Screening Business Processes as Claude Use Cases

1.What makes a task a good fit

Requirements analysis with Claude starts with a narrower question than "can AI help this department?" It asks which specific pieces of work have the traits that Claude handles well. Anthropic's production use-case guides (customer support, ticket routing, content moderation, legal summarisation) each open with a list of such indicators, and the lists overlap to a useful degree.

For customer support, the guide lists several indicators. Claude handles a large number of similar questions efficiently. It can pull together information from large knowledge bases faster than a person consulting several sources. It absorbs sudden jumps in volume without new hiring, and it can be instructed to keep a consistent tone. Note how the first indicator is framed: the point is to free human agents "for more complex issues", not to remove them.

For classification work such as ticket routing, the indicators are about data and change. Claude can classify from a few dozen labelled examples rather than a massive training set. It adapts when category definitions change. It can explain its decisions in readable language, which the guide says builds trust in the automation. Content moderation adds nuance: telling that "killed it" in a film review is a metaphor, while a threat with no violent words should still be flagged.

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2.Map how people do it today, before automating

Knowing the fit signals is not enough to write requirements. The ticket-routing guide is direct: "Before you automate, it's crucial to understand your existing ticketing system." It lists the questions to ask the team that does the work now. What criteria decide which service level applies? Does routing decide the support tier or the specialist? Which automated rules already exist, and when do they fail? How are edge cases and ambiguous tickets handled? How does the team prioritise?

These questions do two jobs. First, they show the real inputs to the decision. The guide notes that routing may depend on urgency, customer type, SLAs or language as well as intent, and a requirements list that captures only intent would miss those. Second, they bring out the hard cases, which are exactly where a new system is most likely to go wrong. The guide sums up the principle: the more you know about how humans handle certain cases, the better you can work with Claude on the task.

Claude can help with this analysis itself. It can draft interview questions, organise the team's answers, or turn a pile of notes into a list of decision criteria. But the facts about the current process have to come from the people and systems that run it. A requirements list Claude writes without that input describes a generic process, not yours.

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3.Which tasks feel safe to hand over, and why

Anthropic Academy's AI capabilities course opens with a short audit that works well as a first screening pass. List real tasks, and be specific. "Drafted a client email explaining a project delay" tells you something; "writing" does not. For each task, note whether the output was usable first time or needed rework. Then ask Claude how each task could go wrong if nobody is paying attention, and push back when its answer does not match what you have seen.

The course then asks which tasks felt safe to hand to AI and which felt risky. That question matters for requirements analysis. A task where a mistake is cheap to spot and fix is a different kind of candidate from one where Claude's output would become a final decision about a person with nobody checking it. The sources here do not give a formal rule for drawing that line. They do make clear that the case for Claude in support work is about volume and consistency, with people kept for the complex issues.

Keep the solution you propose modest as well. Anthropic's engineering guidance on agents recommends finding the simplest solution possible and adding complexity only when it is needed. For many applications, one well-prompted call with retrieval and examples is enough. A use case that has passed screening does not automatically justify an autonomous system.

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Exam traps

Each one states something that sounds right. Open it to see what is actually true.

1. 1.The first step in scoping a Claude use case is to try Claude on the task and see what it produces.Why is that wrong?

   The guides start with the existing process: how the team handles it now, where current rules fail, and how edge cases are treated. That knowledge is what you work with Claude on.

   Covered in Map how people do it today, before automating

2. 2.A high-volume support process is a good Claude use case because Claude can replace the human team.Why is that wrong?

   The indicator is handling many similar questions efficiently so that people are free for the harder work. Humans stay on the complex issues.

   Covered in What makes a task a good fit

3. 3.Once a task is judged suitable for Claude, the solution should be a fully agentic system.Why is that wrong?

   Anthropic recommends the simplest solution that works. Often that is a single LLM call with retrieval and examples, and more complexity is added only when needed.

   Covered in Which tasks feel safe to hand over, and why