Why "Go Experiment with AI" Fails as an AI Adoption Strategy

Welcome back to Untangled. It’s written by me, ​​Charley Johnson​​, and valued by ​​members​​ like you.

This week I talked with ​Jen Briselli​, co-founder of ​Topology​ and an adjunct professor at Massachusetts College of Art and Design. Jen is one of my favorite thinkers on complexity, and she’s written two essays in the last six months that I keep returning to: one on ​what hockey taught her about complexity​, and one arguing that ​“uncertainty tolerance”​ doesn’t live inside a person.

We discuss:

* Why “skate to where the puck is going” is worse advice than it sounds.

* What a triangle of three forwards can teach an organization about rules.

* Why “you’re not in traffic, you are traffic” is the hardest lesson in complexity work.

* Jen’s image of organizations amputating healthy limbs to make room for a prosthetic -- and why it’s an apt way to think about AI adoption.

As always, please send me feedback on today’s post by replying to this email. I read and respond to every note.

On to the show!

Untangled HQ

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Deep Dive

Uncertainty tolerance is not a personality trait

Jen wrote ​her essay on uncertainty tolerance​ because she was fed up with ​headlines​ telling leaders that their teams can’t handle ambiguity. She argues that people actually love uncertainty. We gamble, we play sports, and we get mad at anyone who spoils the end of a movie. What people resist is uncertainty that comes with high consequences and no influence over the outcome. If those consequences reach someone’s salary, Jen said, “I don’t blame someone for being a little bit uptight about it.”

The point, as Jen argues, is to stop treating uncertainty tolerance as a trait that a person has or lacks. Tolerance emerges from the entanglement between a person and their context. The same person who bluffs happily at a poker table on Saturday can freeze during the announced reorg on Monday. Nothing inside that person changed over the weekend. The stakes changed, and so did how much say they have.

Why employees hesitate to experiment with AI

Now map this distinction on to being instructed by your boss to “go experiment with AI.” The generous read might be that they don’t know how to strategically incorporate it into a workflow, and they want to involve you in the process. A li’l experimentation can’t hurt, right?

The less generous interpretation is that management is asking staff to figure out what parts of their job can be automated — and they’re being asked to fork over that information with no sense of how it’s going to be used. Read this way, your hesitation makes sense. Why would you experiment with AI if success means you may have just shown your employer how to automate part of your job? When leadership calls that hesitation “resistance,” they locate inside you something that you and they produced together.

How individual AI experimentation becomes a team problem

The other issue with this instruction is that “go experiment with AI” asks you to act. It says nothing about whether you’ll ever learn what your action set in motion — as an individual or a team. This cuts against one of the core ideas in ​Jen’s hockey essay​: never act faster than you learn. Take actions that are, in her words, high in learning potential and low in disaster potential, and sense for what those actions changed. So if your experimenting faster than your learning, slow down.

Now comes the hard part: doing this as a team. Because teams that don’t sense and learn together develop problematic patterns. It’s rarely because someone has bad intentions or doesn’t care about their impact on the group. It’s because everyone is moving fast and trying new things. It feels like they’re saving time and being more efficient. But it turns out that each individual, working alone, is making what seems like a small accommodation: deferring to a confident output instead of forming their own view, starting from the AI’s framing instead of their own, letting the time it saves get absorbed as more volume. Within teams, these small accommodations snowball. They build and build until they become a patterned dynamic — judgment that no longer gets exercised, disagreements that no longer get voiced, accountability no longer exercised. And no one notices, because action outpaced learning and the action felt productive.

What hockey systems teach leaders about AI adoption

Hockey has something to offer here, too: hockey teams operate in systems, not plays. As Jen explained, 1-2-2 forecheck tells almost no one what to do. It tells the first forward to pressure the puck, the two behind her which lanes to hold, and the two behind them what they own if the puck gets through. Everyone can see where everyone else is, and the decision belongs to whoever is closest to the puck. The first forward can skate hard at a risky puck because two layers behind her make losing it recoverable. Her uncertainty is tolerable because of where her teammates are standing. “Go experiment with AI” is the opposite of a system. It says nothing about where anyone stands in relation to anyone else or what layer is responsible for covering the person who takes the risk.

So what’s a better approach than ‘go experiment’? First, say plainly what will and won’t happen to someone’s role if they find a way to automate part of it, and keep your word. Second, help people see the system: protected time where people from different teams compare what they tried, what they learned, what they’re sensing and observing, what they would change, etc.

This isn’t a moment for individual experimentation in a vacuum. It’s a moment for collective sense-making and learning. The technology is moving faster than any one person can grapple with one their own. But we can put in place team and organizational processes that attend to emerging patterns and allow groups to adapt together.

So the next time a leader says “go experiment with AI,” the right response is to ask, where do you want me to stand, and who’s behind me?

Until next time,

Charley

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