Static account scoring models have a major flaw: they flatten weeks of complex buyer activity into arbitrary points, leaving revenue teams chasing cold MQLs while active deals slip by unnoticed. In this episode of Astrad Talks, host Daniel Macia sits down with Srikrishna Swaminathan, Co-founder and CEO of Factors.ai, to break down how B2B teams are moving from rigid scoring rules to real-time, signal-based go-to-market engines.
We cut through the noise to discuss:
What separates dynamic buyer intent signals from arbitrary static scoring models
Uncovering the hidden 7 weeks of buyer touchpoints before an account shows up as direct traffic
When autonomous AI agents should trigger direct actions versus notifying a sales rep
How to teach agentic systems to identify when intent signals are too weak to act on
Managing guardrails and risk when an autonomous revenue agent makes the wrong call
Why the traditional ABM platform category is shifting toward agentic GTM layers
Whether you are rebuilding your intent pipeline or looking to operationalize AI agents across your sales and marketing workflows, this episode delivers a clear look at where signal-led ABM is heading next.
Podden och tillhörande omslagsbild på den här sidan tillhör
ASTRAD. Innehållet i podden är skapat av ASTRAD och inte av,
eller tillsammans med, Poddtoppen.
