One of the most common reactions to AI is also one of the most telling: “It’s fine… but it’s kind of generic.”

The outputs are clean. Grammatically correct. Even directionally helpful. But they lack depth. They lack specificity. And they don’t feel particularly tailored to your situation.

That’s not a limitation of the tool—it’s a reflection of how it’s being used.

AI Doesn’t Work Like Traditional Tools

Part of the disconnect comes from how people think AI works. Most software tools we’ve used for years—Excel, Word, PowerPoint—have predefined capabilities. Their functions are fixed. You input data, and the tool performs a specific, predictable action.

AI doesn’t work that way. It combines what it was trained on with the context you provide and generates something in between. That means the output isn’t fixed—it’s shaped by the interaction with you.

Which also means the quality of the output is highly dependent on the quality of the input.

The Real Issue With AI Isn’t the Output—It’s the Input

AI will respond to whatever you give it:
If the input is broad, the output will be broad.
If the input is vague, the output will be generic.

That’s where most teams get stuck. They’re interacting with AI the way they would a search engine: short request, minimal context, expectation of a complete answer. Then AI fills in the gaps the only way it can—by generalizing.

What Better Teams Do Differently Working With AI

The teams getting real value from AI approach it differently. They don’t just ask for answers—they set it up to succeed. That usually means being more intentional about a few key things, making sure to tell AI the answers to the following when providing your prompt:

– Role – Who should the AI “be” in this scenario?
– Goal – What are you actually trying to accomplish?
– Audience – Who is this for?
– Context – What information matters here?
– Output – What does a useful response look like?

This also helps you to be clear on what it is you’re asking for; to provide that level of input, you have to know what you’re actually trying to do.

One additional shift we’re seeing with more advanced users is how they interact with AI after the initial prompt. Instead of expecting a perfect answer on the first try, they use AI as part of a back-and-forth process. This might look like:

• Asking AI what additional information would improve the response 
• Having it identify gaps or assumptions in the original input 
• Prompting it to ask follow-up questions before providing a final answer 
• Or even asking it to challenge the direction entirely

In other words, they’re not just giving AI instructions—they’re collaborating with it. That tends to produce much stronger outcomes, because it forces more complete thinking on both sides of the interaction.

This Isn’t About Writing Better Prompts

It’s easy to reduce this to: “You just need to learn how to prompt better”, but that oversimplifies it. The real skill isn’t typing better instructions—it’s thinking more clearly, by clarifying the problem, defining success, understanding what information matters, and communicating that effectively. If the thinking is vague, the output will be too.

Why This Matters More Than It Seems

When teams rely on generic outputs, ideas start to sound the same, strategies become less differentiated, and decisions become easier—but not necessarily better. This can create the illusion of progress: things move faster, but not necessarily forward.

The Bottom Line

AI isn’t inherently generic. But it will produce generic results if it’s given generic input.

And as these tools become more widely used, the real differentiator won’t be access to AI—it will be how effectively it’s used.

Closing Thought

If your experience with AI has felt underwhelming so far, you’re not alone. In many cases, it’s not the tool—it’s how it’s being applied.

It’s an evolving skill set, and one that’s becoming more important quickly. If your team is working through what that looks like, you’re not alone.

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