There’s a lot of excitement (and noise) right now around AI. Every boardroom slide, investor deck, or company all-hands seems to include the word “AI-powered” somewhere. But here’s the hard truth: most companies don’t know where they are on their AI journey — and that’s exactly why they get stuck.

Just like you wouldn’t start constructing a skyscraper without first surveying the land and laying a strong foundation, you can’t “AI-enable” your company without understanding what stage you’re truly at. And no, buying a few licenses to third-party AI tools or running one successful pilot doesn’t mean you're AI-enabled.

After working with big tech platforms, startups, and marketplaces across multiple continents, I’ve found that companies generally go through three distinct stages in their AI maturity:

1. AI Aware

This is your prep stage. You’ve got decent data hygiene, your systems are modern enough to plug into AI frameworks, and you’ve started investing in AI infrastructure (or at least thinking about it). You’re not building features yet — you’re laying the pipes. Compliance, data sovereignty, privacy, and organizational understanding are key here. You’re setting the stage for real AI to land inside your company instead of bolting it on from outside.

2. AI Ready

Now your internal systems and teams are capable of experimenting with AI and delivering internal or customer-facing features that actually solve a business problem. You’re able to build AI use cases beyond “POC theater” — think real features that touch users or ops, and infrastructure that can support training, fine-tuning, and deployment. But you’re still not reaping full benefits — and that’s okay. This is the proving ground.

3. AI Enabled

This is where AI is embedded in your product’s DNA. You’re not just experimenting; AI is delivering measurable, sustained business outcomes. You’ve operationalized learning loops. You have proprietary data advantages. AI is giving you an edge in the market, and competitors can’t replicate it overnight. Think recommendation systems, real-time optimization engines, or fraud models that feed into your main KPIs.

Why Most Companies Get Stuck

Here’s the trap: most companies think they’re in Stage 2 (AI Ready), but they haven’t even completed Stage 1 (AI Aware). And that’s how they end up in what I call “POC Purgatory” — where all the AI efforts stay stuck in demo decks and prototype slides.

Why? Because they rushed to experiment before building internal readiness. They outsourced the intelligence. The data sits in five vendor dashboards. The learning loops are external. Your knowledge ecosystem is split. If your infra can’t internalize learning, you’re not building durable intelligence — you’re renting it.

Before you race to implement generative AI or advanced ML, pause and ask: Are we truly ready — or just hyped?

Because in this race, speed matters — but starting from the wrong stage guarantees you’ll stall.

Next up in this series: I’ll break down the different types of AI — generative, predictive, and embedded — and how your company’s maturity stage should determine which type of AI you actually need. Don’t miss it.