Why Most GenAI Projects Still Fail to Reach Production
Three months after DAIS and I've now been in enough post-conference retrospectives to see the same pattern play out. The team that attended Summit comes back energized. They have three to five AI use cases they want to build. They get budget approval for a proof of concept. The POC works — or works well enough to get a demo approved. And then it sits. Six months later, it still hasn't shipped.
This is POC purgatory. It's the most common enterprise AI failure mode I see in 2024, and it's not a technology problem.
Why the POC Usually Works
A proof of concept is designed to succeed. The data used for the POC is clean, curated, hand-picked to make the demo work. The user interface is the happy path only. The model is evaluated on examples that were already known to be answerable. The latency is measured on a lightly loaded system. The cost is measured over a two-week trial, not annualized.
None of those conditions are true in production. Production data has the edge cases, the encoding errors, the schema drift, the missing values, and the business logic exceptions that someone knew about but didn't tell the AI team. Production traffic is spiky. Costs compound. The model that performed at 85% accuracy on the curated demo set performs at 62% on the real distribution of queries, and 62% is not good enough to ship.
The Missing Operational Discipline
The gap between a working POC and a production-ready AI system is filled by the same operational discipline that separates a working notebook from a production data pipeline. None of this is AI-specific:
Data quality gates — the system has to handle inputs that aren't clean, not just the inputs that are. Build validation before your retrieval layer. Log and quarantine inputs that fail validation. Treat bad input as an operational signal, not an edge case.
Monitoring — track output quality over time, not just at launch. LLM-based systems drift as underlying models are updated, as usage patterns shift, and as the knowledge base changes. If you're not measuring output quality continuously, you won't know when it degrades.
Fallback paths — production AI systems need defined behavior for failures. What does the system do when the vector search returns no results? When the model times out? When the generated answer fails a confidence check? These paths need to be designed and tested, not discovered when they happen.
Cost controls — model inference has per-call costs that don't exist in traditional software. A POC that runs 200 demo queries a week has a fundamentally different cost profile from a production system serving 200 queries per minute. Calculate the annualized inference cost before you commit to the architecture.
A Framework for Productionizing GenAI Responsibly
Before any GenAI POC moves to production, I now require four things to be in place: a labeled evaluation set (minimum 200 representative queries with expected answers), a monitoring dashboard that tracks output quality weekly, a defined fallback behavior for all identified failure modes, and an annualized cost estimate at expected production load. If any of these are missing, the project stays in POC until they exist.
This sounds like slowing down. It's actually the faster path to a production system that stays in production. The alternative — shipping without these foundations — produces a system that gets quietly turned off six months after launch because it's not trusted. As always, I'm here to help.