Agentic Decision Systems for Enterprise Revenue Operations: A Reference Architecture Beyond Account-Based Marketing
Abstract: Account-based marketing (ABM) improved business-to-business revenue execution by focusing resources on selected accounts and coordinated plays. However, list-centric orchestration is increasingly misaligned with fast-changing buyer behavior, private research channels, fragmented intent signals, and the operational need to decide continuously across accounts, contacts, channels, and budget constraints. This paper reframes agentic go-to-market (GTM) as an enterprise technology problem: the design of an autonomous, governed decision system over revenue data. We define Agentic GTM as a condition-based decision architecture that senses account state, evaluates eligibility and constraints, selects actions, executes through enterprise tools, and learns from downstream outcomes. The contributions are threefold: (1) a formal state-action model that distinguishes static ABM orchestration from condition-centric autonomy; (2) a reference architecture covering signal ingestion, identity resolution, feature management, policy learning, action orchestration, observability, and governance controls; and (3) a design-science evaluation using a transparent synthetic simulation under equal budget. The illustrative results show a 19.6% improvement in revenue per touch, a reduction in low-readiness touches from 35.2% to 9.4%, and broader market coverage without increasing total touch volume. The paper also identifies implementation risks, including excessive agency, data-quality propagation, identity and access control, attribution bias, and governance overhead. The findings support the position that Agentic GTM should be treated as a governed decision architecture rather than a marketing automation upgrade.
Keywords: Agentic AI, autonomous agents, account-based marketing, revenue operations, decision architecture, enterprise data architecture, feature store, governance, AI risk management
How to Cite?: Thirupathaiah Peram, "Agentic Decision Systems for Enterprise Revenue Operations: A Reference Architecture Beyond Account-Based Marketing", Volume 15 Issue 5, May 2026, International Journal of Science and Research (IJSR), Pages: 1127-1131, https://www.ijsr.net/getabstract.php?paperid=SR26515222320, DOI: https://dx.doi.org/10.21275/SR26515222320