The AI Maturity Trap: Why Most Companies Are Stuck at Stage 1

Someone on X shared their company's "AI Transformation Model" -- a framework for assessing where organizations actually are in their AI journey. The responses were revealing. Most people admitted their companies were at the very beginning. Not because they lacked tools. Because they lacked a realistic assessment of where they stood.

I've been using a version of this AI maturity assessment in my own training for the past year, refined across engagements with manufacturers, banks, universities, and retailers. Here's what it looks like, and why most companies are stuck at Stage 1.

The 4-Stage Model

Stage 1: Awareness. The company knows AI exists. Some employees are experimenting on their own (usually secretly, on personal devices). There's no policy, no training, no strategy. Leadership says "we need to do something about AI" but hasn't defined what.

Stage 2: Experimentation. A structured pilot is running. A small team (10-20 people) is being trained. There's a basic AI usage policy. Some security boundaries are defined. This is where Garden Group was when we started their Pioneer Program -- aware of the potential, but lacking the framework to capture it.

Stage 3: Integration. AI is embedded in specific workflows. Teams don't "use AI" as a separate activity -- it's part of how they do their job. Time savings are measured. Best practices are documented. Internal champions exist.

Stage 4: Transformation. AI changes the organization's operating model. New roles emerge. Workflows are redesigned around human-AI collaboration. The company makes decisions differently because AI surfaces insights that humans alone would miss.

Where Most Companies Actually Are

In my experience across dozens of engagements: 80% of companies are at Stage 1, 15% at Stage 2, and almost none beyond that.

The trap is that Stage 1 companies often think they're at Stage 2 because they've "done AI training" -- meaning someone gave a one-hour presentation about ChatGPT at an all-hands meeting. That's not experimentation. That's awareness with better PowerPoint.

How to Move From Stage 1 to Stage 2

The transition from Stage 1 to 2 is the hardest. It requires three things:

1. Permission. Explicitly tell employees they are allowed to use AI for specific tasks. Define the security tiers. Publish a policy. Until this happens, people will either not use AI at all or use it secretly.

2. Structure. A single workshop helps, but it's not enough. When I designed the 4-module experiment at DoRich, the structure -- literacy, creativity, visual creation, data analysis -- gave participants a clear path from "what is this" to "I can do this myself."

3. Accountability. Assign someone to own AI adoption. Not the CTO. Not an "AI committee." One person whose job includes tracking adoption metrics, identifying workflow opportunities, and following up after training sessions.

The Collect-Your-Hard-Problems Principle

Ethan Mollick says to "collect your hard problems and good ideas now, because they'll get more valuable as AI improves." He's right. The companies that move to Stage 3 fastest are the ones that start documenting their most painful, repetitive workflows today -- even before they have the AI solution.

When I trained tourism executives at CTS, the most valuable output wasn't the training itself. It was the list of 15 specific workflow pain points that emerged during the session. That list became the roadmap for everything that followed.

Don't wait until you're at Stage 4 to start. Map your hard problems now. The AI maturity journey starts with knowing exactly where you're stuck.