Context
Teams are often told to "use more AI," but they rarely have a structured way to identify where AI would actually help inside the workflows they already run.
Problem
AI conversations often stay too vague. Teams may have broad ambitions, scattered ideas, or pressure to experiment, but no clear starting point, prioritization logic, or shared way to decide which opportunities are worth exploring first.
Approach
- I built a lightweight discovery framework that moves from workflow context to friction points to AI-fit scoring.
- The framework was designed to evaluate opportunities based on practical criteria such as impact, feasibility, and risk. Instead of treating AI ideas as a generic wish list, the tool helps structure them into a ranked backlog that can support planning, discussion, and next-step decisions.
What I Built
- An intake flow that captures business context, workflows, and friction points in a structured format.
- A scoring view that ranks AI and automation opportunities and explains trade-offs.
- Output summaries that can fit into existing planning, roadmap, or discovery documentation.
- A prototype interface that helps move the conversation from "we should use AI" to "these are the opportunities worth evaluating first."
Outcome / What It Demonstrates
The tool reframes AI exploration from a vague strategy conversation into a more practical prioritization exercise. Instead of starting with broad AI ambition, users can identify a smaller set of opportunities worth piloting, along with the reasoning behind those priorities.
Lessons
- Most AI value lives inside specific, well-defined workflows, not flashy demos.
- Scoring is most useful as a conversation starter, not a final verdict.
- The best AI opportunities usually become clearer when the workflow, friction point, data need, and business outcome are defined first.