From POC to Production: Why Most AI Projects Fail (And How to Avoid It)
The gap between a working demo and a production system is larger than most teams realize. Here's what actually matters.
The Production Gap
The AI industry has a dirty secret: most projects never make it to production. Gartner estimates 85% of AI projects fail to deliver value. The problem isn't that the AI doesn't work — it's that teams underestimate everything else required to deploy and maintain AI systems.
A working Jupyter notebook is maybe 10% of a production system. The other 90% is data pipelines, monitoring, error handling, scaling, security, and operational tooling. Teams that plan only for the 10% fail.
This research examines the systematic causes of AI project failure and presents a framework for planning, building, and maintaining AI systems that actually ship and stay running.
Research Findings
Infrastructure Underestimation
Teams typically allocate 20% of effort to infrastructure but it requires 60%. The gap causes timeline and budget overruns that kill projects.
Data Quality Impact
73% of production AI issues trace back to data problems — drift, quality, availability. Model issues are comparatively rare. Data infrastructure is the foundation.
Monitoring Gaps
Only 15% of AI teams have adequate production monitoring. The rest discover problems from user complaints. By then, damage is done.
Iteration Velocity
Teams that can deploy model updates in days outperform teams with monthly cycles by 4x on business metrics. Speed of learning beats initial model quality.
The Production Framework
Planning for Production from Day One
Before writing any model code, answer: How will data flow into the system? How will predictions flow out? How will you monitor quality? How will you update models? How will you handle failures? What's the rollback plan? If you can't answer these questions, you're not ready to build. Planning this upfront prevents expensive rework later.
Data Infrastructure First
Build data pipelines before model pipelines. You need: reliable data ingestion, quality monitoring, versioning, and feature stores. The model is easy to replace; the data infrastructure is not. Teams that skimp on data infrastructure spend all their time firefighting data issues instead of improving models.
Observability and Monitoring
Operational Readiness
Before launch, you need: runbooks for common failures, on-call rotations, escalation paths, rollback procedures, and load testing results. You need to have practiced incident response. AI systems fail in novel ways — your team needs to be ready to diagnose and fix issues under pressure.
Ship AI That Stays Running
We help teams build the infrastructure and processes needed to deploy AI that delivers value in production.