The Third Era of GTM: Fixing the Data Before Scaling the Agents

GTM data has gone through two big eras.

First, we turned static directories into digital lists. Then we connected those lists to workflows and automated what happened next.

But somewhere along the way, we skipped a step: fixing the data underneath.

Incomplete records limit the market you can see. Missing context makes precise targeting harder. And when AI agents enter the picture, they don't solve those problems. They can act on them faster and at much greater scale.

So what should the data foundation for an agent-powered GTM motion actually look like?

In this session, Apurva Shukla of Sumble joins Matthew Volm of RevOps Co-op to explore what Sumble calls the third era of GTM data, and why advances in LLMs and modern data engineering are changing what's possible.

They’ll unpack how teams can move beyond static records toward GTM data built from underlying evidence, with greater granularity, field-level lineage, and a foundation that can be maintained and governed as AI becomes more deeply embedded in GTM workflows.

🔑 Key Takeaways

• Why the data foundations built for traditional GTM workflows can struggle as teams introduce AI agents

• How bad or incomplete data limits market visibility, targeting precision, and downstream automation

• What defines the third era of GTM data, and what has changed to make it possible

• How concepts like raw evidence, data lineage, governance, and greater granularity can create a stronger foundation for AI-powered GTM

📣 Speakers

Matthew Volm | CEO & Founder, RevOps Co-op | Moderator

Apurva Shukla | Head of Growth, Sumble

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