"AI inference costs have fallen more than 90% in two years, driven by hardware improvements, model distillation, and infrastructure optimization — making production deployment dramatically more accessible." — a16z, August 2024
There's a phenomenon I've been calling "POC graveyard" — the accumulated pile of AI proofs-of-concept that organisations have built, demoed to enthusiastic applause, and then quietly shelved without ever reaching production.
I'm not guessing about this. I've talked to enough clients and colleagues to know it's a pattern. The demo impresses. The project gets greenlit. The POC gets built. The demo of the POC impresses. And then... it stalls. Somewhere between "this is exciting" and "this is deployed and being used," things fall apart.
The failure modes are consistent. Here's what they are, what they look like from the inside, and — most importantly — how to build POCs that are actually designed to ship.
Failure Mode 1: The POC Was Optimised for the Demo, Not the Problem
This is the most common failure, and it's subtle. POCs built for demos are built to work on the examples everyone prepares for the demo. They fail on the messy, edge-case-heavy, real-world data that production systems actually encounter.
What it looks like: The demo runs on a curated subset of clean data. When the system is connected to real data in production, accuracy drops significantly. The use cases that were chosen for the demo were easy — the hard ones were avoided because they'd undermine the demo's impact. The fix: In week 1, identify the hardest 20% of cases — the ones that would break a simple implementation. Build your POC against those. If it handles the hard cases, it'll handle the easy ones. If you're only testing the easy cases, you're not actually learning anything.Failure Mode 2: No One Owned the Path to Production
A POC is built, typically by a small team (often external consultants or an innovation team). Then it sits. No one in the organisation is specifically responsible for turning it into a production system. It's not on anyone's KPIs. The business unit is interested but doesn't have engineering resources. The engineering team has other priorities.
What it looks like: Endless "next steps" meetings that don't result in actual steps. The POC gets demonstrated again to new stakeholders, generating enthusiasm but not action. Six months later, the technology has moved on and the POC is quietly retired. The fix: Before starting the POC, define who owns the path to production. Which team will maintain this? Who has the budget to fund Phase 2? Is there executive sponsorship with actual accountability? A POC without a designated production owner is a demo, not a project.Failure Mode 3: The Integration Work Was Underestimated
The POC works in isolation. It's impressive. The production system needs to integrate with your CRM, your authentication system, your data pipelines, your monitoring infrastructure, your compliance review process. That work is substantial and wasn't scoped in the POC phase.
What it looks like: After the POC, someone does the actual integration scoping and realises it's three times the cost of the POC itself. The business case collapses. The project doesn't get funded. The fix: Part of a well-designed POC is a production integration assessment. What are all the systems this needs to connect to? What does the integration work actually involve? Getting a realistic answer to this during the POC phase, not after, means the business case that goes to leadership includes the real number.Failure Mode 4: It Worked, But Not Well Enough
Some POCs fail because the technology simply isn't good enough for the task. The accuracy isn't high enough. The latency is too slow. The model fails on critical edge cases that can't be accepted in production.
This is actually a success — you learned something important. But it's often treated as a failure, and the lesson extracted is "AI can't do this" rather than "AI can't do this yet, here's what we'd need to change."
The fix: Set explicit quality thresholds before the POC starts. Define: "This goes to production if we achieve X% accuracy on this evaluation set, with Y response time, handling Z edge cases correctly." If you don't hit the threshold, you know exactly what fell short and can make an informed decision about whether and how to close the gap.Failure Mode 5: The Evaluation Was Subjective
"The team thinks it works well" is not an evaluation. I've seen POCs make it to the production decision point with no quantitative evaluation at all — just the impressions of the people who built it and the executives who saw the demo.
Subjective evaluation leads to two failure modes: shipping a system that doesn't actually work well enough, and shelving a system that would have worked fine if someone had actually measured it.
The fix: Automated evaluation sets. Before the POC starts, create 50-100 test cases with known correct answers. Run the system against them. Get a number. Measure it again after each improvement. This doesn't capture everything (qualitative feedback matters too) but it gives you something objective to stand on.Building POCs That Ship: The Pattern That Works
The POCs that make it to production consistently share these traits:
They're scoped as "Phase 1" of a production system, not as experiments. The architecture decisions are made with production in mind. The data pipeline is built to be maintained, not just run once. The code is documented. They include integration assessment as a deliverable. Before the POC wraps, you know what Phase 2 (production integration) involves and what it costs. They have defined success criteria from day 1. Quantitative, measurable, agreed upon before work starts. There's a designated production owner before the POC begins. The person responsible for taking it to production is identified and aligned. They're evaluated against hard cases, not just easy ones. The hardest 20% of real-world cases are in the test set from the beginning.The difference between a POC and a Phase 1 is mindset. Build for production from day one.
Want to implement AI in your business? Book a 30-minute architecture call at blok-ai.com.