(Formerly "A Methodology for Designing Scenario Solutions") A good solution is not a pile of features; it starts from a business anchor and stitches AI, data, and systems into one workflow that runs, is measurable, and can be reused.
A solution, at its best, is one business flow that runs, is measurable, and can be reused.
Many AI projects fail not because the model is weak, but because there is no business anchor: unclear for whom, what to solve, how to measure. Starting from "what the tech can do" often yields a clever demo that never lands in the business.
Core view: do not start from the model; start from the business problem. Define "what job to get done" first, then decide which intelligence, data, and systems are needed.
Not every scenario should get AI. 71 SI screens scenarios with a simple three-axis score: business value × feasibility × measurability. Only when all three are high is it worth prioritizing.
| Axis | Question to answer | How to judge |
|---|---|---|
| Business value | Is it worth doing? High volume, high cost, key impact? | Higher means higher priority |
| Feasibility | Are data and rules clear enough? Can it run? | Clearer means do it sooner |
| Measurability | Can the result be quantified and reviewed? | Only measurable things can iterate |
Example: a retailer first let intelligence run in just "ticket auto-classification + first-response", connecting orders and the knowledge base as scenario context; manual handling dropped about 30% within two weeks (illustrative: example figures, method demo only). After that it expanded to the full after-sales flow.
Launch is not the finish line; getting it to run is only the start. Real competitiveness comes from deepening the scenario again and again.
Use a scenario diagnostic workshop and needs assessment to turn an idea into a runnable first step.