Dropleaf
A useful AI taxonomy. What do you actually mean when you say AI – what are the kinds of problems that you might be trying to solve?
Purpose
“AI” has become semantically meaningless. The term now encompasses everything from a regression model to an autonomous robot, creating confusion in strategic discussions, partner conversations, and product positioning. This taxonomy provides a functional framework based on what the AI actually does, not what technique it uses.
The Framework in One Sentence
We use Analytical AI to decide, Semantic AI to understand and remember, Generative AI to create, Agentic AI to act, Perceptive AI to sense, and Physical AI to move.
The Six Functional Categories
| Category | What It Does | Typical Tech | Relevance |
|---|---|---|---|
| Analytical AI | Predicts, classifies, scores, optimizes | ML models, gradient boosting, neural nets on structured data | Propensity models, LTV prediction, fraud detection, churn scoring |
| Semantic AI | Understands meaning, finds relationships, grounds context | Embeddings, vector DBs, knowledge graphs, GraphRAG | Customer intent understanding, intelligent matching, truth anchoring |
| Generative AI | Creates new content: text, images, code, media | LLMs, diffusion models, fine-tuned domain models | Personalized messaging, creative variation, content generation |
| Agentic AI | Plans, reasons, uses tools, executes multi-step workflows | LLM + orchestration (MCP, LangGraph), tool interfaces | Campaign optimization, autonomous workflows, digital coworkers |
| Perceptive AI | Interprets sensory input: vision, speech, documents | Multimodal LLMs, computer vision, ASR | Document processing, visual inspection, voice interfaces |
| Physical AI | Applies intelligence to physical actuators and space | World models, sim-to-real transfer, robotics platforms | Drones, robotics division, autonomous infrastructure |
