Why Most AI Projects in Mid-Size Companies Never Reach Production
The graveyard of AI projects is full of successful pilots. Proof of concepts that worked. Demos that impressed the board. Models that performed well in controlled testing. The people who built them were competent. The technology functioned as expected. And then, somewhere between "this looks promising" and "this is running in production," the project stopped.
According to IDC research, for every 33 AI prototypes companies build, four make it to production. S&P Global's 2025 survey found that 42% of companies abandoned most of their AI initiatives that year — up from 17% the year before. MIT's GenAI Divide research, based on 150 interviews and analysis of 300 public deployments, found that only 5% of AI pilots achieve measurable P&L impact. These numbers are not from organisations that failed to invest or failed to try. They are from organisations that invested seriously and tried earnestly, and still ended up in the same place.
The problem is not the technology. The problem is what happens after the demo.
The Pilot Is Not a Scaled-Down Version of the Production System
This is the core misunderstanding. A pilot is designed to answer a question: can this work? A production system is designed to answer a different question: can this be relied upon, maintained, and improved over time, at scale, in a real operating environment? These are not the same question, and the work required to answer them is not the same work.
A pilot runs on a curated dataset, in a controlled environment, with a small team paying close attention to it. When something breaks, the team notices immediately and fixes it. When the data is inconsistent, the team handles it manually. When the model produces a wrong output, the team catches it and logs it as a known issue to resolve later.
In production, none of these conditions hold. The dataset is live and messy. The team is not watching the system constantly. Wrong outputs go to real users, affect real decisions, and create real consequences. The system needs to be monitored, updated as input data shifts, retrained when performance degrades, and integrated with whatever the organisation's actual workflow looks like — not the simplified version built for the pilot.
The gap between these two environments is large. Most mid-size organisations underestimate it because they have never run an AI system in production before. Large technology companies have entire MLOps functions devoted to closing this gap. Mid-size companies frequently discover it exists only after committing to a production timeline.
Three Things That Kill the Transition
The first is the missing owner. Pilots are typically built by a project team — a combination of data scientists, engineers, and a business sponsor who championed the initiative. When the pilot succeeds and the conversation turns to production, the project team moves on. They have other work. The innovation team, if there is one, is already running the next pilot. The question of who will own the production system — monitor it daily, manage its retraining, troubleshoot its failures, and make decisions about when it needs to be updated — is often not answered until it is too late. The answer cannot be "whoever built the pilot." The pilot team's job was to prove feasibility, not to run a production service. Production AI requires operational ownership, and operational ownership requires a named person with the time and mandate to do it.
The second is the business process problem. An AI system that produces outputs no one is set up to act on does not generate value. This sounds obvious, but it is consistently underestimated. A demand forecasting model that produces weekly predictions means nothing if the procurement process does not have a step where those predictions are reviewed and acted on. A customer support triage model means nothing if the support team's workflow does not incorporate the model's classifications. McKinsey's research on successful AI implementations finds that organisations generating significant returns from AI are twice as likely to have redesigned end-to-end workflows before selecting modelling techniques. The technology does not insert itself into the organisation — the organisation has to reshape around it, and this is harder than building the model.
The third is model decay. AI models are not static. They perform well when the data they encounter in production resembles the data they were trained on. Over time, as the world changes, input data shifts — customer behaviour changes, product categories change, market conditions change — and model performance degrades. A model that was performing at 90% accuracy at deployment may be at 70% accuracy eighteen months later, with no obvious visible signal to users. Without monitoring infrastructure to detect this drift and trigger retraining, the model quietly becomes less useful while the organisation assumes it is still working. Most pilots do not build this monitoring in. Most mid-size organisations do not have the MLOps infrastructure to run it. The model decays, someone eventually notices that the outputs seem wrong, and the project is quietly shelved.
What "Treating AI as a Product" Actually Means
Organisations that successfully deploy AI in production tend to treat it differently from the start. They do not build a pilot and then figure out productionisation. They design the production system from the beginning and build the pilot inside that design.
This means answering operational questions before the build begins. Who will own this system? What does their daily responsibility look like? What monitoring will detect when the model is underperforming? What is the retraining cadence? What happens when the model produces a wrong output — who reviews it, who decides whether it is a model failure or a data failure, who has the authority to take the system offline? What is the escalation path when something breaks at 2am?
These questions feel premature when asked during a pilot. They are not premature. They are the questions that determine whether the pilot ever becomes something useful. An organisation that cannot answer them is not ready to build a production AI system — it is ready to build a demonstration of one.
The Realistic Timeline
The timeline most mid-size organisations plan for AI implementation — build the pilot, validate it, deploy it, benefit from it — compresses several months of work into an unrealistic sequence. The Gartner research is blunt: the average enterprise spends eight months getting from AI prototype to production, and only 48% of AI projects make it past pilot at all.
The realistic timeline for a mid-size company moving an AI application to production includes time for the data infrastructure work that the pilot glossed over, time for the process redesign that the workflow requires, time for the MLOps setup that monitoring and retraining depend on, and time for the organisational change management that adoption requires. None of these phases is glamorous. None of them appears in the pilot plan. All of them determine whether the investment produces value.
The question to ask before any AI development commitment is not "when can we have a demo?" It is "who will own this in production, what does their job look like, and what is our plan when the model stops performing?" An organisation that can answer these questions clearly is an organisation that is ready to move from pilot to production. An organisation that cannot is an organisation that is likely to produce another demo for the graveyard.