July 27, 2025
5 min read
Jonathan Moran
Discover how adaptive, goal-driven agentic AI is transforming martech decisioning beyond static rules and passive analytics.
How Agentic AI Broke the Rules of Martech Decisioning
By Jonathan Moran — July 25, 2025 Hard-coded logic and passive analytics are out. Adaptive, goal-driven agents are the next evolution in marketing technology.The Gist
- Old rules break. Legacy rules-based systems lacked learning or adaptation, limiting real-time marketing decisioning.
- Analytics plateaued. Predictive models provided insight but stopped short of automation or workflow integration.
- Agents emerge. Agentic AI supports goal-oriented, adaptive decisions inside the martech stack. Agentic AI has suddenly appeared on the martech scene, prompting many to learn about its technology and applications. This article is the first in a three-part series exploring the history of agentic AI, how organizations can integrate it into their martech ecosystems, and quick-win use cases.
- Hard-coded rules of static enterprise decisioning
- Passive analytics of predictive models and machine learning
- Rigid workflows of robotic process automation with goal-oriented agents that can reason, act, and learn within the martech stack. These AI agents make adaptive decisions and learn over time, delivering contextually intelligent actions with minimal human intervention. They will co-create and continuously optimize customer journeys based on real-time data and feedback. This article is Round 1 of a three-part series. The next post will provide tips and tricks for integrating AI agents into your martech ecosystem.
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When Rules-Based Systems Ruled Martech
Static enterprise decisioning, also known as rules-based decisioning, was an early automation approach. It relied on hard-coded logic to automate emails, nurture decision paths, or score leads. These systems had no learning capabilities and required frequent manual adjustments to keep up with dynamic marketing, service, and support environments.When Predictions Stopped at Insight
Machine learning and predictive analytics gained popularity in the 2010s. They forecasted churn, scored leads, and predicted purchase or response behaviors. However, these model outputs were typically fed to humans or business intelligence dashboards rather than embedded into automated workflows. They provided insight but not decision or action automation.The Rise of Robotic Task Workers
Robotic process automation (RPA) emerged in the early 2010s as the first software agents. These low-level “robots” executed rule-based processes primarily for back-office operational tasks such as finance, service, and support. They were not focused on front-end customer experience.How Chatbots Brought Agents to the Front Line
Conversational AI, via chatbots, brought agents into front-office customer experience in the late 2010s. Vendors created predefined conversation flows and narrow natural language processing (NLP) capabilities for customer service and lead qualification. These systems were often siloed with limited integration into broader dialogue or workflows. Related Article: The Evolution of AI Chatbots: Past, Present and FutureOrchestrating Customer Journeys by Script
Customer journey orchestration engines have helped brands design journeys based on segments, triggers, and channel rules for over 15 years. Initially, these engines relied on static logic and fixed success criteria, lacking real-time adaptability and scalability for personalization.The Evolution That Made Agentic AI Inevitable
Agentic AI is the natural evolution of automation, intelligence, decisioning, and autonomy in marketing technology. It replaces:About the Author
Jonathan Moran is Head of MarTech Solutions Marketing at SAS, focusing on customer experience and marketing technologies. With over 20 years of marketing and analytics experience, he has held roles at Earnix and Teradata Corporation in pre-sales, consulting, and marketing.Source: Originally published at CMSWire on July 25, 2025.