Data and Infrastructure Foundations for AI
Explore how to assess data accuracy, completeness, and accessibility to support AI initiatives. Understand the importance of data ownership, sharing frameworks, and infrastructure readiness for scaling AI solutions. This lesson helps you evaluate organizational capabilities to manage data silos, govern data use, and provide secure, scalable technical foundations supporting AI workloads.
The company’s complaint pilot cannot move beyond experimentation while complaint records remain split across customer service, billing, and product-quality systems. The immediate decision is not which model to buy. It is whether the available data and operating foundation can support a broader complaint workflow without producing incomplete, inaccessible, or unreliable results.
Data readiness for complaint AI
Data readiness is the extent to which data is accurate, complete, current, accessible under policy, and suitable for the intended AI decision. A complaint-routing or summarization capability needs enough reliable history to recognize the issue, enough current information to reflect the customer’s situation, and enough linked context to avoid treating related records as unrelated events.
For the company, accuracy means a complaint category reflects the actual issue rather than an outdated or inconsistent label. Completeness means records include the fields needed for the intended outcome, such as product, channel, resolution status, and relevant customer context. Timeliness means recent policy changes, product recalls, and case updates appear soon enough to guide a live workflow. Earlier data-quality work established these dimensions; here, we judge whether they are sufficient for expansion rather than merely useful for a pilot.
Accessibility is not unrestricted access. The right business and technical teams must be able to use approved data for an approved purpose, subject to the access, classification, and minimization boundaries already established. If the complaint team cannot reach billing-resolution status, for example, it cannot reliably identify whether repeated ...