There are two questions I’ve been thinking about almost nonstop lately:

1. In an era where you can do nearly everything with AI, what do you prioritize?

2. What is the hard problem you’re actually trying to solve?

I’ve written about the first question from a couple of different angles. Here, I argue that when output is abundant, differentiation comes from decision quality. And here, I argue that when options are effectively limitless, the most valuable skill a leader can develop is the ability to choose.

The more I sit with question number one, the more convinced I become that prioritization—how you decide what gets your time, resources, and attention—is becoming one of the clearest leading indicators of success. Knowing what to do—and when to do it—has always mattered. It’s just more consequential now.

But how do you actually answer the prioritization question? How do you decide what deserves focus?

Well, that’s where the second question comes in.

The Filter Question

By focusing on the second question—what is the hard problem you’re trying to solve?—you’ll inadvertently answer the first. The second question is the filter that turns “we could do anything” into “we should do this.”

On the surface, it sounds fairly straightforward. In practice, it’s not. Here’s an example.

Let's say I’m currently working with an organization that rents specialized equipment. They’re feeling pressure everywhere: more competitors, more equipment in market, more pricing pressure, more noise. The things that used to differentiate them don’t seem to differentiate as much anymore. Revenue is down. Margins have tightened. Win rates are slipping. Utilization is flat.

So what’s the hard problem we need to solve?

Each of those metrics is a reasonable target. We could run initiatives to increase revenue, improve margins, lift win rates, or boost utilization. But in this case, focusing on any of those directly would be a mistake, because they’re downstream outcomes.

They’re signals, to be sure, but they’re not the hard problem.

The Real Problem

As I dug into the customer’s buying path, one issue kept surfacing before price, before freight costs, before relationships—before anything: availability. Before a customer can choose which provider to rent from, they have to believe the company has the equipment they need, when they need it.

And even that’s not as simple as it sounds. Availability isn’t a static “yes/no.” It’s dynamic. Job schedules move. Dates change. Needs shift. What matters isn’t just whether a unit exists somewhere in the fleet—it’s whether the customer can see availability clearly enough to plan, and whether the rental partner can respond to shifting timelines without creating surprises.

So the hard problem becomes something like this: How do we make availability visible, dependable, and easy to act on?

If we solve that—if customers can reliably understand what’s available, when, and under what conditions, we don’t just improve one metric. We improve all of them. Win rates improve because we’re in the deal earlier and more often. Margin pressure eases because we’re competing on certainty, not discounts. Utilization rises because planning gets tighter and dead time drops. Revenue follows because the funnel stops leaking at the first decision gate.

This is what the second question does. It turns a scattered set of “problems” into one clear priority. And it’s the leadership team’s job to name it, operationalize it, and protect it, so the organization’s speed is applied where it actually compounds.

How to Find the Hard Problem

If you’re leading a business or team right now, you probably have no shortage of “problems” you could work on. The goal isn’t to deny those problems; it’s to find the one constraint that precedes the others.

Here’s a simple way to do it:

1. Start with the outcomes you want to change. Revenue, margin, win rate, utilization, cycle time, retention. These are the signals telling you performance is drifting.

2. Trace those outcomes back to the earliest decision gate. Where does the customer’s decision actually start? What has to be true before price even matters? Before preference matters? Before the “sales conversation” really begins?

3. Name the constraint in operational terms. Not “we need better marketing” or “we need growth.” Name what must become easier, faster, clearer, or more reliable, because it’s the bottleneck the rest of the system runs through.

When you get it right, it will be specific enough to act on, but foundational enough that solving it would lift multiple metrics at once. And this is where AI changes the stakes. If you haven’t named the hard problem, AI will happily help you produce an impressive volume of work that doesn’t matter. If you have named it, AI becomes a force multiplier—compressing time-to-insight, accelerating execution, and increasing your organization’s ability to run fast in the right direction.

So start there: name the hard problem. Then let that problem decide what deserves priority.

Everything else is just noise.