The Missing Layer in AI Pipelines: Why Data Engineers Must Think Like Product Managers

AI is reshaping industries, but without the right data mindset, it won’t go far. Everyone’s trying to launch AI, be it predictive models, LLMs, or anything else. But when projects stall, the model is rarely the problem. The issues are upstream: messy data, unclear ownership, or mismatched expectations.

Data engineers used to be behind-the-scenes builders. Now they’re front and centre in AI delivery. But the bar’s higher. Moving data isn’t enough. You have to own what happens next, and that means thinking like a product manager.

Let’s talk about what that looks like, why it matters, and how to start.

Why AI Projects Fail

Most AI projects don’t make it to production. Gartner once said that 85% fail to deliver real results. If you’ve worked on one, you’re probably not surprised.

And here’s the root cause: it’s usually not the model’s fault. It’s the assumptions behind it. Incomplete data, fragile pipelines, teams working in silos. These aren’t just technical issues. They are product issues tagged as infra.

A couple of quick stories from personal experience.

A data science team at a big tech company developed a robust machine learning model. However, once it was deployed into production, its performance declined. The reason? The aggregate features used by the model came from another team, and the logic for those features changed upstream without anyone noticing. While the code operated smoothly, the underlying data was silently flawed.

At a cloud software company, we looked into customer feedback. One dataset. Three interpretations. Sales saw feature gaps. Support blamed delays. Engineering didn’t even know the field existed. The pipeline worked, but no one trusted what came out the other end.

These weren’t gaps in skill. They were gaps in thinking. We treat data like plumbing. But for AI, it is the product. And someone has to own it.

What Product Thinking Looks Like in AI Pipelines

So, what does “thinking like a product manager” mean for data engineers?

It means you don’t just ship code. You build something useful. You think in terms of outcomes. You care who’s using your data and why. 

Let’s break it down below.

Know Your Consumers

Product Managers obsess over users. You should, too. Who’s consuming the data? An ML model? A dashboard? A partner API?

Each one has different needs like latency, reliability, and clarity. You can’t meet those needs if you don’t ask upfront.

Define success

 “Pipeline ran” isn’t a success. Did it produce the right number of records with the correct data as expected? Was the schema intact? Were the key metrics in range?

These are your product KPIs. At one job, we added freshness checks to a feedback table. Just exposing that made AI teams trust it more and use it more.

Build for change

Good products evolve. Good pipelines do too. Write modular code. Set ownership boundaries. Version your datasets.

At a big tech company that I worked for, we treated aggregates like APIs. We versioned and documented them and made sure they were backwards-compatible. ML teams could adopt them without fear of breaking things later in production.

Align on the ‘Why’

PMs constantly revisit the problem they’re solving. You should ask: What decision will this data drive? Ask early. You’ll save yourself from painful rework later.

How to Start Thinking This Way

You don’t need a new title to do this. You just need to change your habits. Start with these:

Write Data Specs

 Before coding, write a one-pager, and make sure you include these

• Who uses the data?
• What inputs/outputs are expected?
• What defines success?
• What alerts should fire on failure?

You don’t need a lengthy explanation, just enough information to avoid rework later.

Do Stakeholder Reviews

You wouldn’t launch a product without feedback, and you shouldn’t launch data without it either. Bring in your users, analysts, PMs, and engineers. Show sample outputs. Ask if it makes sense.

At a big tech company I worked for in the past, we started doing this for behavioural data. It saved us from trust issues down the line.

Track Changes

Data changes. Schemas evolve. If someone depends on your table, they need a heads-up. Maintain a changelog. Communicate breaking changes and give them at least a sprint or a couple of weeks to analyze the impact and prepare for handling changes. Even a shared doc or a Confluence page is simple enough. The model usually isn’t what breaks; it’s the silently shifting data underneath.

Set SLAs

 If a model refreshes every 6 hours, a missed run isn’t just a glitch; it’s a problem. Set expectations:

• How fresh is “fresh”?
• Which checks must pass?
• What alerts should exist?
• Use tools to monitor. Even basic logs and alerts can earn trust.

Be the Bridge

Finally, AI isn’t just models. It’s data that people can trust, use, and act on. And that’s where you come in.

When you think like a Product Manager, you’re not just writing pipelines; you’re building products. You’re translating business intent into AI impact. The business analysts or data scientists are users of your product. They run their Artificial Intelligence and Machine Learning models on the data foundation that you have designed and built. 

This isn’t about chasing job titles. It’s about stepping up, asking “why” before “how, " and owning the whole experience, not just what your DAG runs at midnight.

Next time you’re designing a new pipeline, ask yourself: What would a product manager do?