AI in Account-Based Marketing (ABM): Tactical Playbooks for Hyper-Personalized Prospecting

Abstract

Account-based marketing has shifted B2B go-to-market strategies from high-volume campaigns to targeted engagement with specific accounts. However, traditional ABM implementations struggle with scale, requiring manual research and personalization that limits the number of accounts teams can effectively pursue. Artificial intelligence enables ABM programs to operate at greater scale while maintaining personalization depth through automated research, signal detection, content generation, and multi-channel orchestration. This paper presents tactical playbooks for implementing AI-assisted ABM workflows. We describe account selection and tiering, research automation, AI-generated personalization, multi-channel coordination, and measurement frameworks. The playbooks address common implementation patterns including one-to-one, one-to-few, and one-to-many ABM motions, with guidance on tool selection, workflow design, and team coordination. While examples reference common ABM platforms, the patterns remain applicable across different technology stacks