AI use cases scale through architecture. The challenge is creating the architecture that allows several use cases to run, connect, be monitored, and be governed as adoption grows. Three pressure points usually appear. - Integration becomes critical when AI needs to connect with existing systems, applications, data flows, and business processes. - Control and monitoring are needed once use cases affect decisions, users, or operations. - Reuse becomes important because shared patterns help teams deliver faster and support more use cases with less friction. The working question is simple: what architecture needs to be in place before AI use cases multiply across teams, systems, and processes? If you are designing for AI beyond isolated use cases, let’s compare approaches with others facing similar architecture questions.
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