You Made It to Production: Now What?
Part 3 of 3: From POC to Production
This is the final blog in our three-part series on why most AI projects fail before they reach production and how to change that outcome. If you missed them, start with Part 1: Why 90% of AI Projects Fail Before They Launch to understand the strategic pitfalls, then read Part 2: Rethinking the AI Development Lifecycle to see how a wayfinding approach can set you up for success.
You have built the model. It is deployed. But no one is using it.
This is the quiet failure that haunts many AI teams. After surviving the gauntlet of strategy, experimentation, and integration, adoption falls flat.
Why Good Models Get Ignored
- Lack of Trust: If users do not understand how a model works or fear its outputs, they will not use it.
- Poor UX: If it is hard to use, it will not be used.
- No Training: Users are handed a tool with no guidance or support.
Design for Humans, Not Just Machines
Adoption is a human-centered design challenge. Models need to be:
- Trustworthy: Explainable, consistent, and safe
- Transparent: Able to show their work
- Configurable: Let users control data access and output settings
- Interactive: Include feedback loops like thumbs-up or down, comments, and override options
Test Before You Roll Out
Adoption starts before launch. Use test environments with real-time data to evaluate behavior and performance. Run A/B tests against current systems. Start with pilots before full-scale deployment.
Find the Sweet Spot
The best use cases for early adoption are high-friction and low-risk. Tasks people hate doing but that do not carry major business or legal risk are perfect targets. These wins build momentum and trust.
Takeaway: Production is not the finish line. Design for trust, usability, and feedback or your AI investment may never pay off.
Looking Back and Moving Forward
With this post, we wrap up our series:
- Part 1: Why 90% of AI Projects Fail Before They Launch explains why most projects stall before production.
- Part 2: Rethinking the AI Development Lifecycle introduces wayfinding as a flexible, science-inspired approach to building AI.
Together, these three posts provide a roadmap for taking AI from proof of concept to sustainable production.
Want to go deeper?
Join our upcoming webinar, End the POC to Nowhere Cycle: How to Beat the 90% AI Deployment Failure Rate, where we will share practical strategies for overcoming these challenges. Register here.
