What is shadow AI?
Shadow AI is the unmanaged use of generative AI tools by employees. Definition, real-world risks, and how to respond without killing productivity.
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Notes, field reports and analysis on enterprise AI.
RSS feedShadow AI is the unmanaged use of generative AI tools by employees. Definition, real-world risks, and how to respond without killing productivity.
Read article →95% of enterprise AI pilots fail not because models are weak, but because they're loaded with knowledge and asked to deliver know-how. Why these are different categories.
Read article →Why enterprise AI gets heavier as it gets more capable — and how a capability graph lets you assemble context as a runtime payload instead of a static config file.
Read article →A new layer is taking shape in the agentic enterprise stack: the context lake. Business context, tool permissions, and governance need their own home.
Read article →MCP solves the N×M connectivity problem. Skills solve context saturation. The real opportunity isn't picking one — it's composing Skills over MCP, organized by business context.
Read article →Single skill files hit a complexity ceiling fast. Skill graphs turn domain expertise into a navigable network so agents can traverse business logic instead of guessing.
Read article →Zapier vs. AI agent — it depends on how messy your reality is. A framework for choosing between deterministic workflows and adaptive agent skills, plus the hybrid that wins.
Read article →RAG retrieves content. Skills package competencies. Why conflating them limits how companies think about AI agents, and a framework for choosing between them.
Read article →As agents gain access to more tools, the bottleneck shifts from reasoning to retrieval. Why picking the right tool is the next frontier in AI agent design.
Read article →Agent Skills are modular instruction sets that extend AI capabilities for specific tasks. Anatomy of a SKILL.md, activation flow, and how to build your first one.
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