3 Stages of AI Maturity in Competitive Intel

We’re a little over two years into the AI hype cycle. And as someone knee-deep in an AI-focused role at Apollo, I can confidently say this stuff isn’t going anywhere.

But here’s what I’ve been noticing:

Competitive intel teams feel like a holdout.

Nearly every other team is finding ways to plug AI into their workflows. Sales. Marketing. RevOps. Even Legal. But CI? Still pretty cautious.

And I get it. We don’t need to adopt every shiny tool the moment it drops. Honestly, I’d argue we shouldn’t.

But if we don’t start figuring out how to use AI in smarter ways, we’re putting ourselves at risk. Not just of falling behind, but of being replaced by someone who knows how to do less with more.

That might sound dramatic, but imagine your CEO asking: “Why do we need a CI program when I can just ask ChatGPT to do deep research on competitor trends, scrape G2 reviews, or run a win-loss survey analysis?”

We know our jobs go far beyond that. But not everyone else does. And that’s the problem.

We need to educate our colleagues on what CI is capable of, and how AI can MULTIPLY our work.

And most of all, we need to use it like every other high-performing team: to elevate our role, not defend it.

So I pulled together a framework to help you evaluate where your program stands today. In my opinion, there are three stages of AI maturity in CI. If you’re in Stage 1, that’s OK. But it also means you’ve got work to do if you want to future-proof your role.

Better to know now than find out later when leadership starts asking the same questions without you in the room.

🧱 Stage 1: Traditional CI

This is where most teams start.

You’ve got a few battlecards, a Slack channel, and a shared drive full of stuff that gets updated when you have the time. You’re probably juggling seller requests, executive fire drills, and a competitor that just launched something weird.

The workflows are manual. And half of your job can feel like justifying that CI is a job (I’ve been there and it sucks).

But look: this stage isn’t bad. It proves there’s a need for your work. It forces you to build trust and relationships with the teams around you. And it teaches you how your company sells, markets, and builds.

The problem is… it doesn’t scale.

• Every question turns into a one-off.

• Every insight needs your personal touch.

• And every time you refresh an asset, you’re thinking, “didn’t I just do this?”

If you stay in this stage too long, you become a bottleneck. And unintentionally, you start trapping yourself in a mid-level role. It’s tough to take on more strategic work when you’re stuck on the hamster wheel.

And while the work is valuable, it’s repetitive and often goes unnoticed unless the C-suite is really looped in and paying attention.

🤖 Stage 2: General-Purpose AI

This is where things start to feel exciting.

You’ve got access to powerful tools. You’re experimenting with prompts. You’re generating summaries, scraping competitor pages, maybe even analyzing call transcripts.

It feels like a cheat code… until it doesn’t.

• The “insights” are actually super generic.

• ChatGPT makes up a product launch that never happened.

• You spend more time fact-checking than analyzing.

This stage is all about experimentation. And that’s great.

But general-purpose AI doesn’t know your space… i.e. it doesn’t pull from YOUR systems and it doesn’t know what matters in an ACTUAL competitive deal.

So while it can save you time, it still puts the burden back on you to clean it up, package it, and make it useful.

Yes, you’re technically working smarter. But you're still doing more work than you need to.

🎯 Stage 3: Purpose-Built Competitive AI

This is where AI stops being a tool and starts being part of the team.

At this stage, AI isn’t just something you use when you have time.

• It’s listening to calls.

• It’s pulling intel from CRM notes, Slack threads, and G2 reviews.

• It’s flagging competitor mentions without you having to dig.

• It’s delivering insights right when a rep needs them without anyone needing to ask.

The best part is it’s not replacing you. It’s freeing you up to focus on strategy, refine positioning, and proactively shape deals instead of just reacting to them.

This is where CI becomes embedded in an actual revenue motion. Not just “a resource.” An actual driver.

Getting here isn’t easy. You need clean(ish) data. You need buy-in. And yeah, you need to be willing to let go of total control. But once you make the leap, CI stops being a service function and turns into a growth engine.

If you made it this far, I’d love to know where you see your program today?

None of this is about shame or perfection. It’s about momentum. AI is going to keep moving. The question is whether your team is moving with it.

If you're in Stage 1 or 2, this isn't your sign to panic… just a sign to pay attention, act with intention, and plan accordingly.

Stay Healthy, my friends.

💚Andy