Cold Open

Most companies get it backwards.

They say “Data and AI,” as if AI is dessert—something you get to enjoy only after you’ve finished your vegetables. And by vegetables, they mean years of data modeling, integration work, and master‑data management. AI ends up bolted onto the side of a data office that’s already overwhelmed.

That mindset isn’t just outdated—it’s actively getting in the way.

It’s time to flip the script. It’s not Data and AI. It’s AI for Data.

AI as a Data Appendage: The Legacy View

In most org charts, AI still reports to the head of data. That tells you everything: AI is perceived as a tool to be used on top of clean data. The assumption is that AI becomes useful only after you’ve reached some mythical level of data maturity.

So what happens? You wait. You delay. You burn millions building taxonomies and canonical models that never quite deliver. When AI finally shows up, it generates dashboards or slide‑deck summaries. Waste of potential.

What If AI Is Your Integration Layer?

Here’s the mental flip: AI isn’t just a consumer of data—it’s a synthesizer. A translator. An integrator – an Enabler!

Instead of cleaning, mapping, and modeling everything up front, what if you simply exposed your data—as is—and let the AI figure it out?

That’s not fantasy. Today, you can feed an AI messy order tables, half‑finished invoice exports, inconsistent SKU lists—and it still works out the joins. Sales and finance data follow patterns the model has seen a million times.

The magic isn’t that AI understands perfect data. The magic is that it doesn’t need to.

MCP: OData for Agents

Remember OData? It promised introspectable, queryable APIs—you could ask the endpoint what it supported. Now meet MCP (Model Context Protocol). Think OData, but for AI agents.

With MCP, an agent can introspect a tool, learn what actions exist, what inputs it needs, what outputs to expect. No glue code. No brittle integrations. You expose a capability, and the AI takes it from there.

OData made APIs discoverable. MCP makes tools discoverable to AIs.

Expose your data with just enough structure, and let the agent reason. No mapping tables. No MDM. Just AI doing what it’s good at: figuring things out.

Why It Works in Science—And Why It’ll Work in Business

Need proof? Look at biology.

Scientific data is built on shared, Latin‑based taxonomies. Tools like Claude or ChatGPT navigate these datasets without manual schema work. At Carlsberg we’ve shown an AI connecting yeast strains ➜ genes ➜ flavor profiles in minutes.

Business data is easier. You don’t need to teach AI what an invoice is. Or a GL account. These concepts are textbook. Give the AI access and it infers relationships. If it can handle yeast genomics, it can handle your finance tables.

Stop treating AI like glass. It’s ready.

The Dream: MCP‑Compliant OData Servers

Imagine every system—ERP, CRM, LIMS, SharePoint—exposing itself via an AI‑readable surface. No ETLs, no integration middleware, no months of project time.

Combine OData’s self‑describing endpoints with MCP’s agent capabilities. You don’t write connectors. You don’t centralize everything first. The AI layer becomes the system‑of‑systems—a perpetual integrator, analyst, translator.

Integration disappears. Master data becomes a footnote.

When Do You Still Need Clean Data?

Let’s address the elephant in the room: there are still scenarios where data quality matters deeply.

Regulatory reporting. Financial reconciliation. Mission-critical operations where a mistake could be costly. In these domains, AI is a complement to—not a replacement for—rigorous data governance.

But here’s the key insight: you can pursue both paths simultaneously. Critical systems maintain their rigor, while the vast majority of your data landscape becomes accessible through AI-powered approaches.

AI for Data: The Flip That Changes Everything

You don’t need perfect data to start using AI. That’s Data and AI thinking.

AI for Data starts with intelligence and lets structure emerge. Let your AI discover, join, and reason across your real‑world mess—not just your sanitized warehouse.

It’s a shift from enforcing models to exposing capabilities. From building integrations to unleashing agents. From waitingto acting while you learn.

If your organization is still waiting to “get the data right,” here’s your wake‑up call: you’re waiting for something AI no longer needs.

AI is ready. Your data is ready enough.

The only question left: Are you ready to flip the model?