From POC to Production: Closing the AI Deployment Gap

The graveyard of AI initiatives is filled with successful proofs of concept that never reached production. After shepherding dozens of projects through this transition, we've identified the patterns that separate success from expensive failure.

The POC Illusion

Proofs of concept succeed in controlled conditions: clean data, unlimited iteration time, success measured by model metrics. Production demands entirely different qualities: handling messy real-world data, operating within latency constraints, and delivering business value under operational conditions.

A model achieving 95% accuracy on curated test data might manage 80% on production data streams. That 15-point gap often determines viability. We insist on testing against production-representative data early—ideally before significant development investment.

Technical Debt Compounds

POC code optimises for experimentation speed. Production systems require reliability, maintainability, and scalability. The translation isn't incremental improvement—it's often wholesale reconstruction.

We've adopted practices that reduce this gap: production-grade infrastructure from project start, modular architectures that separate experimentation from serving, and coding standards that accommodate both rapid iteration and long-term maintenance.

Integration Complexity

AI models rarely operate in isolation. They consume data from upstream systems, deliver predictions to downstream processes, and must integrate with existing business workflows. This integration work often exceeds model development effort.

We map integration requirements during project scoping, not after model development. Understanding data sources, latency requirements, and consumption patterns shapes architectural decisions that would be costly to change later.

Organisational Readiness

Technical success means nothing without organisational capacity to operate AI systems. This includes monitoring capabilities, response procedures for model failures, and governance structures for model updates.

We assess organisational readiness early and build capability development into project plans. Sometimes this means recommending against AI deployment until foundational capabilities exist—a recommendation that saves more money than it costs.

The Staffing Reality

POCs can succeed with borrowed data scientists. Production systems require dedicated teams for ongoing operation. Organisations often underestimate this staffing requirement, leading to degraded systems that deliver diminishing value over time.

Our Production Checklist

Before declaring a project production-ready, we verify: data pipelines handle real-world messiness, latency meets business requirements, monitoring covers meaningful failure modes, documentation supports operational teams, and rollback procedures have been tested.

The path from POC to production isn't a phase—it's a fundamentally different engineering challenge. Success requires treating it as such from project inception.