What Becomes More Valuable Videos

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If you're trying to figure out use cases for AI, you're going the wrong direction.

Most people approach AI by asking, what can AI do here? How can I use AI? Can AI help me? Executives do it, managers do it, teams do it. It's logical, but it's the wrong lens.

The better question is, where is the bottleneck? Where are you waiting? Where are people confused? Where does work slow down? Where do things get lost? Where is clarity missing? Where is rework high? Where do people spend time on things that don't actually matter?

AI is not about sprinkling capabilities across your workload. It's about removing friction from the places that slow everything else down.

When you find a bottleneck, you'll find leverage. And when you apply AI to leverage, the impact is disproportionate.

So stop searching for use cases. Start identifying bottlenecks, and the AI fits naturally once you understand where the friction is.

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So if the hard part isn’t doing the work, then deciding what work to do becomes the hard part.

AI makes producing work faster, cheaper, easier, which moves the bottleneck. The constraint becomes about organizing, not just executing.

Because when the cost of making something drops, everyone makes more, right? There’s more ideas, more code, more reports. But now you have a flood of output, and who decides what gets shipped, who integrates it, who makes sure it works across teams?

And that’s where the bottleneck goes as coordination. That’s where the leverage point is.

For example, cloud computing made launching software cheaper, but the winners weren’t just the ones with servers. They were the ones with faster decisions and better products, and tighter feedback loops.

So if you’re thinking AI will give you an edge just by producing more, you’re wrong. The edge comes from how you organize all that output.

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So why hasn’t AI made productivity skyrocket overnight? It’s made creating emails, research, code cheap, but the hard part is connecting the dots, coordination across teams and systems and decisions.

More output floods in. There’s more drafts, more ideas, more analysis, more code. But someone has to prioritize it, someone has to integrate it, someone has to decide what matters.

And that coordination is expensive. It slows everything down if you don’t have the right system.

So you have an engineering team, right, and they’re using AI to turn out code. They’re likely producing much more code than they were before, but now you have more code to manage.

And if reviewing, merging, testing, and shipping it still moves at the old pace, you haven’t removed your bottleneck, you’ve shifted it downstream.

So if two different companies are using the exact same AI tools, and one thrives while the other stalls, the difference is who coordinates better.

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So when everyone has powerful tools, what actually sets you apart?

When execution is easy, your competitive advantage shifts to the systems that organize and coordinate that execution.

If anyone can produce research and analysis and code, capability gaps shrink, and the advantage moves to how you integrate workflow systems, feedback loops, and decision-making processes.

Those systems are harder to copy, and that’s where the competitive advantage is.

If we think about two competing startups, they’re both using Claude Code. They’re both using the same skills. Who wins?

It’s the one with the better process.

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Technology changes constantly, and the people who succeed are the ones who learn the fastest. That is the ultimate competitive advantage. AI lets you experiment faster. You can try ideas, test approaches, ask questions, iterate.

But learning only happens if you actually try something. See what happens and learn from it. And here’s the easiest way to see this for yourself. Think of something you don’t know how to do, something useful or not.

Open a model, explain what you’re trying to accomplish, give it some context, and start working through it. Do that consistently, and you’ll learn faster than the environment changes. And learning is a huge advantage.

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Last week, my team at Sagepath Reply and I ran an AI enablement workshop with one of our clients using Optimizely Opal. Opal is Optimizely’s agentic marketing platform that sits on top of the Optimizely ecosystem and integrates really well with their tools.

We don’t start by talking about AI at all. We start by opening a FigJam board and listing what the teams responsible for campaigns, trade shows, email marketing, website content, and product launches do. Then we break those responsibilities down into smaller, individual tasks underneath them, like writing emails, building landing pages, or preparing for trade shows.

Even within those, there are smaller tasks. If you’re building a landing page, you need to create copy and assets, consider the audience, personalize, and analyze click-through rate and marketing uplift.

Once that’s mostly mapped out, we start asking questions. Which of these things are repetitive? Which happen constantly? Which are tedious? Those become our first candidates for automation because nobody in the room is trying to replace marketers. We’re trying to look at the process of marketing, break it down, analyze it, and then rebuild that process in a more efficient way.

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So when we’re running these AI enablement sessions, once we’ve identified the first candidates for automation, we start building Opal agents to handle them. And things like landing pages, campaign emails, website updates, they’re good candidates because they’re pretty structured, they’re repeatable, and they never really stop coming.

And so as we start talking about automating those tasks, people don’t really ask, okay, well what work is left for me? It’s pretty obvious what work is left for them. What they start asking is, what work’s never really been possible before, that is now?

So what if competitor research wasn’t something you did once a quarter, or once a year, or never? What if voice of customer analysis was always current, instead of based on the last survey that you did, which was who knows how long ago? What if every campaign started with current market research, instead of whatever you were able to get together, in the schedule that was allotted for it?

Before AI, a lot of that work wasn’t economical. You couldn’t justify spending days researching every competitor before every campaign or continuously analyzing customer feedback. The time just wasn’t there.

AI changes that equation. Work that used to be too expensive or too slow, or too tedious becomes practical.

And so, AI is not just helping marketing teams finish the same work faster. It’s changing what a marketing team is capable of doing.

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So by the end of the workshop, we’d already started building some automations, and what stuck with me was how the conversation changed. Instead of asking, how do we save time, people started asking, what should we do with the time we get back? And the answer is not really work less, sorry. It was run more campaigns, experiment more often, spend more time understanding your customers, continuously analyze competitors instead of just reviewing them every few months, invest more time in figuring out what’s actually working instead of just simply producing more marketing assets.

And it’s a completely different way of running a marketing organization. AI changes the economics of work, and as that happens, the work that creates the most value changes too. And that’s why I keep saying AI automates tasks, not roles. The tasks change, the role gets redesigned, and I think that’s one of the biggest opportunities that AI creates.

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Someone I talked with this past week described an interesting use case involving feature flags. They have this recurring process where several different feature flags, that are split across a few different systems, need to be turned on or off. And accuracy matters because if you miss some of them, it means the process wasn’t completed correctly.

So he built a tool to handle it, and nobody’s job disappeared. The person responsible for the task still owns the outcome. The process just became faster, more reliable, and easier to maintain.

And I think that’s a useful way to look for AI use cases. You find a point of friction. You look for a task that happens repeatedly, follows understandable rules, and produces a result you can verify. And then you automate that task. You make the person doing the job more capable, you learn from it, and then you find the next point of friction.

You don’t have to start by transforming your entire company. You can start by making one frustrating part of someone’s job work better.