The 4 essential building blocks of an AI-ready data platform

This blog is about that foundation: the four building blocks that make your data platform AI-ready. Without these four building blocks, AI works (kind of), but it doesn't deliver the value that truly makes a difference within your company. Want to check whether you've got this foundation in order? You'll read all about it in this blog.

Why a data platform is the foundation

Many companies have their data scattered across dozens of systems. Customer data in a CRM, inventory in an ERP, and transactions in the e-commerce platform. All separate islands that aren't connected or working together. And that works just fine, as long as people are manually creating reports or using standard dashboards. But as soon as you want AI to take over manual processes, that's impossible.

AI needs two things: access to all relevant data and receiving that data in a way it understands. No random Excel files, no manual exports and no databases that only sync on Fridays at 6 PM. A modern data platform brings all your data together in one place and ensures everything is in the right formats: it makes your data available as a single source of truth.

But which data platform should you have?

The choice of a data platform depends on what your company does and where you want to go in the future. Snowflake, for example, is a data platform that specializes in providing structure to data and has lots of functionalities to unlock and model data. AWS Redshift and Azure Synapse are cloud-native solutions, that are strong in scaling and integrating with other services. Google also has a platform, but often stands slightly less central in the market.

We like working with Snowflake because it's independent and really focuses on data. That makes you less dependent on one cloud party. But for some clients, AWS or Azure fits better, for example because they're already strongly embedded in those ecosystems. The most important thing to check is that your platform should be scalable, that your teams can work with it, and that it aligns with your ambitions.

The four essential building blocks

With the purchase of a data platform, you lay the foundation for all the cool things AI can do. It's the foundation, but you need to build on it to make it a success.

1. Centralization: everything in one place

Data from your ERP, CRM, and e-commerce platform must be brought to one central place. There they're structured and made findable. That means you set up data pipelines that ensure information flows in real-time (or at least frequently enough).

Without that centralization, you're still juggling separate systems. The result? Agents or AI models get incomplete information, start making assumptions themselves, or stop halfway because they can't access the right data.

2. Accessibility via APIs and Model Context Protocol

Centralizing data is step one, then you need to ensure that data and the capabilities of your systems are actually usable for AI. You do that with APIs and with Model Context Protocol (MCP).

MCP is a relatively new standard and you can see it as a kind of USB-C port for AI agents. Does an agent want to change something in your CRM, for example? With MCP, that agent immediately gets a standardized toolkit with all the possibilities and instructions. Placing orders, updating customers, replenishing inventory: without MCP, AI stays passive on the sidelines, with MCP it steps onto the field and plays along.

3. Data quality: from source to decision

You have nothing with the best AI tool in the world if the quality of your data isn't good. Customers sometimes type weird things in forms, systems sometimes stop syncing, and people make typos – all pollution of your data. That's why you build in checks and validations that catch this. Standardize formats, enforce validations (for example, phone number length), fill in missing fields where possible, and anonymize where needed.

4. In-house knowledge: ownership and expertise

Successful AI starts with people – without knowledge and support from those who work with it, you can forget about real impact. That's why in-house knowledge is the fourth building block. Ensure data stewards who own certain datasets, train teams in data thinking, and document domain knowledge in a central knowledge base so everyone knows where everything is and how it works.

Building blocks: ✅ - now what?

A data platform with these four building blocks in order is the first and most important pillar of AI readiness. Building blocks in order? Only then is it time to look at the other two pillars of AI readiness: technology and people & processes. With technology, it's not just about a well-configured data platform, but also about the rest of your IT landscape and how you deal with compliance, security, and governance.

In addition, you need to get to work with the people & processes pillar. For successful AI agents, you need adoption, training, change management, and clear roles. Your teams must understand why AI is being deployed, what it delivers, and how they work with it. Without support and knowledge, even the best technology gets stuck in pilots that lead nowhere.

Only when all three pillars are aligned does AI become a strategic accelerator, instead of an expensive hobby. And that's where the real work begins.