Martech’s Future: Predictive to Agentic AI

The Gist

Old rules break. Legacy rules-based systems lacked learning or adaptation, limiting decisioning in real-time marketing.
Analytics plateaued. Predictive models provided insight but stopped short of automation or workflow integration.
Agents emerge. Agentic AI builds on past tech by supporting goal-oriented, adaptive decisions inside the martech stack.

Agentic AI has suddenly appeared on the martech scene, leaving some to wonder how to get up-to-speed on the technology and its uses. Over a series of three short bylines, I’ll dive into the history of agentic AI, how we got to where we are today, the considerations for organizations as they evaluate the integration of agentic AI into their martech ecosystems and what quick win use cases they can consider undertaking.

First let’s look at the history of agentic AI. As much as agentic AI seems to have popped onto the scene just after generative AI got finished making its splash, there have been predecessor technologies. Agentic AI, in my mind, is an evolution of data, decisioning and automation technologies over the past 20+ years.

When Rules-Based Systems Ruled Martech

Static enterprise decisioning (aka rules-based decisioning) entered the scene early. It was all hard coded logic to automate emails, nurture decision paths or score leads. There was no learning and no logic. The created rules needed to be adjusted frequently in dynamic marketing, service and support environments.

When Predictions Stopped at Insight

Machine learning and predictive analytics, though first introduced in the 1950s, became most popular in the 2010s. Churn was forecasted, leads were scored, and other predictions around purchase or response were created. Model output scores were fed to humans or into BI dashboards, not embedded into workflows of any type. Insight, yes. But decision or action automation, not yet.

The Rise of Robotic Task Workers

Robotic process automation (RPA) was the first foray into software agents in the early 2010s, with vendors offering low level “robots” or rule-based processes that were designed to complete mostly low-level, back-office operational tasks. These tasks were responsible for completing finance, service and support tasks, and they were not focused on the front-end customer experience.

How Chatbots Brought Agents to the Front Line

Conversational AI via chatbots allowed agents to leave the back-office and make their way to the front-office customer experience realm in the late 2010s. Vendors created predefined conversation flows and narrow NLP for customer service and lead qualification. Often these conversational systems were siloed, and outside conversation or dialogue integration was limited.

Orchestrating Customer Journeys by Script

Orchestration engines have supported customer journey orchestration and optimization over the last 15 years. These solutions help brands to design customer journeys based on segments, triggers and channel rules. Initially, these engines relied on standard logic and success criteria. They lacked real-time adaptability and the ability to scale personalization efforts.

The Evolution That Made Agentic AI Inevitable

While many recent solutions, particularly conversational AI tools and orchestration engines, have begun to incorporate agentic AI, it’s clear that agentic AI and decisioning is a natural evolution of this somewhat linear flow.

Agentic AI is the natural evolution of automation, intelligence, decisioning and autonomy. It replaces the hard-coded rules of static enterprise decisioning, the passive analytics of pure predictive analytics and ML, and the rigid workflows of RPA with goal-oriented agents that can reason, act and learn inside your martech stack.

AI agents that can make adaptive decisions and learn over time are poised to change the game in martech. They can deliver contextually intelligent and aware actions or activations with limited human intervention. Agents that will be able to co-create and continuously optimize journeys based on real-time data and feedback are the future.

Editor's note: This was Round 1 of a three-part series. The next post in this series will outline several tips and tricks to consider as you start to add AI agents into your martech ecosystem.

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