I use AI as a strategic thinking partner. Not to make decisions for me—that would be abdicating leadership. I use it to expose my own assumptions and clarify my thinking.

The Dialogue Approach

The process: I frame a business challenge. The AI asks questions and makes assumptions. I clarify what I meant. This back-and-forth exposes assumptions I didn’t know I was making.

Last month, I was struggling with a product roadmap decision. I thought I had considered all angles, but through this dialogue, I discovered I was unconsciously prioritizing technical elegance over customer adoption speed. This wasn’t a deliberate choice—it was an unexamined assumption the conversation helped surface.

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How do you distribute your strategy across your organization? A common approach is some form of "goals flow downhill". A leader tells their direct reports what needs to be done. Those reports each figure out how they’ll contribute to the strategy and create goals for their department or team. Then those department goals get passed down to the next rung of the ladder, and sub-goals are created and further passed down, until everyone knows what they’re working on.

The idea is to be able to trace the actions of an organization all the way from the bottom up to the corporate strategy. And to visualize how the strategy cascades into execution throughout the company.

Some OKR guides make this cascade explicit, requiring every one of your KRs to be an Objective for one of your subordinate teams.

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There’s no one-size-fits-all method for prioritizing product work. How you prioritize depends on the state of the company, what problems you’re solving, how you typically solve problems, what your customers expect, and dozens of other factors.

But still, a common question asked of product managers is, "How do you prioritize?"

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A team is running late. Their launch was supposed to be 6 weeks ago. They missed it. They’re still one feature short. What now?

First, dig out of the hole. Then make sure this never happens again.

The Truth About Deadlines

Your date and scope? They’re made up. Even customer commitments in contracts—someone invented those details. You already blew the deadline. Your new date is "ASAP."

That scope? It started vague. It grew as you planned. Now it’s a monster.

Ship dates and feature lists are proxies for value. Nobody cares if you hit the deadline with a useless product. But value is hard to define. It takes time to measure. So people pick dates and features instead. Those feel controllable.

They’re not.

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Venture Capital firms are about to see a shift brought on by the confluence of giant funds and AI. These two forces are going to break the way the venture model works for most firms.

VCs succeed by seeing lots of early stage companies, identifying the ones that have a high chance of being good, and betting that a few will be great. The biggest, most well-known firms get to see a lot of companies. Every founder wants the name recognition of a top-tier investor. These investors have companies coming to them and don’t need to hunt for deals.

Smaller venture firms thrive by building a niche and efficiently hunting within it. These smaller VCs have a contrarian viewpoint that forms their investment thesis. This unique way of looking at the world provides a lens that helps them see potentially-good companies that others miss. They’re able to pick winners from a pool that no one else is targeting.

This is about to change. The giant mega-funds have enough capital that they can A/B test investment theories. They can deploy cash to find out which contrarian viewpoints lead to high returns. But they haven’t, because of two constraints. They can’t create enough viewpoints and they can’t analyze companies against each thesis. But AI changes that.

With AI, a venture firm can generate unlimited investment theses and analyze massive numbers of companies to find matches. If that firm also has a virtually unlimited treasure chest, they can build a niche discovery machine.

It will get harder for smaller VCs to find and exploit these niches. The smaller firm’s advantage was their creativity. But in this case, that creativity is moot if someone can create all of the things and see what works.

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I used to advocate for OKRs for individuals, not just teams. I believed in setting OKRs for everyone. However, I’ve changed my mind, particularly when it comes to cascaded OKRs. You can still set goals for individuals in an OKR-like format (more on that later), but I avoid including individuals as part of the regular OKR process.

The power of OKRs is their ability to link and cascade through the organization, providing everyone with an understanding of how they contribute to the overarching strategy.

In cascading OKRs, it might seem appealing to apply them to individuals as well. It seems like a good way to describe how everyone will contribute to the strategy. However, I’ve come to believe that this cascading should stop at the team level.

A well-crafted OKR describes and measures an outcome, but it can be challenging to create an outcome-focused OKR for an individual. A single person typically can’t generate an outcome independently - outcomes are the result of team efforts. Individual OKRs tend to become output measures. It’s straightforward to gauge an outcome, but surprisingly difficult to objectively assess an output. You can measure that it happened, but measuring how well it was done is subjective.

So, you might end up with a series of "ship this" and "write that" OKRs. It’s tough to naturally link milestones and tasks to outcome-based OKRs.

Furthermore, when an individual has OKRs, it’s tempting to utilize them for performance management. After all, you have a list of things they are supposed to accomplish, so why not evaluate their performance based on whether they accomplished those things? Meeting their OKRs (or not) starts influencing their bonus, salary, or even employment.

This approach undermines one of the key strengths of OKRs: stretch goals.

Stretch goals are magic. If you assign a person a reachable task, they’ll usually accomplish it. If you’re new to running and set a goal to run a mile by the end of the month, you’ll likely do it. That’s an achievable target. But if you set a goal to run five miles, you probably won’t make it. But you’ll run more than one mile. By setting a goal just beyond reach, you’ll progress further than if you simply tried and stopped.

Yet once you start using goals for performance management, you’ve changed the incentives. Achieving and surpassing a goal brings status and monetary rewards like promotions and bonuses. Consequently, people craft goals that are easily attainable, ones they can consistently surpass. They play it safe.

Using OKRs for performance ratings can actually diminish performance. It creates a disincentive for individuals to push themselves.

This is not to suggest that individuals shouldn’t have goals. Everyone should have personal improvement objectives or major initiatives that they’re leading. These typically take the form of milestones or tasks. They might include "learn Javascript" or "deliver higher quality proposals," with KRs describing how these will be evaluated.

But these are not cascades from the overall corporate goals. They’re standard personal goals, simply structured in the format of an OKR. If your company is struggling with OKRs, expressing these goals as OKRs might add confusion. However, if your company is successfully using OKRs, presenting personal goals in an OKR format can enhance understanding.