AI readiness
AI readiness concerns the foundations of an AI project: objectives, usable data, skills, responsibilities and an operating workflow.
Checking the foundations for an AI project
AI readiness describes how well an organisation is prepared for an AI project. It includes a concrete business objective, suitable data approved for use, technical integrations, and the skills and responsibilities needed for operation. Evaluating results and handling errors also need a place in the workflow. An assessment must state its project scope and criteria. Readiness for a limited trial does not mean blanket approval for any automation.
A fictional service business wants internal routing suggestions for incoming messages. In this scenario, suitable, approved example messages and expected assignments checked by domain specialists are available. However, the team has not yet allocated time to review the suggestions. Before a pilot, it would therefore need to establish who checks results, makes necessary corrections and stops the trial if problems arise. The limited pilot requires human review before any suggestion is applied. Its preparation would be assessed against these conditions, rather than treating a successful demonstration as evidence of general operational readiness.
Sources
Related terms
- LLM evaluationLLM evaluation tests model outputs against defined tasks and criteria. One good example does not establish overall quality.
- Human-in-the-loopHuman-in-the-loop actively involves people in an automated process. Useful review requires understandable information and ways to intervene.
- Business process automationBusiness process automation connects recurring steps with rules, responsibilities and defined ways to handle exceptions.
- Model cardA model card documents an AI model’s purpose, foundations, evaluation conditions and known limitations for a particular version.
- Context engineeringContext engineering prepares information for individual model calls: relevant instructions, messages, sources and current states.
- Anomaly detectionMethods that flag unusual observations relative to a defined reference or a learned pattern.
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