The Model Is Not the Product: Why ML Performance Metrics Miss the Point

The accuracy fallacy

An AI team presents to the steering committee. The model has 94% accuracy. F1 score is 0.91. Inference latency is under 200ms. The team is proud. The numbers are good. The steering committee approves the production deployment.

Six months later, the business impact is negligible. Users work around the model’s outputs. The process it was meant to improve hasn’t changed. The metrics that matter to the business (cost per transaction, customer satisfaction, processing time) are flat.

What happened? The model works. The product doesn’t.

This is the most common failure pattern we see in enterprise AI. Teams build excellent models and deploy them into environments where model quality is not the binding constraint. The constraint is everything around the model: the workflow it plugs into, the trust users place in its outputs, the feedback loops that allow it to improve, the organisational processes that need to change to capture the value.

The 80/20 split

We mapped the value chain of 16 enterprise AI deployments and found a consistent pattern: the model itself (the machine learning component, the thing the team spent most of its time building) accounted for roughly 15-20% of the factors that determined business value. The remaining 80% fell into five categories:

Integration design (25%). How the model’s outputs enter the user’s workflow. Is it a recommendation they can act on immediately? Does it require them to open another system? Does the output arrive at the right moment in their process, or does it land in a queue they check once a day?

User trust calibration (20%). Whether users actually trust and act on the model’s outputs. This isn’t a function of accuracy alone. It’s a function of explainability, consistency, the user’s ability to override, and the consequences of following a wrong recommendation. High-accuracy models that users don’t trust produce zero business value.

Feedback loops (15%). Whether the system learns from its deployment. In production, models encounter data distributions that differ from training. Without mechanisms for users to flag errors, for the team to track real-world performance, and for corrections to feed back into the model, accuracy degrades silently.

Process adaptation (15%). Whether the surrounding business process changes to take advantage of the model’s capabilities. If the model can classify loan applications in seconds but the downstream approval process still requires three days of manual review, the model’s speed is irrelevant.

Organisational alignment (5%). Whether the teams upstream and downstream of the model understand what it does, what it doesn’t do, and how to interact with it. Misunderstanding at any point in the chain (feeding it wrong inputs, misinterpreting its outputs, bypassing it entirely) breaks the value proposition.

A model with 94% accuracy in a broken workflow produces zero business value. A model with 80% accuracy in a well-designed workflow can transform a process.

Why teams focus on the wrong 20%

The bias toward model quality is understandable. It’s measurable. It’s within the team’s control. It has clear benchmarks. And it’s the part of the work that’s intellectually rewarding for the people doing it.

The other 80% is messy. Integration design requires understanding someone else’s workflow in detail. Trust calibration requires spending time with end users, not building models. Feedback loops require ongoing maintenance, not a one-time build. Process adaptation requires convincing another team to change how they work. Organisational alignment requires communication, training, and relationship-building.

None of this is what AI engineers were hired to do. None of it appears in their OKRs. And none of it is visible to the steering committee that approved the deployment based on model accuracy.

What production-ready actually means

Production-ready doesn’t mean the model performs well on test data. It means:

• The model’s outputs arrive in the user’s workflow at the right moment, in the right format, with the right level of explanation
• Users have been involved in the design, understand what the model does and doesn’t do, and trust it enough to act on it
• A feedback mechanism exists for users to flag errors and for the team to track real-world performance against training performance
• The business process has been adapted to capture the value the model creates
• The teams upstream and downstream understand their role in the value chain

These are organisational capabilities, not technical ones. And they’re the capabilities that determine whether a 94%-accurate model delivers business value or becomes another line item in the “AI investments that didn’t pay off” column.