AI is only as good as the data you feed it.
(And it’s useless if you can’t activate it.)
While many organisations are eager to explore AI use cases, they quickly discover that their data is:
If your data is trapped in silos, unstructured, or disconnected from your marketing tools, your AI initiatives will fail to launch.
Join us for this strategic roundtable where we define how to prepare your customer data architecture for AI-driven intelligence, decisioning and automation, without falling into hype or complexity.
What's on the table?
We will skip the hype and focus on the plumbing required to make AI work in the real world. Topics include:
Defining "AI-Ready": Moving beyond the buzzwords to understand the specific data attributes: cleanliness, history, and context, required to train reliable models.
Modelling for Prediction: How to structure your data not just for backward-looking reporting, but for forward-looking scoring, churn prediction, and automated decisioning.
Architectural Fit: Determining where AI fits in your stack. Should your models live directly in the Data Warehouse, in a separate AI environment, or embedded within the CRM?
Build vs. Buy: Navigating the critical trade-offs between developing custom models on your own data infrastructure versus licensing off-the-shelf AI agents.
Responsible Scaling: Best practices for democratising AI access across marketing and product teams without creating new data silos or governance risks.
Who is this for?
This session is curated for decision-makers who bridge the gap between technical data and business value:
Marketing Directors & Managers who want to move from "reporting" to "predicting."
Head of Data & CDO's responsible for building an infrastructure that supports GenAI and ML.
Digital Transformation Leads tasked with turning AI pilots into production value.
