Briefing #36: The Permission Nobody’s Giving
Instead of pursuing an AI strategy, some organizations are performing one. And some AI leaders might not be doing the right thing, even if they’re doing things the right way. Those are not the same.
I recently worked with a leader who, in every conceivable way, is tack-sharp, smart, and utterly capable. She’s the kind of leader that every organization wishes they had. A role model that her organization genuinely looks up to. Because of her star status, this leader had found herself under enormous pressure from executive leadership to show that she was leading the way on AI adoption in her organization.
In a show of that leadership, she had an important presentation coming up, and she decided to use AI to build a slide deck. She gave it a rough outline and loved what AI came back with. On the surface, it was astonishingly polished and thorough. “I can’t believe the number of hours AI has saved me,” she proudly shared with me. “Building slide decks is the bane of my existence.”
A week or so passed, and the time to deliver her presentation came and went. We spoke the day after. The expression on her face spoke volumes — utterly crestfallen.
She recounted how she walked to the podium, confident that she had everything well-in-hand. And how it all started to come apart from there. She wasn’t sure how to transition from one slide to the next. Each slide was so full of messaging that she wasn’t altogether clear what she wanted to say in the moment. And her delivery fell well beneath what she knew she was capable of.
She asked me what she could have done better, whether there was something she should have prompted the AI to do that she missed.
What I shared was that her workflow deserved a full look. When asked what she had done following the preparation of the slide deck, we got into what actually got missed: the refinement to make the presentation hers.
See, in having AI build the deck, the friction of deciding what deserves to be on each slide, what needed to be cut, and what she really wanted her audience to take away, was never addressed. She had skipped the most important part of public speaking preparation: rehearsal.
AI had automated the preparation, and in so doing, it also let her skip the work.
What got automated wasn’t the task
I share this story because this leader’s experience with AI has become increasingly common, and it’s become one of the reasons why some leaders have come to believe that “AI slop” is the only kind of output that AI is capable of. Not necessarily because the outputs are bad, but because the outputs were deployed without the human work that should have surrounded them.
Many organizations are eager to adopt AI in the belief that “saving time” or “eliminating manual work” through automation is the key to optimizing productivity.
AI is indeed very good at removing friction. What it can’t tell you is whether that friction was doing something you needed.
Business leaders often carry the intuitive view that friction is where the waste is. Eliminate the friction and we eliminate what keeps the business from marching forward. And sometimes that’s true.
What might be counterintuitive is that there are times when friction is actually load-bearing.
Friction is also where understanding gets built, and how sound judgment is formed. Where leaders stay connected to work in ways that matter. When that friction is automated away, you might save time. But, you might also lose something in the process — and you often won’t know what’s gone missing until you need it.
At Brilliant, I still review business expenses by hand every month. Not because I couldn’t automate the analysis; I could, and I do, with AI. But I do set aside the time to look at every line myself and with my team, and we ask: what was this for? Did it make sense? Should we keep doing it?
Some might say that process is inefficient. But, it’s also how I stay connected to the judgment calls we’re making as a business. An automated summary might give me the numbers, but it’s the manual review that gives me the understanding.
These are different things.
It’s “AI-first,” not “AI-everywhere.”
“Compliance” is how I’d describe what many organizations I encounter are doing with AI right now.
And I don’t mean in the regulatory sense of the word. What I mean is that organizations are responding to their Boards, who are asking about AI, and their senior leaders who are mandating it.
Somewhere in the middle are a lot of capable people who are nodding, executing, and performing visible alignment to AI, all while privately wondering whether any of it is producing what they were told it should.
Spend enough time with me and you’ll hear me remark on a distinction that I first heard years ago and come back to often:
Leaders do the right thing. Managers do things the right way.
In the context of AI adoption, a lot of people who ought to be leading are managing instead. They’re executing the directive well, but they’re not asking whether the directive is right.
That’s a distinction with an organizational cost because it’s one that goes beyond culture and influences final results.
But isn’t AI different?
I know what some readers will be thinking at this point.
But AI is transformative. It’s everywhere. I’m using it all the time. You can’t afford to hold back.
And in a way, you’d be right. Consider the Internet. In 1995, it was hard to build a financial model that justified Internet adoption. The ROI wasn’t always obvious. The metrics didn’t exist. And yet the organizations that held back because they couldn’t see the numbers paid for it for decades.
But unfettered ubiquity wasn’t how organizations adopted the Internet.
Those companies who found the most success in the dot com era weren’t necessarily the ones who put the Internet everywhere, into every process, as fast as possible. They were the ones asking a much harder question: where does this genuinely change what we can do, and where doesn’t it?
That’s a question that demands deliberation. It requires leaders to say “Here, and not here. This, and not for that.” Even when the boardroom wants to know how much and how soon.
AI is no different. One is leadership and the other is management.
What does AI leadership look like?
Many accountable AI leaders will be asked in the days ahead, if they haven’t already, to show how AI is driving business results.
And that’s where some important AI decisions will need to be made. It could be tempting to think that the decision is where AI must be most urgently deployed. But, I’d suggest that the most important question is actually the converse: where must AI deliberately not?
Don’t look at this stance as a retreat from AI, because it’s emphatically not. It’s actually the exercise of strategic judgment in its most powerful form. Not what an organization wants to do, but what it won’t do.
It starts with leaders being honest, with their organizations and with themselves, about the difference between what AI can really do to drive results and where it just gets in the way of achieving results.
Call those places where AI gets in the way your organization’s “no-fly zones.” These are the processes, practices, and decisions where deliberate human judgment produces something that speed and automation can’t replicate. And not because AI can’t replicate the outputs, because it often can. But because the act of doing it yourself is doing something that matters beyond the output being created.
Good leaders have clear no-fly zones for AI. They can name them, defend them, and they’ll resist the pressure to automate them away in the name of moving fast. They have the courage that every organization desires, even if they don’t know it yet.
You already know which parts of your work matter most when you do them yourself. You already sense where your organization is going through the motions. You already have a view on what would be lost if certain things got handed to a model.
That instinct is worth something, maybe more than you know. Act on it.
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