🚀 AI Strategy Matrix Builder

A Streamlit application for creating and visualizing AI strategy matrices to prioritize AI use cases based on actionability, feasibility, and business value.

This project is based on the prioritization framework described in Google Cloud's article "How to build an effective AI strategy", which recommends evaluating AI use cases based on business value, actionability, and feasibility.

Overview

The AI Strategy Matrix Builder helps organizations make data-driven decisions about which AI initiatives to pursue. By evaluating potential AI use cases across three key dimensions, this tool creates a visual matrix that clearly identifies which projects should be prioritized.

According to Google Cloud's research, organizations with a comprehensive AI strategy see ROI from generative AI much faster. A key part of this strategy is prioritizing the right use cases using a matrix that evaluates initiatives based on:

• Business Value: Impact on customer/employee needs, alignment with business objectives
• Actionability: Ease of adoption, speed of implementation, usability
• Feasibility: Technical fit, data readiness, risk tolerance

This application implements this prioritization framework as an interactive tool.

Screenshots

AI Strategy Matrix Visualization

Data Management Interface

Key Features

• Interactive Input Form: Easily add new AI use cases with ratings for actionability, feasibility, and business value
• Editable Data Table: View and modify your use cases directly in the application
• Quadrant Visualization: See your AI initiatives plotted on a strategic matrix with clear quadrant labels:
  • PRIORITY (High Feasibility, High Actionability): Focus here first
  • RESEARCH (Low Feasibility, High Actionability): Investigate further
  • BACKLOG (Low Feasibility, Low Actionability): Consider for future
  • ENABLEMENT (High Feasibility, Low Actionability): Build capabilities
• CSV Import/Export: Save and load your data for future sessions or sharing

Rating Dimensions

• Actionability: How ready is your business/team to adopt this AI solution?
• Feasibility: How technically feasible is this solution today?
• Business Value: How much potential business impact does this solution offer?

Each dimension is rated as High, Medium, or Low.

Getting Started

Prerequisites

• Python 3.12+
• uv (fast Python package installer)

Development Setup

1. Clone the repository

git clone https://github.com/yrangana/ai-strategy-matrix.git
cd ai-strategy-matrix

2. Install dependencies

make setup-dev  # Install all dependencies including development tools

Or for production only:

make setup  # Install only production dependencies

Development Workflow

You can use the Makefile commands directly, as they handle dependencies automatically:

• Run the application: make run
• Format code: make format (uses Black)
• Run tests: make test
• Run tests with coverage: make coverage
• Lint code: make lint

Note: All development commands (lint, format, test) will automatically install required dev dependencies if needed.

Alternatively, you can use uv directly to run commands in the virtual environment without activation:

uv run -- streamlit run main.py  # Run the application
uv run -- pytest tests/          # Run tests
uv run -- black *.py tests/*.py  # Format code
uv run -- pylint *.py tests/*.py # Lint code

Manage dependencies:

• Synchronize dependencies: uv sync (updates dependencies to match pyproject.toml)
• Add new dependencies: Update pyproject.toml and run uv sync

Usage

1. Add Use Cases: Fill out the form with a name and ratings for each dimension
2. View the Matrix: See your use cases plotted on the quadrant visualization
3. Edit Existing Data: Modify your entries directly in the data table
4. Export Your Work: Download your data as a CSV file for future use
5. Import Previous Work: Upload a previously saved CSV file

Project Structure

.
├── main.py             # Main Streamlit application
├── tests/              # Test directory
│   ├── __init__.py     # Makes tests directory a package
│   ├── test_main.py    # Tests for main functionality
│   └── test_utils.py   # Tests for utility functions
├── pyproject.toml      # Project configuration and dependencies
├── requirements.txt    # Pinned dependencies
├── Makefile            # Development workflow commands
└── README.md           # This file

Testing

This project uses pytest for testing. Run the tests with:

make test

Or with coverage information:

make coverage

Example Use Cases

• Implementing chatbots for customer service
• Developing predictive maintenance for manufacturing equipment
• Creating an AI-powered recommendation engine for products
• Building a computer vision system for quality control