Translating Business Requirements into Claude Solutions.

This is the first move a Claude Certified Architect - Professional has to make correctly: taking a stated business problem (reduce support-ticket handle time, automate contract review, speed up research synthesis) and translating it into a concrete, buildable requirements spec - not just "add AI," and not yet a pattern choice. Anthropic's enterprise guidance frames the whole exercise as decision-first, outcome-anchored work: before any architecture gets sketched, an architect needs to know what the system must do, what it must connect to, what constraints bound it, and how success will be measured.

Getting this translation wrong most often means skipping it entirely - jumping straight from a one-line business ask ("automate contract review") to a pattern or a prototype with no written functional requirements, infrastructure requirements, constraints, or success metric to build against or evaluate later. This step is deliberately upstream of, and distinct from, choosing workflow vs. agent vs. augmented LLM - that pattern-selection decision is a separate concept (see "Workflow vs Agent: Choosing the Right Architecture") that consumes this spec as its input, it does not replace it.

Functional requirements: what the system must do. Specify the input shape (what arrives, in what format), the required transformation or task (extract which fields, answer which question, generate what artifact), and the output contract (structured JSON, a summary, a routed decision) - concretely enough that two different engineers would build compatible systems from the same brief. This is also where an architect identifies what augmentations the task needs beyond the base model - retrieval over a specific corpus, a specific external tool, persistent memory across turns - since those are functional needs, not architecture choices.

Infrastructure requirements: what it must run on or integrate with. Name the systems the solution touches (a claims database, a CRM, an internal MCP server), what auth or data-access scope each integration needs, and where the solution has to live (embedded in an existing app, a standalone service, a chat interface). This is the CCD-F-facing half of the same translation: turning a business ask into requirements a solution architecture can actually be built against, not left implicit until implementation surfaces the gap.

Constraints: what bounds the design before any pattern is chosen. Cost ceiling per unit of work, a latency or accuracy SLA, compliance or data-residency rules, and how much judgment can run unattended versus needing a human in the loop are all constraints the requirements step should surface explicitly - each one narrows, sometimes decisively, which architecture pattern the next step can pick from.

A measurable success metric, tied to a narrow pilot. Anthropic's enterprise transformation materials frame the translation step as tied to success metrics defined up front - enterprises that succeed define a metric and a narrow pilot scope before expanding, rather than translating a vague mandate ("use AI more") directly into a system design.

Treat the requirements spec as a hypothesis, not a one-shot document. Anthropic's planning-to-production guidance stresses that implementation is iterative: the first translation of a business problem into functional requirements, infrastructure requirements, constraints, and a metric is expected to be revised against real usage data, not treated as final the moment it's written down.