The Data Foundations Every Business Needs Before Using AI

Instead of only describing what has happened, AI can identify patterns, predict behaviors, and provide recommendations. This shifts time away from manual data processing toward understanding and driving the business forward.

The development is moving fast, and new use cases appear all the time. It can feel difficult to know where to begin and how the technology can best be used in your organization. Which model should you choose, and what problems will need to be solved tomorrow?

Start With A Solid Data Foundation

The good thing is that you don’t need to make every decision immediately. Whether you choose ChatGPT, Claude or Gemini, or traditional analytical methods, the groundwork around data remains the same, your data. A few core building blocks within your data strategy set the stage for successful AI and GPT adoption

Define Your Data: What Do You Mean By “Sales”?

All analyses, even AI-driven ones, need clear definitions of key concepts. If terms like “customer” or “lead” means different things across systems or departments, AI models may generate incorrect conclusions. Make sure all people and machines across the organization speak the same data language.

Centralize Your Data: Is It Accessible To AI systems?

Data in an organization is often scattered across systems due to years of technical debt. For AI agents and GPT models to perform analysis and activation, you need a centralized marketing data warehouse supported by a semantic data layer. Without accurate, unified data, AI can neither understand the business nor contribute to it effectively.

Identify Actionable Insights

You don’t need another dashboard or report, you need the ability to act. Think about which areas can actually be influenced in your business. What insights can be turned into action, and which datasets can help you automate or improve processes? Start there.

When you use AI that works with personal data – for example customer information, chat logs, or emails – you must comply with GDPR and other data protection regulations. This means having clear processes for how data is stored, used, and possibly deleted.

If customer data is used for AI analyses and activations, consent is required, especially if the data is used outside its original purpose. Users must understand what they are agreeing to and how their data will be processed.

Summary: Build The Foundation Before The Technology

Generative AI and autonomous agents are redefining analysis, marketing, and customer relationships. But before you let a model write campaigns or handle customers interactions, your data must be defined, structured, and legally managed.

If you want help building the right data foundations before investing in AI, reach out at [email protected]. We’re happy to discuss your setup and explore what you need to move forward.