About

Every team now has an AI tool of choice and usually several more they didn’t choose. As Claude, ChatGPT, Gemini, Copilot and specialist agents become interfaces to business data, organisations face a new problem: how do you give people freedom to use the AI tools that make them productive without creating five different versions of the truth?

The answer isn’t necessarily standardising on one model. It’s creating a governed data foundation that separates where trusted data lives from the AI interface people choose to interrogate it through.

Using marketing data examples, this session explores how organisations can connect governed data to tools such as Claude and other leading LLMs while retaining control over access, definitions and provenance and how platforms such as Supermetrics Studio can provide the trusted data layer underneath that ecosystem.

Takeaways:

• A practical framework for balancing AI experimentation with data governance
• Why the AI model should increasingly be treated as an interface rather than the source of truth
• How a shared data layer can prevent different AI tools producing conflicting answers
• How to give teams freedom to adopt new AI tools without continually rebuilding their data infrastructure