
GTM Science - A show for GTM and RevOps leaders Building the Foundation for AI in GTM
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Dec 1, 2025 Dive into the world of AI implementation in go-to-market strategies! Discover why many AI efforts fail and the foundational elements needed for success. Using a compelling case study of an AI SDR that outperformed human reps, the discussion highlights the importance of clean data, mapped processes, and the human touch in making AI effective. Learn how to identify bad CRM data, define efficient practices, and strategically introduce AI at the right stage. Don't miss tips on accelerating content production with AI and the pitfalls of rushing into tech without proper groundwork.
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Detect Garbage vs Missing Data
- Audit data for inconsistency and hidden behaviors (e.g., sandbagging reps) before trusting AI-driven insights.
- Use sales cycle, conversion shifts, and outlier performers to spot dirty or incomplete CRM data.
Structure Process For Pattern Detection
- Map the customer journey and translate frameworks (MEDIC/MEDPIC) into stage-specific CRM fields and questions.
- Require reps to capture decision criteria and stakeholders so AI can spot win/loss patterns.
Drive Data Quality Through Management
- Assign frontline managers ownership for CRM adoption and enforce a cadence of pipeline reviews and corrections.
- Minimize optional data entry and automate enrichment to avoid killing rep productivity.
