From ETL to Intelligence: Why Clean Data Won’t Be Enough in the Age of AI

By Andrew Day, AI & Technology Executive on October 29, 2025

Data pipelines have become the backbone of the modern tech stack, orchestrating and moving data throughout an organization. As AI evolves, so too are these systems — shifting from mechanical assembly lines to cognitive threads that think, learn, and connect the enterprise together.

I started my career at Experian, the global data powerhouse operating credit bureaux around the world. Back then, data pipelines were built like fortresses — mainframe-based ETL processes (Extract, Transform, Load) designed to move data through tightly controlled systems.

Every field was mapped, every rule reviewed, every outcome tested. Precision mattered more than speed, and change was measured in weeks.

These pipelines were engineered for reliability, not agility — monuments to a world where control defined confidence.

And for that world, it worked. Clean, structured data was the gold standard.

From Structure to Understanding

At Travtus, around 2018, the world started to shift. We began experimenting with BERT, one of the first modern language models. At first, it was a small experiment — automating transactional, conversational processes.

But the power was clear almost instantly.

We could train models to classify, extract entities, and generate metadata. For the first time, we could structure the unstructured — transforming messages, emails, and notes into data that revealed why people were interacting.

It wasn’t fast. Labeling data took weeks. Training took months. But it opened the door to understanding.

Suddenly, data pipelines weren’t just about moving information — they were orchestrating intelligence.

The Generative Leap

Then came Generative AI, and the whole picture accelerated. We could now generate synthetic data, train models faster, and build richer metadata.

But it was when large language models developed reasoning capabilities that the real leap occurred.

Pipelines evolved beyond tagging or classification. They could summarize, extract, interpret, and even answer questions. And not just for single messages — but across entire customer journeys.

Now, a single message could be understood in context — part of a larger story, connected to patterns of behavior and outcomes.

Pipelines That Reason

By combining LLMs with retrieval and contextual memory, pipelines have started to reason — understanding not just what data says, but why it matters.

We have moved from static data flows to semantic systems — pipelines that can read, comprehend, and relate. They no longer just describe the past. They have begun to understand the present and even predict the future — how people behave, how systems respond, where friction or opportunity lies.

Data pipelines are no longer infrastructure. They are systems of reasoning.

The Rise of Agentic Pipelines

Now we’re entering the next phase: agentic pipelines. These systems don’t just follow instructions — they make decisions.

They can look at a dataset and ask:

“Do I have the information I need?”
“Should I retrieve more context?”
“Do I need to call another model or take an action?”

They can reflect, adapt, and re-evaluate.

It’s a profound shift — from pipelines that were designed to those that can design themselves. From flows that execute, to agents that think. These are not passive systems anymore.

They’re collaborators.

The Illusion of Clean Data

I often hear companies say,

“We’ve invested heavily in our data — making sure it’s structured and clean.”

And I can’t help but think:

You’ve spent a lot of money building pipelines for the world that was, not the world that’s coming.

For decades, clean data was the goal because pipelines couldn’t think. Every transformation, every rule, had to be designed by people. But now, pipelines can review data, write their own cleaning logic, pull context, and progress autonomously.

The focus is shifting from data cleanliness to data cognition — from “Did we clean this correctly?” to “Did we understand this correctly?”

The winners won’t be the companies with the cleanest data, but the ones with the smartest systems.

The Mindset Shift

This isn’t just a technological step forward. It’s a mindset shift. For years, we’ve designed systems for control. Now, we must design them for intelligence.

The companies that thrive in the next decade will see pipelines not as plumbing, but as living cognitive threads — running through their organizations, constantly reasoning, sensing, and learning.

The pipes no longer carry data. They carry understanding.

And that changes everything.