Why Your Competitors Can Buy the Same Data - But Not the Same AI Models

In B2B, data has become commoditised. Using AI to apply it has not.

The truth nobody wants to admit: the intent feeds, IP resolution services, and contact enrichment data you’re buying are also available to every competitor with a credit card.

Your advantage isn’t in owning the data.

It’s in how you train AI on your own proprietary signals so it sees the market differently than anyone else’s model can.

The Level Playing Field Myth

For the last decade, B2B marketing teams have been sold the idea that better data equals competitive advantage.

You buy Bombora, 6sense, or LinkedIn audience data.

Your competitors buy Bombora, 6sense, or LinkedIn audience data.

Same vendor. Same targeting logic. Same gaps. Same price

The playing field isn’t just level — it’s crowded. And that means “we bought better data” rarely changes market share.

What an AI Moat Really Is

A moat isn’t just about having something others don’t. It’s about having something they can’t replicate — even if they know exactly what it is.

Processes, methods and implementation absolutely can be a moat

In the AI context, that moat is the trained decision-making layer between the raw signals you collect and the actions your stack takes.

The raw data might be similar between you and your competitors.

But the feedback loop you create with your own:

• Account-level engagement history

• High Value Actions (HVAs)

• Identity graph of known + anonymous visitors

• Media data tied to actual revenue

…means your AI learns patterns nobody else’s model can see.

Niche specialism can play a huge part too, being deep into a sector or sub niche can bring a richer layer of specialism. Applying this knowledge layer on top of the commoditised data layer can agitate an edge

Case Study: Signal-Driven Bidding

Two competitors are bidding in The Trade Desk for the same set of target accounts.

• Competitor A: Optimises to click-through rate (CTR).

• You: Optimise to a proprietary “AI Account Score” built from:
  

  • In-market probability (HVA + intent)

  • Buying stage fit (identity + engagement depth)

  • Creative engagement fingerprints

  • CRM win probability history

  • The result:

Even with identical IP targeting, your model:

• Avoids low-fit accounts that click but don’t convert

• Prioritises creative proven to move your funnel

• Dynamically shifts spend as accounts surge or stall

That’s the moat — not because you bought better data, but because you trained a better decision engine.

The irony being that this engine IS then throwing off proprietary data - engagement scores merged into the licensed data and self learning with sentient AI layers

As the system matures it becomes a true flywheel building intellectual property alongside real measurable results

Why This Matters Now

AI has commoditised execution speed. Anyone can spin up targeting logic, generate 20 ad variants, or build a quick dashboard in minutes.

Agencies, not known for their technical prowess now boast of tens of thousands of “AI agents”. In media land the gap between agency and tech vendor supplier has narrowed, at least superficially. Clients, adtech and everything inbetween are in an AI arms race to build real differentiating value now

So AI commoditised speed. What it can’t commoditise is the history of interactions, wins, losses, and micro-signals that only exist in your ecosystem.

Competitors can copy your messaging.

They can buy the same datasets.

They can even reverse-engineer your target list.

But they can’t buy the AI model you’ve been training on your audience for the past 6 months.

You’ve reached the end of the free content. Below the line we dive into:

The Moat-Building Playbook: How to Train AI on Your Signals for an Unfair Advantage