AI Glossary
What it means, why it matters to your business, and where it shows up in a real build decision.
In plain terms
AI readiness is how prepared an organization is to adopt AI, measured across its data, systems, skills, and processes. The blocker to AI value is usually not the model but the data and workflows around it. An honest readiness check prevents spending on AI the business cannot yet absorb.
Asking if a business is AI-ready is like asking if a house is renovation-ready. The design can be brilliant, but if the plumbing and wiring need replacing first, the renovation timeline is the infrastructure, not the design.
A professional services firm wants to automate contract review but cannot. Their contracts are stored in a shared drive in inconsistent file formats with no tagging. The AI readiness gap is data infrastructure, not model availability.
Run an honest readiness check before committing budget to any AI initiative. The output tells you whether to start building or whether to invest in data, systems, or process maturity first.
Do not skip the readiness check because the business case for AI is compelling. A compelling use case with unready infrastructure produces a failed project, not a deployed product.
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