Citation:

Akhila Gudla, 2026. The Agentic GTM Maturity Model: A Framework for AI-Driven Revenue Systems,  Volume 6 Issue 3: 93-100.

Abstract:

The aim of this review is to suggest the Agentic GTM Maturity Model as a simple matrix to evaluate how mature an AI-powered go-to-market revenue system and related organisations are in their transition from uncoordinated analytics to managed semi-autonomous revenue systems. The academic perspectives of the peer-reviewed journal articles reviewed herein build upon one another when it comes to understanding the agentic maturity of GTM and provide insights to AI-driven sales, B2B marketing automation, CRM intelligence, customer engagement, customer-retention analytics, and digital transformation. The literature indicates a higher probability of the revenue system developing more as an integrated process when the benefits of data integration, predictive modelling, workflow automation, human-agent interaction and outcome measurement are planned together as an integrated system, rather than developed individually. There are, however, several unknowns that need to be dealt with, including inadequate causal attribution, inconsistent handling of AI in sales, limited explanatory power, unclear functional governance, and limited evidence of when it is safe or appropriate to grant autonomy to AI to initiate, sequence, and/or adapt revenue-related actions within a human-defined commercial boundary. The author does not think that the 'mature agentic GTM' is a technology adoption, it is a capability of the revenue system. The benefit of the review is that it brings together research on AI decision support systems with responsible orchestration of revenue for acquisition, conversion, expansion and retention and clarifies the organizational environment in which agentic GTM systems can generate measurable and governed business value.

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Keywords:

Agentic AI, Customer Relationship Management, Go-To-Market Systems, Marketing Automation, Revenue Analytics