Implement UseCaseScoringEngine with weighted multi-criteria evaluation — lab audio overview

Host: Welcome back to AI Solution Delivery. This is the chapter on AI Use Case Discovery and Data Readiness Assessment — the early-stage work of figuring out which AI ideas are actually worth building. You're in a course about shipping AI systems to real clients, and this chapter gives you the tools to run discovery workshops that produce defensible, data-driven recommendations — not just gut-feel opinions. Expert: Picture this. You're a consultant or an internal AI lead at a mid-sized insurance company. Leadership just came back from a conference, and now there are twenty-three proposed AI use cases on a whiteboard. Claims triage. Fraud detection. Chatbots. Document summarization. Underwriting assistants. Everyone has a favorite. Everyone thinks theirs is "high ROI." And you have one quarter of budget to pick maybe three. Without a rigorous scoring process, what happens? The loudest executive wins. Or the shiniest demo wins. Six months later, the project stalls because nobody checked whether the data was actually available, or whether the business value was real. This is the number one reason AI initiatives fail in the enterprise — not the model, not the infrastructure, but the fact that the wrong use case was picked in the first place. A proper scoring engine turns that whiteboard chaos into a ranked, weighted, defensible list. That's what you're building today. Host: So how does this fit with what you did before? In the previous exercise, you defined the data shapes — the structured blueprints that describe what a use case looks like, what criteria you're scoring against, and what a scoring result contains. Those blueprints are ready. Now you're building the engine that actually does the scoring. So what exactly will we build? Expert: You're building the brain of the scoring system. Think of it as an automated evaluator that takes a use case description — something like "automate claims triage for auto policies" — and produces a structured score across multiple dimensions: business value, technical feasibility, data readiness, strategic fit, and so on. Each dimension gets a numeric score, and then those scores get combined into one overall ranking. Here's the key idea — the conceptual aha. You're doing two very different things and stitching them together. First, you're using a large language model — an AI like GPT — to read the use case description and produce judgments on each dimension. Large language models are great at reading messy text and reasoning about it, but they're notoriously bad at giving you clean, structured output you can put in a database. So you're using a tool called Instructor, which is an open-source library that wraps around the language model and forces it to return its answer in a predictable, structured shape — every time, no surprises. That's the first half. The second half is pure math, no AI. Once you have the raw scores, you apply weights — because not every client cares equally about every dimension. A startup might weight speed-to-market heavily. A bank might weight regulatory risk heavily. Your engine takes those weights, multiplies them against the scores, adds them up, and produces one final number per use case. Then it ranks them, and attaches a confidence range so the client knows how sure the model is about each score. Host: Before you start, what's the one thing that trips people up here? Expert: The temptation to let the language model do everything. People think, "I'll just ask the AI to rank these use cases for me." Don't. The language model's job is narrow — read the description, score each individual dimension, return structured output. That's it. The weighting, the aggregation, the ranking, the confidence math — all of that is plain, deterministic code that you write yourself. Why does this matter? Because clients will challenge your results. They'll ask, "Why did use case A rank above use case B?" If the answer is "the AI decided," you've lost the room. If the answer is "here are the five dimension scores, here are the weights you chose, here's the arithmetic" — now you have a defensible process. Keep the AI on a short leash. Let it do the subjective reading, and let your code do the objective combining. Host: So what will you walk away with, and where does this lead? Expert: After this exercise, you'll be able to take any pile of proposed AI use cases and produce a ranked, weighted, explainable evaluation — the kind of artifact that holds up in a client steering committee. You'll know how to make a language model return reliable structured data, how to separate AI judgment from deterministic business logic, and how to express uncertainty in your results. This is a building block your team can drop straight into any discovery workshop or proposal process. Next, in the final exercise of this sequence, you'll wrap this engine in a web interface — so other applications, dashboards, and client-facing tools can call into it over the network and score use cases in batches. That turns your engine from a script into a service. Thanks for listening, and good luck with the build.