Is your tech stack ready for 2026 and beyond? Download our new eBook and get a blueprint for the composable AI data stack.
AI decisioning sytems are like baby AI — they don't have the full customer context and general world knowledge to provide intelligent recommendations. That's why every AI decisioning platform needs an agentic context graph within its data cloud. And if your team plans to use decisioning in its marketing stack, you need to start building an agentic context graph today.
In the video series, From Data to Decisioning, we explore the concepts of causation and correlation in marketing, and why causality data is the missing ingredient your team needs to drive meaningful growth with agentic AI.
Watch episode 6 below, and scroll to the bottom for a full list of episodes.
Does your toddler make all decisions in the household? Next vacation, which city to move to, investment decisions? Probably not!
Decisioning systems are baby AI. They make decisions based on virtually no relevant data — and all they can do in the beginning is guess.
That’s what the agentic context graph fixes. And if you plan to use an AI decisioning tool in your marketing, you need to start building this context graph today.
The agentic context graph is like a living knowledge layer that sits behind AI decisioning. The graph combines:
Real-time customer context from your cloud warehouse
General world knowledge
Causal learning from past interventions.
This last component is key. Every time you run an experiment, launch a journey, or route customers through some kind of marketing intervention, the system records three things: the customer context, the action taken, and the outcome.
It brings all that into a single connected view of “what works, for whom, and under what conditions.”
And that’s why it’s a critical piece of modern AI decisioning:
It speeds up time-to-value. Decisioning doesn’t start from random exploration—it starts from prior proven learning.
It reduces risk. If the system has evidence that certain treatments boomerang for certain customers, it can avoid those paths before they burn budget and trust.
It enables compounding growth. Learning doesn’t reset when you scale. Each campaign becomes fuel and a learning opportunity for the next one.
If you don’t let your toddlers make decisions in your home, don’t start by letting baby AI take over your marketing stack! The agentic context graph ensures adults are in the room, helping your marketing team learn and optimize campaigns faster to compound growth.
Watch more From Data to Decisioning episodes:
Learn why marketing teams must start building an agentic context graph in their data cloud, and how it helps AI decisioning platforms deliver smarter results.
Compare the Salesforce CDP and GrowthLoop across user experience, AI, data integration, segmentation, campaign management, pricing, and time to value.
Compare Adobe Real-Time CDP and GrowthLoop across user experience, AI, data integration, segmentation, campaign management, pricing, and time to value.
