Shantanu Shekhar, VP of Revenue Operations at Personio, funded his GTM engineering team by cutting two BDR heads and redirecting that budget into builders. Twelve months later, AE productivity is up 30%, pre-call research time dropped from two hours to 15 minutes, and 80% of MQLs run through an AI inbound SDR. He tells Noah and Andy exactly how he got there.

What makes this episode worth your time is the operational specificity. Shantanu doesn't talk about AI strategy in the abstract. He walks through the four-pillar charter he built, the agents his team shipped, the ones that flopped on adoption, and the build-versus-buy calls that didn't go as planned. If you're trying to stand up a GTM engineering function or make the case for one, this is the closest thing to a playbook you'll find.

Topics discussed:

• Four-pillar GTM engineering charter: culture, process, data, and systems sequencing

  
  

• Redirecting BDR headcount to fund GTM engineers and how to make that case

  
  

• Why GTM engineering embedded in RevOps eliminates an entire layer of alignment friction

  
  

• Building an attribution agent on Gong transcripts so attribution becomes a prompt, not a tool

  
  

• Research agent that cut AE pre-call prep from two hours to 15 minutes, driving 30% ARR lift

  
  

• Capturing 80% of MQLs through an AI inbound SDR and expanding from chat to multimodal

  
  

• Post-sales reachability agent orchestrating Zendesk, email, and Outreach to surface churn risk and cross-sell signals

  
  

• Evolving from a center-of-excellence model to specialized GTM engineers by segment

  
  

• Why shipping without a feedback loop kills adoption, and how to build the transition cycle

  
  

• What Shantanu actually tests for when hiring GTM engineers, and why technical skill is just the floor

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