Every business we talk to wants "AI" somewhere in their roadmap. Very few have asked whether their data, systems, and processes can actually support it.
AI readiness starts with data: is it centralized, structured, and accessible, or scattered across spreadsheets, legacy databases, and someone's inbox? Models are only as useful as the data you can feed them.
Next comes workflow fit. The highest-value AI use cases are usually narrow and well-defined: summarizing support tickets, drafting first-pass responses, classifying inbound leads. Broad, vague goals like "add AI to the platform" rarely produce anything shippable.
Finally, integration: AI features need to plug into the tools your team already uses, or adoption stalls regardless of how good the model is.
Our AI Readiness Assessment walks through all three areas and comes back with a prioritized, realistic starting point, rather than a generic AI strategy deck.