Why Most of Us Stay Stuck at “AI Curious”

TLDR: You’ve bookmarked the tools, read the guides, saved the prompt libraries. But your team still hasn’t delivered a single working AI use case. This post breaks down why content consumption stalls progress, and how to shift from passive learning to applied capability using structured frameworks like IDEA and CARE.

AI Content ≠ Capability

Content consumption rewards scanning, not thinking. It fragments understanding across 50 tools, 10 frameworks, and a dozen contradictory diagrams.

It also creates false confidence. Teams mistake exposure for readiness. They believe reading about an AI tool means they can spot where it fits. It doesn’t.

They need a system for translating knowledge into action. One that constrains thinking enough to make decisions, not just generate ideas. 

Inside both my client engagements and Masterclass, we use IDEA and CARE frameworks. But not as theory. They’re applied to live problems, in real-time, with actual team workflows.

IDEA helps scope and select:

• What’s the actual result we want?
  
• Where does the data live, and is it usable?
  
• What’s the current effort required?
  
• Is this automatable, or are we forcing it?
  

CARE comes after, to communicate it:

• What’s broken in plain terms?
  
• What action will we trial?
  
• What outcome tells us if it worked?
  
• What does the prototype or workflow look like?
  

In one session, I had a student group map out a staff onboarding process they thought could be improved with AI.
At first, they went straight to tools: “Could we use Claude to answer FAQs?” “What if we build a chatbot for HR?”

We pulled them back. Used IDEA to reframe.
What was the actual problem? Delays in IT provisioning and missed manual steps in role-based onboarding.

The data existed. The effort was high. And yes, the task was rules-based and repeatable. But it wasn’t an LLM problem. It was a workflow orchestration issue.

So we prototyped something simpler: a no-code agent that pulls role data, triggers app provisioning, and logs task status for HR. No chatbot. No prompt library. Just the right solution for the real friction.

That shift from tool-first to outcome led is what most professionals never make. Because they’ve never been taught how.

One AI Use Case, One Change

A client in marketing ops spent months sharing AI articles internally. They had a shared folder of 40+ tools, 12 prompt guides, and two pages of AI notes in Notion. Still, no change in how the team worked.

We sat with them. Took one repeated task: campaign reporting. Scoped it using IDEA. Applied CARE to make the test legible to the team. In less than two weeks, they weren’t talking about AI. They were using it to summarise cross-platform reports with a 70 percent time saving.

This is a pattern I’ve seen again and again, whether with leadership teams or university students in applied innovation programs. Progress comes from a method to trial something grounded.

FAQ

Q: I’ve collected great resources: why hasn’t anything changed?
A: Because collecting isn’t the same as applying. You need a structured way to trial ideas and link them to real outcomes.

Q: Can I use IDEA and CARE without technical skills?
A: Yes. These frameworks are designed to help you think clearly about the problem and opportunity, not build the solution yourself. We use them in our Masterclass to help non-technical professionals go from first principles to functional use cases.

Q: What’s the first move?
A: Pick a repetitive task with clear inputs and outputs. Use IDEA to see if automation fits. If it does, write up a CARE summary and test it in no-code.

 So....start building capability. You don’t need another roundup. You need to learn how to think structurally. How to test small. How to assess fit before you pick a tool. That’s what we teach inside the AI Fundamentals Masterclass. Because whether we are working with public sector teams or postgrads, the issue is never access to AI. It’s the lack of method to use it ......and to use it....well.