Everyone’s talking about AI like it’s already table stakes. But peel back the surface in most organizations, and what you’ll actually find looks less like a well-oiled machine and more like a garage full of half-finished side projects. A pilot here, a proof-of-concept there, a few one-off tools someone swears are “game-changing.” All moving in different directions.
That’s not maturity. That’s Exploration. And right now, that’s exactly where most brands are.
In Part 1 of this series, I laid out the AI Maturity Framework we use at KORTX—a roadmap designed to help organizations move from isolated hype to integrated capability. In this post, we’re digging into the first real phase of that journey: Exploration.
Exploration is where the energy is high—but the impact is often low. Let’s unpack what that means, why it matters, and how to move through it with intent.
What Is the Exploration Phase?
In Part 1, I defined it like this: “Teams are testing AI in pockets, often without structure or metrics. Energy is high, but learnings are fragmented.”
Now, let’s add some color.
Exploration is the “try stuff and see what happens” stage. It’s when:
- Marketing is experimenting with ChatGPT prompts.
- Ops is tinkering with AI scheduling or summarization tools.
- Someone in IT is quietly building a Slackbot.
- Your agency pitched an “AI-powered persona” tool last quarter (and yes, you still haven’t logged in).
And none of it connects.
This phase is normal—even necessary. Every organization has to start here.
But here’s the trap: Exploration without strategy doesn’t lead to maturity—it leads to wheel-spinning.
Why Exploration Matters (And Why It Fails So Many Teams)
Exploration is not a bad thing. It’s how innovation starts. At this stage, you’re:
- Lowering the barrier to entry for your teams.
- Creating space to experiment without immediate ROI pressure.
- Discovering what AI can—and just as importantly, can’t—do for your workflows.
- Building internal curiosity and momentum.
The problem? Most organizations mistake activity for progress.
- Multiple pilots running in isolation.
- Five tools solving five different problems, none of them scalable.
- Results that look promising, but aren’t captured or shared.
- No shared definition of success.
Sound familiar? You’re not alone.
MIT Sloan research shows most companies are stuck in these early phases—lots of scattered adoption, very little impact. Accenture’s 2024 AI maturity study found only 12% of organizations are achieving sustainable AI success at scale. The rest? Still exploring.
Much like digital transformation a decade ago, the hardest leap isn’t starting—it’s turning small experiments into enterprise-wide capabilities.
5 Signs You’re Stuck in Shallow Exploration
Exploration should feel like building a foundation, not chasing your tail. Here’s how to know you’re spinning in circles:
Pilots with No Clear Goals
Tools are tested, but nobody can articulate what success looks like.
No One Owns AI Strategy (Because There Isn’t One)
Dabbling across teams without direction doesn’t add up to learning—it adds up to wasted time.
You’re Chasing Tools, Not Solving Problems
AI isn’t a shiny product you buy; it’s a capability you build.
Learnings Live in Silos
The copy team wins. The media team fails. Nobody connects the dots.
Leadership Is Curious, But Not Committed
Interest is passive, not proactive. And that’s the bottleneck.
How to Explore with Purpose (and Actually Make Progress)
Exploration doesn’t need to be chaos. If you’re in this phase, here’s how to do it right:
- Tie Every Test to a Real Business Problem. Don’t ask, “What can AI do?” Ask, “Where are we stuck—and could AI help?” For example, is creative production slowing down go-to-market? Start there.
- Build a Cross-Functional Exploration Squad. You don’t need a formal Center of Excellence yet, but you do need marketing, ops, tech, and data talking regularly. Think of it as a mini task force.
- Define Success Before You Hit ‘Run’. Speed? Efficiency? Lift? Cost savings? Pick one. Set a baseline. Track it.
- Centralize the Learnings. Spin up an internal AI wiki, dashboard, or shared doc. Treat it like R&D. Wins, losses, lessons—all in one place.
- Go Deeper on Fewer Tools. Tool fatigue is real. Stop hopping from one platform to the next. Depth beats novelty.
What Progress Looks Like in the Exploration Phase
Progress at this stage doesn’t look like transformation. It looks like momentum.
You’re moving forward if:
- Teams are sharing learnings openly.
- Repeatable use cases are emerging.
- Leaders ask, “What’s next?” instead of “What is this?”
- Early experiments are nudging KPIs in the right direction.
- You’re confident enough to say “no” to tools that don’t fit.
A Real Example:
A national franchise group leveraged AI to generate unique video creative at scale. What used to take weeks of production time (and a big budget) was now produced in days, at a fraction of the cost. The result? They gained access to advertising channels—like streaming video and programmatic CTV—that were previously out of reach. That initial experiment evolved into a repeatable capability, fundamentally changing how they approached creative production and media planning.
That’s how exploration moves from novelty to capability.
How to Know You’re Ready for What’s Next
You’re ready to graduate from Exploration when:
- AI use cases align with core business goals.
- Leadership is investing, not just observing.
- Teams are requesting training, governance, or guidance.
- A few wins have been repeated—and you know why they worked.
That’s the moment to move into Phase 2: Integration (coming in Part 3).
Final Thought: Exploration Isn’t a Destination
Every organization starts in Exploration. But the best don’t linger here.
If your AI efforts feel scattered, disjointed, or hard to measure, don’t panic—just reframe:
- Start tying tools to real problems.
- Define what “good” looks like.
- Capture and share your learnings.
- Get strategic about what comes next.
Exploration without direction is digital busywork. Exploration with intent is the bridge to AI maturity.
Need help getting unstuck? Let’s talk.
Catch up on Part 1: The AI Maturity Framework. Next up: Part 3 – Integration: Turning Pilots Into Real Capability ✅.
About the Author: Damon Henry is the founder & CEO of KORTX and has led the company since its beginning in 2014. Passionate about building teams and products, Damon started KORTX to demistify the complex marketing and ad-tech ecosystem for brands and agencies.