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A composable architecture is essential for AI decisioning: by running directly on governed, real-time warehouse data, marketers can avoid delays, incomplete customer context, and broken learning loops. The result is faster, more accurate decisions and a system that continuously improves by connecting actions to outcomes in real time.
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 5 below, and scroll to the bottom for a full list of episodes.
AI built on causality data is a powerful tool for marketers. But there’s one critical element it must have to operate: trustworthy, real-time and exhaustive customer information.
That’s why a composable architecture, built around and within the data cloud, is a non-negotiable for causal AI.
The primary issue with non-composable decisioning solutions is the data foundation they’re built on.
These strategies copy data into a secondary system, leading to data delays dependent on custom sync schedules, mismatches, incomplete context limited by the data copied over, and governance headaches.
Ultimately, this breaks the AI. It creates decision lags and results based on incomplete or inaccurate data.
Your “real-time” AI is making decisions based on half the context of half of yesterday’s customer.
A composable AI decisioning approach flips that.
It runs directly on your data cloud — on governed, live warehouse data — not copy-out, ever.
That means the AI sees the full customer context in the moment of decision. And there is no lag between training the machine, and executing the decision.
It also means the system can ingest data from customer actions and observe outcomes as they happen, learning and improving in real-time.
Those actions — and the results of those actions — help you build the causality data to further inform your AI decisioning.
If your data has to move, your decisions are already late.
Composable decisioning keeps decisions on governed, live warehouse data—so you can connect actions to outcomes as they occur in real time and constantly optimize, achieving compounding revenue growth.
Watch more From Data to Decisioning episodes:
Most marketing data is correlational. Learn how causality data from randomized experiments powers smarter AI decisioning and faster growth.
Most marketing data is correlational. Learn how causality data from randomized experiments powers smarter AI decisioning and faster growth.
Most marketing data is correlational. Learn how causality data from randomized experiments powers smarter AI decisioning and faster growth.
