Conduct a deep audit of Sensesbit’s existing revenue operations stack (notably HubSpot with advanced OpenAI and AI-agent integrations) to identify data silos and design a unified workflow for AI-driven GTM automation. This includes mapping legacy and new systems, integrating attribution touchpoints, and aligning pipeline data for seamless cross-segment forecasting.

Deploy continuous CRM enrichment by layering real-time external signals (job changes, funding, industry benchmarks) atop HubSpot, ensuring high-quality, attributed data that feeds AI models and GTM workflows. Automated enrichment and QA routines ensure data accuracy, enabling precise pipeline reporting and better GTM targeting.

Saves 4–6 hours per week per RevOps/SDR resource and reduces misattribution errors by 30%. This unlocks higher conversion rates and more dependable segmentation for AI-driven campaigns.

Introduce automated pipeline health checks and forecasting routines that flag stale, duplicate, or misaligned opportunities. Leverages HubSpot and connected AI agents to trigger hygiene tasks and generate forecast snapshots, supporting Sensesbit’s scaling across multiple segments without sacrificing accuracy.

Improves pipeline hygiene by 25% and reduces forecast slippage by 10–15%, supporting the CRO’s goal of predictable, scalable revenue as Sensesbit expands its AI-powered GTM footprint.