How to audit a business for AI readiness (2026 guide + free checklist)
The 7 visible signals that show real AI adoption vs claimed AI adoption - customer-facing AI surface, AI tech-stack signals (LangChain / MCP / vector DBs / LLM observability), automation depth, data maturity (CDP / warehouse), emerging signals (llms.txt / agent-shoppable schema / AI governance pages), the claim-vs-reality gap, and competitor AI adoption.
Frequently asked questions
How is "AI readiness" different from "digital transformation readiness"?
Digital transformation is a 2015 concept - moving paper to digital. AI readiness is a 2024+ concept - moving rule-based digital to autonomous agentic.
We don't need AI. We're a service business. Does this apply?
It applies more than you might think. AI-mediated buyer research means your prospects ask ChatGPT about you before they ever visit your site.
Is "agentic AI" actually a thing or just a buzzword?
Both. Real agentic AI (LangGraph, CrewAI, MCP-callable tools, multi-step planning) is shipping in production at maybe 10% of SaaS companies.
What's the cheapest AI readiness win for an SMB?
Three: ship llms.txt (free, 1 hour). Replace your rule-based chatbot with an LLM-powered one. Add LLM observability if you are already using an LLM.
How often should I re-audit AI readiness?
Quarterly during the 2024-2026 acceleration phase. The space moves fast - what was state-of-the-art six months ago is now baseline.