Why Your AI Deployment Failed
The Rise of AI-Vaporware
Here’s how it usually goes:
Someone showed you an impressive demo that seemed to capture your problem and delivered a solution seamlessly. Maybe it was document search that seemed to understand context perfectly, or automated summarization that captured the key points every time, or insight generation that looked genuinely transformative. You’re a smart person who’s good at discerning vaporware from real technology, and this raised none of the usual flags, so you green-lit the project and talked up the solution to anyone who would listen. You finally had “AI”!
Then production hit. Responses became inconsistent, accuracy dropped from the 95% you saw in demos to maybe 70% in production, API costs skyrocketed to numbers that made the CFO’s eye twitch. The system needed constant hand-holding and tweaking from the vendor to stay on its rails. Was it a bait-and-switch? No.
The issue is that every instinct, metric, and tool you used to evaluate software can’t be applied the same way to AI tools.
Most likely, on top of the cherry-picked examples you’re used to looking for, there were hand-tuned prototypes that would work in a narrow window of examples (so the AI is hardcoded), and my favorite: the generic LLM wrapper. Maybe the demo was just prompting ChatGPT or Claude for results, which isn’t scalable, private, or reliable. But it does do great on a demo!
So the features were real, and they worked in a narrow use case or a small implementation, which is why the vaporware flags weren’t flying. But the scalability and the architecture considerations weren’t part of that demo, only the user-facing features were.
And with AI, the scalability and production architecture are also features.
This unfortunately isn’t an accident, it’s how AI is being sold right now. Because it works like a charm! In the era of hype AI, vendors are heavily incentivized to sell you demos, not systems. Because most people haven’t adapted their procurement process to buying AI yet, so this is the time to cash in for them.
Building a true production-ready AI system requires:
Careful model selection based on your specific use case
Training or fine-tuning pipelines that can be maintained
Data governance and privacy controls
Monitoring for model drift and performance degradation
Clear ownership of model weights and infrastructure
A path to independence from the vendor
All of that is complex, time-consuming, and expensive to explain. It introduces friction into the sales process because it forces the prospect to think about maintenance, governance, and long-term costs rather than just the exciting capabilities. If they’re not asking hard questions, why should you rock the boat? And if anyone asks, it’s just a matter of “customization for the proof of concept.”
So here is your cheat sheet on the new questions to ask to prevent buying AI-vaporware.
On model architecture:
“Is this using a generic LLM, or a purpose-built model trained for this specific task?”
“Which model specifically, what’s the parameter count, and why did you choose that architecture?”
“What data are you sending where? How often? For what purpose?”
“How does this handle our specific data structure and edge cases?”
On production readiness:
“What’s different between this demo and the production pipeline you’ll deploy?”
“What does the retraining process look like when our data changes or when there’s model drift? Who’s responsible for that?”
“What happens if your LLM provider changes their infrastructure or pricing?”
“How does performance scale with usage? What are the cost curves?”
On maintenance and independence:
“What monitoring is in place for model drift and performance degradation?”
“Who owns the model weights? Can we take this in-house?”
“What’s our path to independence from your team?”
“If you get acquired or shut down, what happens to our system?”
The vendors who have real answers to these questions are building actual systems, the ones who say “we can figure that out during implementation” are selling you demos.
If you’re evaluating AI deployments and want to avoid expensive disappointments, I’m happy to talk through what production-ready AI actually looks like. DM me or comment below. This isn’t a sales pitch, I do this because bad AI deployments give the whole field a bad name, and I’m tired of watching smart people get burned.