At this point in our collective AI journey, one thing is clear: AI is an output machine. Whether your output is words, code, images, plans, strategies, products—or even AI agents—the output is immense. Never before have we been able to produce so much, so quickly. In fact, we even had to invent a new term for it: workslop.

The issue with so much output is that it’s a problem masquerading as a benefit. The barrier to entry to create almost anything is now so low—and the volume is so high—that output often obscures the real question we should be asking: What outcome are we actually trying to achieve?

If you don’t answer that first, your output is almost certainly irrelevant. It might be impressive. It might even be “good.” But it’ll be good in the wrong direction.

Strategy Still Requires a Human

At the risk of sounding like a self-help guru, the key ingredient to achieving an outcome is found in you—in your perspective, your understanding of your company, your understanding of your team. It’s found in your ability to assess and discern whether an output will actually lead to an outcome, or whether the output is just noise.

Here’s what this looks like in practice.

Let’s say you want to launch a marketing campaign in 2026. You’re excited to move into a new market, so you open ChatGPT and ask for campaign ideas. It gives you five. You ask for more and it gives you fifty. At first, it feels like momentum, but then the ideas start to blur together. So you narrow the list to a couple you like, pick one, and start building.

You create a strategy document. Then a schedule. Then email copy, social posts, ad banners, blog content. Maybe even a few print ads, just to round it out. Pretty soon, you’ve got more deliverables than you know what to do with—and you’ve lost the plot. The work looks good, and it might even be good, but somewhere in the flood of outputs, you forget the point.

Because the point was never “create a campaign.” The point was to acquire customers in a new market. And customer acquisition doesn’t begin with deliverables. It begins with understanding the customer—what they value, what they fear, what they already believe, what would make them switch, and what would make them trust you. If you skip that part, your output may be high quality, but it will still be the wrong output entirely.

This is what AI has changed. Not that we can create more—we already knew we would. What’s changed is that our ability to pause, discern, and think is now more important than ever. Because when you can generate almost anything in almost no time at all, knowing what not to create becomes a strategic advantage.

So before you think about your output, get ruthless about your outcome. Name it. Define it. Put numbers on it if you can. Then work backwards from there.

Because in 2026, the advantage won’t go to those who can generate.

It’ll go to those who can choose.