Why Unowned Evals Kill Long-Term ROI

Big Fortune 500 company. Three years ago they deploy a pricing model. Data science built it. Engineering maintains it. Operations monitors it. Sounds good, right?

Except nobody owns the evaluation.

Performance tanked. They didn't notice for six months. Customers complained. Nobody had data to show what was happening. Markets shifted. The cost function didn't update. The model just kept running, making worse and worse decisions, while everyone figured someone else was handling it.

Here's the real problem: it wasn't technical. The model was fine. The infrastructure was fine. The issue was organizational. Nobody was responsible. And when nobody's responsible, nobody does the work. That's how you end up with AI systems that silently stop making money.

This is the thing nobody talks about. Everyone focuses on model accuracy. But the thing that actually kills long-term ROI? Organizational structure. When nobody owns evaluation, evaluation doesn't happen. When evaluation doesn't happen, you get crushed.

The Ownership Problem

Here's what happens when a model goes to production. Responsibility scatters everywhere:

• Data science: Built it, now they're sick of it, they want to build new models
• Engineering: Keeping the servers running and the latency low, not their job to check if it's accurate
• Product: Cares about the customer experience, doesn't own the model metrics
• Operations: Monitoring the infrastructure, but not qualified to say if the model's any good
• Business: Knows this is supposed to make money, can't actually evaluate it technically

So who's actually checking whether the model's working? Nobody. Everyone assumes someone else is on it. And the model just quietly degrades while everyone's looking somewhere else.

In normal software engineering, DevOps owns uptime. SRE owns performance. But in AI? There's nobody. We don't have that role. So models drift undetected for months or years.

RACI for AI Evaluation

Here's how to fix this mess. Use RACI (Responsible, Accountable, Consulted, Informed) to make it crystal clear who's doing what:

See the pattern? One person (the evaluation owner, could be ML Ops, could be whoever you choose) is accountable for almost everything. That's the point. Someone has to walk in on Monday morning and actually look at the metrics from the weekend. That's their job. Not as a side task. As their primary responsibility.

How to Actually Own Evaluation

Different orgs handle this differently, but the ones that work all have someone with explicit accountability. Here are three patterns that don't completely suck:

Pattern 1: Dedicated ML Ops Team (Big Companies)

If you've got a thousand people and ten production models, you can afford to build a team around this:

• ML Ops Engineer: Builds the monitoring dashboards, sets up alerts, keeps infrastructure alive
• ML Evaluation Specialist: Defines what metrics matter, figures out when things are broken, designs how we detect drift
• ML Governance Lead: Owns the whole function, reports to whoever's in charge (VP Engineering probably), makes sure this doesn't get ignored

They sit between data science and everyone else. Data science builds models. This team makes sure those models actually work and keeps reporting back on whether they're making money.

Pattern 2: Data Engineering Owns It (Medium Companies)

Mid-size orgs (100-500 people, few models) sometimes stick this with data engineering:

• ML-Focused Data Engineer: Takes evaluation as an extension of data pipeline work
• Their job: Define what success looks like, set up monitoring, track metrics
• The handoff: Data science builds, data engineering evaluates

This works if your data engineering team is strong and not already drowning. The problem is that evaluation tends to lose priority when pipes go down.

Pattern 3: Product Manager Owns It (Consumer AI)

For stuff customers actually interact with (recommendations, search ranking), sometimes the PM owns evaluation:

• Product Manager: Defines the business metrics, tracks if they're moving in the right direction, decides when to retrain
• Why this works: PMs already think in business terms. They get it.
• Why it's risky: PMs aren't data people. They need data engineering backing them up.

This only works if you've got data engineering and ML Ops supporting them to actually build the monitoring infrastructure.

Treat Evaluation Like an Actual Product

The teams that nail this? They don't treat evaluation as a checkbox or overhead. They treat it like a product. With users. With requirements. With quality standards. With a roadmap.

• Users: Data scientists looking at evaluation results, engineers responding to alerts, PMs watching business metrics
• Requirements: Metrics updated every two hours max, alerts that don't lie, dashboards that actually help people figure out what's broken
• Quality standards: False alerts are expensive. Missed alerts are worse. This infrastructure gets tested.
• Roadmap: New metrics. Better drift detection. Improved dashboards. This is actively maintained, not abandoned.

When evaluation is a real product, it gets real resources. When it's treated as overhead, it withers.

What Does This Actually Look Like?

How to Make Evaluation Actually Happen

If you don't have someone explicitly owning evaluation right now, here's how to fix it:

Week 1-2: Pick Someone. Give Them the Job.

Don't make it fuzzy. Pick an actual person and tell them: this is your job. Your KPI. Your responsibility. When leadership asks "is this model working?" the answer comes from you.

They're the single source of truth on whether your model's making money or not.

Week 2-3: Get Everyone in a Room and Agree on Costs

Bring together the evaluation owner, data science, and product. Argue through what each kind of error actually costs. Write it down. Get sign-off.

This is the foundation for everything else. Get it wrong and nothing downstream works.

Week 3-4: Build the Monitoring Infrastructure

Dashboards. Alerts. Daily tracking. The evaluation owner drives this, possibly with help from data engineers.

Keep it simple at first: track cost-per-error daily. Alert when something gets weird. Show it to your executives. They need to see this.

Ongoing: Weekly Sync

The evaluation owner runs a 30-minute meeting every week with data science, engineering, and product:

• What happened last week?
• Anything weird in the alerts?
• Do we need to retrain or investigate?
• Did anything change in the business that affects what we're optimizing for?

This makes evaluation a real practice. It's not a dashboard nobody looks at. It's a conversation that happens every week. The team knows what's happening. When something breaks, you fix it fast.

What Actually Changes When Someone Owns It

The numbers from orgs that nail this are compelling:

All of this comes from one thing: someone wakes up every day whose job is to make sure evaluation is happening.

Simple, right? One person owns evaluation. They report to someone in leadership. They work with data science and engineering. They report metrics to product and business. Clarity. Accountability. No ambiguity.

The Annual Org Check

Every year when you're reviewing your structure, ask: "Who owns evaluation for each model we have running?"

If the answer is "well, the team handles it" or "data science sort of owns it" or "engineering looks at it sometimes," you're going to lose money. You don't have ownership.

If the answer is "Vikesh owns it and reports to the VP of Engineering," you're fine. Vikesh is accountable. Vikesh will make it happen.

Your First 30 Days

If you're starting from nothing, here's what 30 days looks like:

• Day 1: Appoint someone. This is their job now. Make it official.
• Day 2-3: Get that person with data science and product. Argue through what errors cost. Document it.
• Day 4-7: Basic dashboards. Daily metrics. Alert when something gets weird.
• Day 8-14: Look back at the last 90 days of data. Is the model drifting?
• Day 15-21: If you found problems, make a plan to investigate and fix them.
• Day 22-30: Start your weekly meeting. Every Monday, 30 minutes: what happened, what's next.

You've got 30 days to go from chaos to having a person who owns this, infrastructure that tracks this, and a rhythm of meetings where this is discussed. That's the difference.

The companies that crush it with AI don't have smarter data scientists or better models. They have organizational clarity. Someone owns evaluation. That's it. That's the secret. When evaluation ownership is clear, ROI stays real.