Most enterprise AI POCs never reach production.
The number that gets quoted is 87%. It goes back to a 2019 VentureBeat piece on data science projects. In 2025, MIT’s Project NANDA published The GenAI Divide: State of AI in Business, and the number landed even higher — about 95% of enterprise generative AI pilots showed no measurable P&L impact. Only around 5% achieved rapid revenue acceleration. Other studies keep landing in the same band.
Pick a number. They all say the same thing: most of what gets demoed never ships.
Here’s the part that should get more attention.
It’s not technical failure. The MIT team reviewed 300 public AI deployments and interviewed 150 enterprise leaders, and their conclusion matches the earlier surveys: the core issue isn’t model quality. It’s flawed integration into the enterprise — the “learning gap” between a capable model and an organization that hasn’t built the workflows, data access, and governance around it. That gap is what kills most POCs.
Algorithmia’s State of Enterprise ML survey — from teams actually trying to deploy ML, not lab experiments — puts numbers on it. 42% cite infrastructure complexity. 31% hit regulatory roadblocks after the POC was approved. 28% couldn’t manage cost — what worked in dev exploded in production. 26% struggled with data governance: getting the right data, with the right permissions, at the right time.
Notice what’s not on that list. “The model didn’t work.”
The gap between POC and production is where the real engineering lives. And it’s the part almost nobody budgets for.
The POC runs on a laptop or one cloud GPU. Production needs a cluster.
The POC used a clean dataset. Production needs all the data — governed, permissioned, audited.
POC latency was best-effort; five seconds was fine. Production has a 200-millisecond SLA and 99.9% uptime.
The POC had one developer. Production has hundreds of users with role-based access.
The POC had no audit trail. Production needs complete logging.
POC model updates were ad hoc. Production needs CI/CD, versioning, rollback.
The POC skipped compliance review. Production means HIPAA, SOC2, GDPR.
The POC had no cost monitoring. Production needs chargeback.
Every line in that gap is work someone didn’t budget for when they approved the demo.
Then there’s the cost shock.
An A100 GPU runs about $3 to $4 an hour on the major clouds. Around the clock, that’s roughly $2,800 to $3,000 per GPU per month. A typical production setup might use 16 GPUs for training and 4 for inference — about $57,000 a month. Five modest enterprise models and you’re approaching $3.4 million a year.
I’ve watched this play out inside engagements. A small team builds a POC in three months, gets 89% accuracy, runs it for $5,000 a month. Everyone’s excited. Then legal gets involved. Compliance says the data can’t leave the data center. Uptime requirements appear. Multiple business units want in. The $5K a month becomes a $200K a month estimate. The 3-month POC becomes an 18-month infrastructure program. You need a platform team, not two data scientists.
That’s how projects end up in the 87%.
One more detail from the MIT data worth sitting with: more than half of generative AI budgets went to sales and marketing tools. The biggest measured ROI showed up in back-office automation. Money follows what’s easy to demo — not what’s valuable.
There’s a second failure mode that gets less airtime. Some POCs fail because they chose the wrong system in the first place — generative AI where a rule would do, an LLM where traditional ML would do, an agent where a plain function would do. IBM puts it cleanly: most failures to reach production don’t come from bad models. They come from choosing the wrong system.
So there are really two ways POCs die. You picked the right tool and never planned for production. Or you picked the wrong tool and the demo was never going to scale.
The difference between the 5% that make it and everyone else isn’t model quality.
The 95% didn’t choose. They let circumstances choose for them. They built the POC without thinking about production. They optimized for speed without thinking about compliance. They said yes without understanding the cost.
The 5% understood their constraints before the POC. They planned for production from day one.
The teams that get this right won’t be the ones with the best models. They’ll be the ones who planned for production before they wrote the first line of POC code.
Next in this series: how to pick use cases — and how to know when AI is the wrong answer entirely.
#AI #EnterpriseAI #MLOps #GenAI
