Teams often fall into the trap of optimizing for model accuracy, a metric popularized by academic settings like Kaggle. In business, this is misleading. A highly accurate model might be too passive and miss opportunities. The focus must shift from pure accuracy to real-world business outcomes and ROI.
Before writing code, use the Snowball Sprint framework to align teams. It involves three exercises: storyboarding the user journey, creating a detailed 'digital twin' workflow diagram, and using a value matrix to quantify the business cost and benefit of potential AI outcomes, moving beyond simple accuracy.
Projects get stuck when a successful POC can't be deployed. This often happens because the demo was built on clean, synthetic data, hiding real-world challenges like data cleaning and hallucinations. Building with a thin slice of *real* customer data ensures the POC is an honest evaluation of a deployable solution.
Static wireframes fail to represent the dynamic, probabilistic nature of AI. A better method for rapid validation is to build a simple browser plugin that injects live, AI-generated content into your existing product. This allows for immediate, real-world user testing focused on the value of the content, not UI polish.
When an AI designed to replace a human expert fails to get traction due to cost or risk, pivot. Instead of replacing the expert, position the AI as an assistant that covers their 'blind spots' or moments of inattention. This adds a layer of safety, preserves the human's role, and creates clear ROI by preventing catastrophic failures.
Unlike traditional software, the core of an AI product is its dynamic, often unpredictable output. Static wireframes, even with placeholder text, are mere 'gargoyle rain spouts'—decoration that fails to represent the actual system. You can't validate an AI idea without building and testing the real, content-generating thing.
The 85% AI project failure rate isn't a technology problem. It stems from four business and process issues: failing to identify a narrow use case, using data that isn't clean or ready, not defining success and risk, and applying deterministic Agile methods to probabilistic AI development.
AI development makes identifying the right use case and wrangling data the new bottlenecks, not coding. This flattens traditional hierarchies. The most effective teams are integrated 'tiger teams' where UX designers manage RAG files and developers talk to customers, valuing adaptability over rigid job descriptions.
The classic 'pick two' project management triangle (fast, cheap, good) is altered by AI. You can achieve all three, but only by focusing on an extremely narrow use case or a 'thin slice' of data. Prove product-market fit on this small scale first, then expand once you get strong customer validation.
When facing a massive dataset, don't build for the whole thing. Isolate a representative 'thin slice,' such as 50 rules for a single technology like CloudTrail instead of 1,000 rules. Build a complete, working product for that slice to prove value and validate your approach before committing to the full-scale project.