The Layer Between an AI Demo and Production
Good demos don't make it into production when the missing middle is ignored
Most AI demos work.
They load a curated dataset, run a trained model, and produce outputs that look sensible. The numbers move in the expected direction. The plots are stable. The conclusions appear reasonable. In the room, there is often a quiet sense of relief: the hardest part seems to be over.
From a technical standpoint, something important has been proven. The model can learn a signal. The data is not completely broken. The approach is viable.
And yet, many of these projects never make it any further.
There is no obvious failure. No catastrophic bug. No email announcing that the initiative has been cancelled. Instead, momentum dissipates. Meetings become less frequent. Ownership becomes blurry. Eventually, the demo remains what it always was: a promising artifact that never turned into a system.
What’s missing in these cases is rarely a better model or more data. Much more often, it’s an entire layer that sits between the demo and the production environment — a layer that doesn’t look impressive on slides, but that determines whether anything can actually scale.
Fundamentally Different Purposes
A demo is designed to answer a narrow question: can this model work on this data, under these conditions? It lives in a controlled setting. The scope is limited. The assumptions are implicit but shared by the small group that built it. Success is defined locally and temporarily.
A production system has a different mandate altogether. It must run repeatedly, often on imperfect data. It must survive changes in upstream systems, shifting definitions, and evolving expectations. It must produce outputs that can be trusted by people who were not involved in its design — and who may only encounter it when something looks wrong.
The transition between these two worlds is where most AI projects quietly fail.
The Missing Middle
This middle layer is not a single component, tool, or framework. It’s a set of design choices and mechanisms that translate exploratory work into operational behavior. It’s the scaffolding that allows a model to exist as part of a larger system rather than as an isolated experiment.
In practice, this layer does a number of unglamorous but essential things.
It validates inputs before they reach the model, checking whether today’s data still resembles what the model was trained on. It enforces contracts around schemas and definitions so that silent changes upstream don’t propagate unnoticed. It makes assumptions explicit — about time horizons, aggregation levels, thresholds — instead of leaving them buried in a notebook cell.
It also handles traceability. Which version of the model ran? On which data? With which parameters? Under which business rules? These questions are rarely urgent during a demo, but they become unavoidable in production. Without clear answers, even correct outputs can become suspect.
Equally important, this middle layer captures context. Not just what the model produced, but under what conditions it produced it. This is what allows teams to explain results weeks or months later, when someone asks why a number changed or why two reports no longer align.
No Middle, Much Confusion
When this layer is missing, failures don’t show up as crashes or exceptions. They show up as confusion.
A number looks slightly off in a meeting, and no one can quite explain why. The same model produces different results a month apart, and it’s unclear what changed — the data, the parameters, the assumptions, or the code. A downstream system breaks because an upstream definition shifted quietly. Each incident on its own seems minor. Together, they erode confidence.
This is how trust is lost in enterprise environments: not through spectacular errors, but through small, unexplained discrepancies.
Once trust starts to slip, usage declines. Not because the model is demonstrably wrong, but because it feels unreliable. Teams stop relying on it for decisions that matter. They may still consult it occasionally, but it no longer has authority. At that point, technical improvements — better performance, more data, more sophisticated models — rarely change the outcome.
The Boring Part
Skipping the middle layer is costly precisely because its absence isn’t obvious at first. The demo looks successful. The code runs. The outputs make sense. Only over time does the lack of scaffolding become apparent — usually when the system is already being questioned.
This is also why the missing middle is not just a technical problem, but an organizational one. Enterprise systems are expected to be defensible. They must withstand scrutiny, explain themselves under pressure, and behave consistently over time. The middle layer is what makes that possible.
The systems that do survive this transition tend to look surprisingly modest. They don’t optimize for elegance in the demo. They invest early in validation, traceability, and deliberately boring interfaces. They make explicit what is often left implicit. They prioritize stability over cleverness.
Quietly Successful
As a result, they rarely attract attention once they’re in place. They blend into existing workflows. They stop being discussed — which, in enterprise settings, is often the clearest signal of success.
That invisibility is not a lack of ambition. It’s a sign that the system has crossed the threshold from experiment to infrastructure.
Most AI projects don’t fail because the model didn’t work. They fail because nothing was built to carry the model into the messy, long-lived reality of production. The missing middle is where that work happens — quietly, slowly, and mostly out of sight.
I’ll be taking a short break over the holidays and will be back in January.
Until then, wishing you a joyful end to the year — and systems that keep working even when no one is watching.