Why AI Maturity Begins with Curiosity, Not Strategy

Almost every AI conversation starts the same way. An executive says, “We need to do something with AI.” Their eyes light up as they talk about the possibilities. Efficiency. Cost savings. Competitive advantage. The energy is real.

Then I ask, “What have you tried so far?”

The answers are usually small experiments. Someone uses ChatGPT for emails. A few people are testing content generation or summarization. Others admit they’re not sure where to start. The takeaway is that they’re open, and they feel curious. And that curiosity is the first step in maturity.

Why Strategy Can Wait

Many organizations start by thinking they need a strategy before they can take action. Leaders begin exploring questions. What’s our roadmap? How do we measure ROI? Which workflows should we automate? What’s our governance framework?

These are valuable questions but asking them too early can slow progress. I once spoke with someone who attended a workshop and was amazed by what AI could do. Weeks later, when we reconnected, they said they were still exploring ideas and opportunities. That space between inspiration and action is where maturity begins.

How Maturity Builds

Through conversations with organizations exploring AI, I’ve seen clear stages of growth. Growth that comes from those who take part in the experience, with each moment teaching something important. Moving step-by-step allows lessons to build naturally and success to grow steadily.

Stage One: Scattered Experimentation

This is where everyone starts. People try AI tools independently. Marketing drafts posts. Finance summarizes reports. A project manager writes meeting minutes. Nothing is structured, and nothing is measured. But curiosity is high, and that matters.

In workshops, participants often describe their use of AI as “limited.” That’s Stage One. It’s exploration, not implementation and it’s completely appropriate. The key is recognizing that this stage lays the groundwork for the understanding that makes future progress possible.

Stage Two: Accepted Practice

The first significant milestone occurs when AI-assisted work becomes an integral part of daily routines. Teams use tools for writing, research and summarization. Subscriptions get approved. People type less and produce more.

Most organizations reach this stage quickly. It feels like momentum. As one person said after a workshop, “I can’t think widely enough to see how and where we could use it.” That curiosity to expand is its own sign of growth. The tools are in place, and vision starts to form.

Bridging the Maturity Gap

This is where many organizations begin to elevate their work. They realize that general AI responses can be improved with their own data and insights. The model can reflect their specific processes and knowledge, making results far more valuable.

At this point, AI benefits from access to internal context. Documents. Data. Institutional knowledge. When that happens, AI evolves from a general assistant to an informed collaborator. It takes thoughtful planning and steady commitment. Data becomes structured. Access is organized. Someone takes responsibility for coordination.

For many mid-sized companies, the resources required to reach this stage are modest compared to those needed for larger initiatives. What matters most is leadership focus and enthusiasm. I’ve been in meetings where everyone shared excitement about potential and wanted to begin small projects to learn quickly. That eagerness is a maturity signal and a healthy sign of progress.

At the next stage, the change becomes more visible. AI starts taking actions. It calls systems, runs calculations, queries databases and automates steps that once took time. The focus shifts to designing processes that can scale and stay consistent.

Stages Five and Six: Transformation

By this point, organizations recognize that AI works best when tasks are well-defined. Complex work is broken down into a set of clear, repeatable steps. Each step may need its own workflow and tools. This is where expertise and technology combine to create lasting value.

The main investment here is time and collaboration. Teams document what they know so the organization can scale it. And at Stage Six, AI becomes seamless. People simply work, and the work improves. Less stress. More satisfaction. Greater creativity and productivity.

Reaching full maturity takes patience and consistent learning. The mindset matters most. At this point, AI strategy and business strategy are naturally aligned.

Why Maturity Grows Step by Step

Maturity is a journey. Each stage builds on the one before it. When an executive says, “I don’t really know what to expect,” that honesty shows readiness to explore. Curiosity is a starting strength.

Organizations further along move confidently to the next level. They have seen results. Projects move efficiently. Quality rises. Senior staff spend time on creative and strategic work. Each success builds confidence for the next step.

Starting Where You Are

If you’re at Stage Two, and many companies are, the key question isn’t “How do we get a budget for AI?” It’s “Are we ready to evolve how we work?”

That’s what growth in AI maturity really means. The software is accessible. The tools connect. The models keep improving. The opportunity lies in helping people align around new ways of working and making decisions.

After many workshops, participants describe the session as “outstanding” or “eye-opening.” The true progress comes after the session ends. Do they move from insight to implementation? Are they ready to make the operational changes needed for lasting improvement?

Every stage adds value. You don’t need to reach the final stage to see returns. You do need to understand where you are and what it takes to move forward. Time, focus and leadership alignment all play a role.

The organizations advancing with AI aren’t always the ones with the biggest budgets or the flashiest tools. They’re the ones whose maturity matches their ambition. They learn from results, build on success and grow steadily.

So, when you say, “We need to do something with AI,” remember that you already are. Those early experiments count. They are not side projects. They are your first step toward maturity.

AI maturity begins with curiosity, and it grows through commitment, one stage at a time.