That study claiming “95% of AI POCs fail” has been making the rounds. It’s clickbait nonsense, and frankly, it’s not helping anyone. The real number? Nobody knows, because nobody’s tracking it properly. But here’s what I do know after years of watching teams build AI systems: the study masks a much more important problem.
Teams are confused about how to design POCs that survive beyond the demo stage. There is no playbook.
Most AI POCs die because they were designed to die. They’re built as disposable demos, optimized for executive presentations rather than production reality. They burn through cloud credits, rely on perfect conditions and perfectly structured data, and quickly collapse when real users start to touch them. If they don’t collapse then, often under scale they collapse when the design problems emerge under strain, leading to more serious failure.
But it doesn’t have to be this way.
After watching hundreds of AI projects at Docker and beyond, I’ve seen the patterns that separate the 5% that make it from the 95% that don’t. Here’s the playbook I wish every platform and MLOps team had from day one.
The New Foundation: Remocal Workflows
Before we dive into the rules, let’s talk about the biggest shift in how successful teams approach AI development: remocal workflows (remote + local).
Running AI locally isn’t just about saving money—though it absolutely does that. It’s about maintaining developer velocity and avoiding the demo theater trap. Here’s how the best teams structure their work:
- Test locally on laptops for fast iteration. No waiting for cloud resources, no surprise bills, no network latency killing your flow. The nature of building with AI should be making the process feel very interactive.
- Burst to remote resources for scale testing, production-like validation, or when you actually need those H100s. It should feel easy to move AI workloads around.
- Keep costs transparent from day one. You know exactly what each experiment costs because you’re only paying for remote compute when you choose to.
POCs that incorporate this pattern from day zero avoid both runaway bills and the classic “it worked in the demo” disaster. They’re grounded in reality because they’re built with production constraints baked in.
The Nine Rules of POC Survival
1. Start Small, Stay Small
Your first instinct is wrong. You don’t need the biggest model, the complete dataset, or every possible feature. Bite-sized everything: models that fit on a laptop, datasets you can actually inspect, and scope narrow enough that you can explain the value in one sentence.
Early wins compound trust. A small thing that works beats a big thing that might work.
2. Design for Production from Day Zero
Logging, monitoring, versioning, and guardrails aren’t “nice to haves” you add later. They’re the foundation that determines whether your POC can grow up to be a real system.
If your POC doesn’t have structured logging and basic metrics – observability – from the first commit, you’re building a disposable demo, not a prototype of a production system.
3. Optimize for Repeatability and Model Improvement, Not Novelty
Infrastructure should be templated. Prompt testing should be in CI/CD. Model comparisons should be apples-to-apples benchmarks, not “it felt better this time.” What’s more, POC designs can and should assume existing model families will continue to rapidly improve. That includes larger context windows, greater accuracy, lower latency and smaller resource consumption.
The sexiest part of AI isn’t the novel algorithm—it’s how we’re learning to frame problems in ways that make AI more reliable at scale.
4. Think in Feedback Loops
This is the big one that separates amateur hour from production-ready systems. Separate your non-deterministic AI components from your deterministic business logic. Build in layers of control and validation. Domain knowledge is still your magic ingredient.
In a remocal setup, this becomes natural: your agent loops can run locally for fast iteration, while tool execution and heavy compute burst to remote resources only when needed. You get reliability from layered control, not from hoping your model has a good day.
5. Solve Pain, Not Impress
Anchor everything to measurable business pain. Real users with real problems they’re willing to pay to solve. If your POC’s main value proposition is “look how cool this is,” you’re building the wrong thing.
Kill the vanity demos that only look good in slideware. Build the boring solutions that save people actual time and money.
6. Embed Cost and Risk Awareness Early
Track unit economics from day one. What does each request cost? Each user? Each workflow?
Benchmark small vs. large models. Cloud vs. local execution. Know your trade-offs with real numbers, not hand-waving about “cloud scale.”
7. Make Ownership Clear
Who owns this thing when it breaks at 2 AM? What are the SLAs? Who’s responsible for retraining the model? Who pays for the compute?
Don’t let POCs drift in the organizational void between research labs and operations teams. Assign owners, responsibilities, and lifecycle management from day one.
8. Control Costs Upfront
Transparent cost per request, user, and workflow. Hard budget caps and kill switches. No surprises in the monthly cloud bill.
Remocal workflows make this natural: you default to local execution and only burst remote when you consciously choose to spend money. Your costs are predictable because they’re intentional.
9. Involve Users From Day Zero
Co-design with real users, not executives who saw a ChatGPT demo and want “AI for everything.” Measure adoption and time saved, not just accuracy scores.
The best AI POCs feel like natural extensions of existing workflows because they were built with the people who actually do the work.
Why This Actually Matters
Most failed AI POCs never had a chance. They were too big, too expensive, too disconnected from real problems, and too optimized for demo day rather than daily use.
By flipping the script—starting small, designing for production, involving real users, and building on remocal workflows—you dramatically increase your odds of building something that ships and scales.
The difference between a successful AI POC and a failed one isn’t the sophistication of the model. It’s the boring engineering decisions you make on day zero.
Stop treating AI POCs as disposable demos. Treat them as the first draft of a production system.
Jim Clark is Principal Engineer for AI at Docker, where he helps teams build AI systems that actually make it to production. He’s spent the last decade watching the gap between AI demos and AI products, and occasionally bridging it.