AI Jobs to be Done
Diagrams | Matrices | X List
Actionable use cases for practical day-to-day productivity gains with AI.
| Roles / JTBD | Tool | Prompt |
|---|---|---|
| Accelerating learning and skill acquisition | ||
| Automating Coding | ||
| Enhancing productivity | ||
| Finding Answers | Perplexity | |
| Rapid prototyping and development | ||
| Research assistance | ||
| Task automation | ||
| Code optimization and refactoring | ||
| Documentation and explanation |
Context
AI Productivity
Using interfaces that abstract interaction with LLMs.
- Big (Centralized) AI
- AI Agents / Smart Agents
- AI Agent Frameworks
- AI Agent Analysts
- AI Agent Traders
- AI Coding
Everyday Tasks
AI is particularly useful for summarizing and extracting key information from large amounts of text, rather than generating content from scratch. practical day-to-day productivity gains with AI include:
Accelerating learning and skill acquisition
- Teaching how to use new frameworks and technologies
- Replacing web searches for setting up/configuring new packages and projects
- Assisting with debugging error messages
Enhancing productivity
- Automating boring/repetitive tasks to allow focus on higher-level problems
- Replacing time-consuming web searches with direct AI assistance
Research assistance
- Generating ideas for experiments
- Analyzing and visualizing results (e.g. creating histograms)
Task automation
- Automating data processing and analysis workflows
- Creating scripts to automate repetitive tasks