The graveyard nobody talks about
Every organisation running AI projects right now has a graveyard. It's full of proofs of concept that worked beautifully in a controlled environment, impressed everyone in the presentation, and then quietly disappeared.
No official cancellation. No post-mortem. Just a Confluence page that nobody updates and a Slack thread that went cold in December.
This is not a technology problem. The technology usually works fine. The proof of concept fails for reasons that have nothing to do with the model, the architecture, or the data quality.
What a POC is actually optimised for
A proof of concept is designed to answer one question: can this work? The demo environment is controlled. The data is clean. The edge cases are excluded. The person presenting it knows exactly which inputs to use.
That's by design. You're proving feasibility, not fitness for production.
The problem comes when the organisation treats a successful POC as evidence that the thing is ready to ship. It isn't. A successful POC proves that the idea is sound under ideal conditions. Production is not ideal conditions.
Production is messy data, inconsistent user behaviour, legacy system integrations, compliance requirements that nobody mentioned during the discovery phase, and a support team that needs to maintain something they didn't build and don't fully understand.
The POC was never designed to survive contact with that reality.
The four gaps that kill AI projects
The data gap. The POC ran on a curated dataset or a sandbox environment. Real production data is noisier, more inconsistent, and often structured differently than anyone assumed. The model that performed at 94% accuracy in testing quietly degrades to 71% on live data - and nobody notices until something breaks.
The integration gap. In the demo, the AI feature existed in isolation. In production, it has to connect to the CRM, the ERP, the legacy system that runs on a twelve-year-old codebase, and three internal APIs that have no documentation. Each integration point is a failure mode that didn't exist in the proof of concept.
The ownership gap. The POC was built by an R&D team or an external vendor. When it comes time to ship, nobody has clearly answered: who owns this? Who maintains it? Who fixes it at 2am when it breaks? If engineering didn't build it, they're inheriting something unfamiliar. If the vendor built it, you have a dependency that doesn't scale.
The expectation gap. The demo was optimised to look impressive. The people who approved further investment did so based on that impression. By the time the project reaches production, the scope has grown, the timeline has compressed, and the original success criteria have been quietly replaced with something larger and vaguer. The project can't win because the goalposts moved.
Why R&D teams are structurally set up to produce POCs
Research and development exists to explore what's possible. That's a valuable function. But the incentives inside R&D are aligned with exploration, not with shipping.
A new capability demonstrated is a win. A technical problem solved is a win. A model that performs well in a controlled environment is a win. What happens after the demo is usually someone else's problem.
This isn't a criticism of R&D teams - it's a structural reality. If you want R&D outputs to make it to production, you have to design a handoff process that accounts for the gap between research and engineering, and you have to do it before the POC is built, not after.
What changes the outcome
The POCs that actually ship share a few characteristics:
Production constraints are defined upfront. Before the first line of code is written, someone has answered: what systems does this need to integrate with, what does the data actually look like in production, and who is responsible for maintaining this after launch? These questions feel premature during the exploration phase. They feel catastrophic when you try to answer them after the demo.
Engineering is in the room from the start. Not as a passive audience for the demo. As an active participant in the design. When the people who will own the production system help design the proof of concept, the proof of concept gets built with production constraints in mind.
The success criteria survive the demo. Define what success looks like in production before you define what success looks like in the POC. If the production definition of success is unclear, the POC will be optimised for the demo instead - and it will look great right up until it doesn't.
The handoff is treated as a project, not a formality. Moving from POC to production is not a handover meeting and a GitHub repo. It's a project with its own timeline, resourcing, and milestones. Teams that treat it as overhead consistently underestimate what it takes.
The uncomfortable truth
Most organisations are better at generating AI pilots than at shipping AI products. The pipeline is full at the top and empty at the bottom.
That imbalance isn't going to be fixed by better models, more compute, or a larger R&D budget. It gets fixed by treating the path from proof of concept to production as a first-class problem - one that requires deliberate design, cross-functional ownership, and the same rigour applied to the research itself.
The technology is rarely the bottleneck. The process almost always is.
Written by
Martin Dimoski
Senior R&D Executive & AI Systems Builder