Knowledge Area
Enterprise AI Strategy — What It Takes to Move Past the Chatbot
The real limits companies run into when adopting AI, and the decision criteria worth checking when choosing a platform. Nothing here is about stock price or investment — it’s about organizational data strategy.
Why this area matters
Personal-productivity AI adoption mostly succeeds, but “AI that can answer questions about our company data” often stalls at the pilot stage. This covers why that gap exists, and how to judge your way past it.
Reading order
- Why Enterprise AI Stalls at the Chatbot Stage
- We Connected ChatGPT to Company Data — Why It Disappointed Us
- Why AI Can’t Understand Your Company Data
- What RAG Can and Cannot Do — When You Actually Need an Ontology
- Palantir Is Expensive. Here’s Why We Use It Anyway
Get hands-on — AI Agent Series (6 parts)
You don’t need to know Palantir at all. Build an AI agent by hand and get a feel for these concepts through a 6-part hands-on series.
- Setting Up an AI Agent Practice Environment (Part 0: Before You Start)
- What Is an AI Agent? Context, Subagents, Skills, and MCP Explained
- Prompt Engineering vs. Context Engineering: What Is the Difference?
- What Is Harness Engineering? Keeping AI Agents Reliable
- Agentic Engineering and the PH-AH Loop: Not Vibe Coding
- AI Agent Concepts, Hands-On: Build a Budget Summary App
Beyond the series
Not part of the 6-part series, but useful follow-up reading.
- What Is a Multi-Agent System? Getting Agents to Collaborate
- Building an MCP Server: Connect Claude to Your Internal Systems
Once you’ve confirmed the problem, see how it actually gets solved (Ontology, Foundry, AIP):
Go to the Ontology guide