Rethinking the AI Development Lifecycle

Part 2 of 3: From POC to Production

This is the second post in our three-part series on why most AI projects fail before they reach production and how to change that outcome. If you missed Part 1, Why 90% of AI Projects Fail Before They Launch, we covered the strategic missteps that cause initiatives to stall before they ever get off the ground. In this post, we will look at how to build AI differently so it has a real chance of success.

If AI projects fail differently, they need to be built differently. Traditional agile or waterfall methodologies fall short because they assume a linear, deterministic path. AI demands exploration, experimentation, and flexibility.

Enter Wayfinding

We call our approach wayfinding. Inspired by the scientific method, it is a structured but flexible framework that helps organizations reduce risk and increase the odds of success.

Wayfinding has five key phases:

1. Strategy and Alignment
   Before a single experiment runs, you need a shared understanding of what problem you are solving, why it matters, and how success will be measured. Misaligned projects rarely make it past the demo stage.
2. Technology and Data Assessment
   You do not always need big data, but you do need good data. Wayfinding focuses on identifying what data exists, where it lives, and whether it is good enough to support the intended model. It also evaluates whether your infrastructure is ready for what comes next.
3. Applied Experimentation
   There is no shortcut to finding the right model. You have to try multiple approaches and see what works. Experienced practitioners know how to prioritize the right experiments and, just as importantly, which ones to avoid.
4. Research and Evaluation
   Each model needs to be benchmarked against real-world criteria: accuracy, latency, interpretability, and cost. Wayfinding embeds evaluation from day one so you do not get to the end and realize you cannot trust the results.
5. Trust-Building
   If no one uses the model, it is a failure, even if it is technically brilliant. From transparency and explainability to performance and usability, trust is a prerequisite for adoption.

Takeaway: Replace agile with wayfinding. Treat AI like science, not software, and build cross-functional teams that can think strategically, test rigorously, and align continuously.

Up Next in the Series: In Part 3, we will examine how to operationalize AI at scale, ensuring models stay reliable, maintain performance, and continue delivering value long after launch.

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 real-world strategies for avoiding failure and creating sustainable AI success. Register here.

‍