After the initial wave of excitement around AI, most teams are now asking a more practical question: Where does this actually add value? Because while using AI is easy, using it well is something else entirely.

The difference isn’t between teams that are using AI and those that aren’t. It’s between teams that are using it intentionally and those that are using it indiscriminately.

The Biggest Mistake: Treating AI Like It Has the Answers

One of the most common patterns is using AI as a shortcut to conclusions.

Ask a question → get an answer → move forward.

When it’s used this way, you tend to get generic recommendations, overconfident conclusions, and outputs that reflect the prompt more than reality. Over time, that leads to an erosion of strategic quality.

AI wasn’t designed to replace thinking—it works best when it’s pushing your thinking further.

Where AI Actually Adds Value

Used well, AI isn’t a decision-maker—it’s a thinking partner. It can act like a virtual roundtable of perspectives, helping you expand, challenge, and organize your thinking.

AI can be particularly effective in these three areas:

1. Generating Possibilities

AI is excellent at helping you explore options you may not have considered. Instead of asking for “the answer,” strong teams use it to widen the field.

Example: Instead of asking AI to write your email or proposal, you ask it to generate 5–7 different approaches based on different priorities or audiences. The value isn’t in choosing one—it’s in seeing options you may not have initially considered, and using that as a starting point for your own thinking.

2. Refining and Stress-Testing Ideas

Once you have a direction, AI becomes more valuable as a challenger than a creator.

Example: You can upload an idea or point of view and ask AI to poke holes in it—what assumptions you’re making, what perspectives might be missing, or how different stakeholders might react. You can even have it respond as if it were a CFO, a customer, or an internal team member.

This is where AI helps remove blind spots—especially when you’re too close to the work.

3. Structuring and Organizing Information

AI is also very effective at turning unstructured thinking into something more usable.

Example: After a brainstorming session or rough draft, you can have AI organize the information into a clear framework, summarize key themes, or structure it into a more polished outline. Instead of starting from a blank page, you’re refining something that already exists.

Where AI Falls Short

AI struggles in areas that require context, prioritization, and real-world experience.

It doesn’t know what’s changed internally, what conversations have already happened, or what matters most to your organization. It can’t fully understand tradeoffs or long-term implications.

And without that context, even strong outputs can lead in the wrong direction.

The Shift: From Doing the Work to Directing It

AI doesn’t eliminate the work—it changes where the value sits.

Historically, a lot of time has gone into execution: building, analyzing, refining. AI can now handle pieces of that faster and at greater scale. But that doesn’t reduce the need for expertise—it raises the bar for it.

Because someone still has to decide what the right problem is, provide the context AI doesn’t have, interpret what comes back, and ultimately make the call.

That’s where the value is shifting. AI can play the instruments, but you’re still responsible for how the song comes together.

What This Looks Like in Practice

In practice, strong teams don’t hand work off to AI—they work alongside it. They use it to explore options before deciding, to challenge their own thinking, and to organize ideas more efficiently. But they remain firmly in control of the strategy, the decisions, and the outcomes.

AI is involved in the process—but not in charge of it.

The Bottom Line

AI can absolutely improve speed, efficiency, and output quality. But it is only as good as the context you give it, and needs to be used with thoughtfulness and intention.

In practice, that often comes down to how you prompt it. Vague inputs lead to generic outputs, while specific inputs—clear goals, audience, and context—lead to something much more useful. That’s one of the biggest gaps we see today.

The teams seeing the most value aren’t the ones using it the most. They’re the ones using it most thoughtfully.

Closing Thought

We’re continuing to test where AI meaningfully improves how we work, interact, plan, analyze, and execute—and where it doesn’t.

If your team is working through similar questions, you’re not alone. It’s an evolving space—and one worth navigating thoughtfully. We’re always happy to compare notes.

BY KRISTEN ORTON Based on learnings from the AI Masterclass Certification, “AI Fundamentals: Think, Create, and Work Smarter,” and real-world application, with support from AI tools like ChatGPT.

This is part of a 5-part series on using AI. Read the other articles by clicking below:

Article 1: AI is Changing the Work It Is Not Replacing It
Article 2: AI Is Confident Even When It’s Incorrect. Here’s What That Means For You
Article 3: Using AI Is Easy, Using It Well Is Not
Article 4: What AI Can’t Replace and Why That Matters
Article 5: Why Many AI Results Feel Generic and What Better Teams Do Differently