AI Decision Tree Builder 🌍 Live demo: https://surfab.github.io/ai-decision-tree/
AI Decision Tree Builder is a single-page HTML/CSS/JS app to evaluate if, when, and how to adopt AI across different business processes, using guided decision trees and structured scoring ( Fit/Readiness/Risk ).
Key Features
- Multi-use-case: 3 built-in cases (email triage, HR screening, predictive maintenance), easily extensible.
- Numeric scoring: Progressive calculation of Fit, Readiness, and Risk along the decision path.
- Node editor: Live editing of the tree (texts, choices, scores, connections).
- Offline-first: Runs entirely locally in a single file, ai-decision-tree.html, with persistence via localStorage.
- Decision Memo: Generates a board-ready memo including problem statement, recommendation, risks, and next steps.
- JSON export/import: Snapshot of state and configuration, versionable in Git.
- Tree visualization: Interactive graphical view of the decision tree, with the current path highlighted.
- Print / PDF: Layout optimized for printing or saving as PDF.
How to Use
- Clone the repository or download ai-decision-tree.html.
- Open the file in a modern browser (Chrome, Edge, Firefox, Safari).
- Select a case study from the top-left menu.
- Answer the questions by following the decision tree.
- Review:
a) Fit / Readiness / Risk scores
b) The suggested verdict
c) The generated Decision Memo
Use the Editor to adapt the tree to your context (nodes, copy, weights, outcomes).
Who It’s For
- Founders, C-level executives, and function heads deciding where AI actually makes sense.
- Innovation, digital, and data teams looking for a repeatable framework to assess AI use cases.
- Consultants who want a lightweight, offline, demo-friendly tool to use with clients.
Roadmap (Future Ideas) Path history and comparison across multiple use cases. Full undo/redo for decisions. Export tree as an image (PNG/SVG) for slides. New vertical templates (finance, retail, healthcare, etc.)
License
MIT (or the license of your choice).