Agentic Marketing: Autonomous AI Systems in Brand Strategy — A Theoretical Framework

The emergence of large language model (LLM)-powered agents capable of autonomous goal pursuit represents a structural shift in how marketing functions are conceived and executed. This paper proposes a theoretical framework — the Agentic Marketing Autonomy Scale (AMAS) — to classify marketing AI systems across five dimensions: perception scope, planning depth, action range, human oversight threshold and feedback integration.

Unlike prior taxonomies that distinguish marketing automation by channel or task type, AMAS operates at the architectural level, enabling cross-industry comparisons. We validate the framework through structured interviews and system audits conducted at 12 Fortune 500 companies with active agentic marketing deployments.

Findings indicate that organizations in the upper two tiers of AMAS achieve a 3.4× faster campaign iteration cycle and a 61% reduction in human touchpoints per decision, while maintaining brand compliance rates above 94%. We discuss implications for the redefinition of the Chief Marketing Officer role from operational director to strategic orchestrator, and propose a governance model for responsible agentic deployment.