed?
That is a much harder question.
It is also where I think some of the biggest opportunities will emerge.
AI and operating models: What should change on Monday?
During one executive conversation, after we had spent considerable time on the longer-term implications, a CEO asked a question I loved:
“This gives us a lot to think about strategically. What, if anything, should we start doing differently on Monday?”
That question matters too.
We shouldn’t reduce the strategic conversation to a collection of immediate AI use cases. But big-picture thinking should not become an excuse for waiting until the future is clearer.
There is plenty to reconsider now.
Where are customers waiting because of the way our internal processes work?
Where are capable people spending time chasing information, reconciling systems, moving data from one place to another, or waiting for approval?
What decisions are made repeatedly under similar conditions?
Where does work move through multiple people simply because that is how we have always coordinated it?
Where could clearer boundaries and better information allow decisions to happen closer to the work?
Those questions often reveal something important.
A surprising amount of organizational friction exists not because anyone designed it deliberately, but because earlier constraints made it necessary.
People had to move information.
People had to connect departments.
People had to remember context.
People had to approve exceptions.
People had to coordinate decisions because the underlying systems could not.
As those capabilities change, some of what we have learned to accept as normal deserves another look.
That does not mean removing people wherever technology can act.
It means asking whether the way the work is organized still makes sense.
And it means getting much clearer about where human judgment matters most.
Greater autonomy does not eliminate leadership responsibility. It changes where that responsibility sits.
Leaders increasingly have to think not only about individual decisions, but about the systems through which decisions get made.
What are the objectives?
What are the boundaries?
What can happen autonomously?
What requires judgment?
What gets escalated?
Who is accountable when the system acts?
That, to me, is one of the more consequential shifts AI may bring to organizations: not simply faster work or fewer tasks, but a different way of thinking about how work, decisions, authority, and accountability fit together.
AI and organizational design: We don’t need to predict the future to begin
I don’t know how quickly autonomous systems will spread across organizations.
Neither does anyone else.
And I don’t think leaders need to predict exactly what their organization will look like five or ten years from now.
But they do need to recognize when a constraint that shaped yesterday’s organization is no longer as binding.
Waymo is useful to me for a different reason.
It is evidence that something that once seemed implausible is already possible. And once something becomes possible, it is worth asking which assumptions we should reconsider.
My Waymo ride didn’t give me a prediction about the future of organizations.
It gave me a better question for thinking about them:
What becomes possible for us now that we could not seriously consider before?