ABM was supposed to make B2B marketing more buyer-focused. But there’s an uncomfortable contradiction at the heart of how many programs now operate: we say we’re following the buyer, then decide in advance which accounts matter, who sits in the buying group and what behaviours count as intent.

Now AI is being asked to make those decisions faster and at greater scale. But if those systems are learning from assumptions marketers have already made about their market, are they helping us discover what buyers are actually doing or simply getting better at confirming what we already believe?

The risk is that ABM becomes very good at finding evidence to support decisions we’ve already made, while AI makes those decisions increasingly difficult to question.

In this client panel, pharosIQ brings together B2B marketers with different experiences across ABM, AI and buyer intelligence to examine what happens when real buyer behavior challenges the models built around it.

The discussion will explore how practitioners balance account strategy with observed buyer behavior, when AI deserves their trust and when commercial instinct tells them to challenge it. We’ll ask how much marketers really know about the people influencing a purchase, and whether AI-generated buying groups and intent scores are creating greater buyer visibility or simply greater confidence in imperfect assumptions.

The goal isn’t to argue against ABM or AI. It’s to ask what happens when we stop using AI to reinforce the ABM strategy we already have and start using it to challenge what we think we know about the buyer.

What you'll learn:

• Where traditional ABM models can create blind spots that AI may reinforce rather than resolve
• When to trust AI-generated account, buying-group and intent recommendations, and when to challenge them
• Why explainability and data provenance matter when AI starts influencing where ABM budget and sales attention go
• How to combine AI, buyer intelligence and commercial judgment to make ABM more responsive to actual buyer behavior
   
• Key takeaway
• ABM starts with assumptions; AI can either reinforce those assumptions at scale or help us challenge them with evidence.