AI Readiness Assessment
Answer 8 questions about your business to find out how ready you are for structured Claude AI deployment. Takes about 2 minutes.
Why readiness matters before you deploy
Most AI deployments don’t fail because the technology was wrong. They fail because the business wasn’t ready for it. A company with fragmented documentation, inconsistent processes, and siloed data can buy any AI tool on the market and still end up with nothing deployed twelve months later. The AI isn’t the bottleneck — the foundations are.
This assessment evaluates eight dimensions that, together, predict whether structured AI deployment will stick: company size and workflow volume, current AI usage, documentation maturity, process standardization, leadership buy-in, data accessibility, and team capacity. Each one is a lever. A strong score on any single dimension can compensate for a weaker score on another. A low score across several dimensions usually means the next six months are better spent on standardization than on AI tooling.
Take the two minutes. There are no “right” answers — just honest ones. You’ll get a specific, actionable breakdown instead of a generic “you’re ready” verdict.
How many employees does your company have?
AI deployment ROI scales with team size and workflow volume.
What your score actually means
Ready to Deploy (25-32):your organization has the structure, workflow volume, and buy-in to start deploying Claude AI projects immediately. The right move is to pick your two or three highest-value workflows and deploy real projects within four to six weeks. Don’t spend another quarter in strategy mode — you’ve already earned the right to ship.
High Potential (17-24): you have strong foundations in most dimensions, but one or two specific gaps need closing before a full rollout. The common pattern is strong leadership buy-in but weak documentation, or strong documentation but inconsistent processes. Fix the weakest dimension first, then deploy. Trying to go full-speed before that patch usually burns trust on the first failed project and sets you back six months.
Building Foundations (8-16):don’t deploy yet. Your team would spend more time fighting basic inconsistency than realizing AI value. Standardize your highest-frequency workflows, centralize scattered reference documents, and run small pilots with individual contributors before you touch anything at team scale. Come back to this assessment in ninety days.