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From AI Proof-of-Concept to Production: Crossing the Gap

Most AI POCs never ship. Here's why, and how to design a POC that actually reaches production.

Elena Voss·Head of AI Delivery, Aiporate··7 min read·Share on XLinkedIn

Key takeaways

  • Most AI POCs die before production.
  • The gap is reliability, integration, cost and ops, not the model.
  • Design the POC with production concerns in mind.
  • Involve the people who'll run it from day one.

The graveyard of AI initiatives is full of impressive POCs that never shipped. The gap between a demo and production is where most value is lost, and it's avoidable with the right design.

Why POCs stall

  • Built for a demo, not real conditions.
  • No plan for integration or ops.
  • Ignored cost and reliability at scale.
  • No owner for the production system.

How to cross the gap

  1. 1Define production success criteria up front.
  2. 2Test on real, messy data early.
  3. 3Plan integration, monitoring and cost from day one.
  4. 4Assign an owner for the live system.

Frequently asked questions

Why do AI POCs fail to ship?

Usually not the model, the gap is reliability, integration, cost and operations. POCs built only to impress skip the concerns that decide production viability.

How do I design a POC that ships?

Set production success criteria up front, test on real data, and plan integration, monitoring and cost from the start, with a named owner for the live system.

Who should own productionizing AI?

An ML/MLOps engineer with product support, and crucially, someone accountable for the live system, not just the demo.

Elena Voss

Head of AI Delivery, Aiporate

Elena has spent 12 years building and embedding AI and data teams inside B2B SaaS companies, from first pilot to enterprise-wide platform. At Aiporate she leads how forward-deployed talent is matched, onboarded and shipped to production.

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