Translate business problems into Claude-based AI solutions
Turn a vague business request into a scoped Claude solution with a named bottleneck, a measured baseline, a target outcome metric and explicit boundaries for what the model does versus what humans decide.
Key points
- 1
Start with the problem, not the technology. Before any prototype, model choice or vector store, agree which step in the process hurts, what the current numbers are (minutes per case, return rate, cost per contact) and what target counts as success.
- 2
Sponsors describe symptoms in their own words ("faster claims", "catch more fraud", "reduce rework"). Discovery often shows the real bottleneck is somewhere else, for example analyst time per alert rather than detection recall. Translate to the evidenced cause.
- 3
Success criteria should be specific, measurable, achievable and relevant to the business goal: an outcome metric with a baseline (time-to-triage from 40 to 15 minutes; first-time-complete rate), not an activity metric (summaries produced, chats per month, tokens used).
- 4
Where you can, compare against a control cohort or a pre-rollout baseline so the effect can be attributed to the solution.
- 5
State scope boundaries in the definition: what Claude drafts, extracts or checks; who reviews and signs; which systems the output may write to and through what gate. Regulated decisions (eligibility, coverage, fraud disposition, clinical sign-off) stay with the accountable human.
- 6
Prefer the narrowest solution that attacks the root cause over a broad general assistant. A tight scope is faster to build, easier to evaluate and gives a clean business case; breadth can come in later phases.
- 7
Do not put an LLM call on a path whose latency budget it cannot meet (for example sub-50 ms real-time scoring). Keep existing deterministic or ML components that already work and put Claude where language, evidence gathering or drafting is the bottleneck.
- 8
Absolute requirements such as "100% accuracy before pilot" are not measurable and are unnecessary when the design includes a human review step; set a target accuracy plus a review mechanism instead.
- 9
Common traps in exam distractors: prototyping before defining the problem; choosing the biggest model first; automating a decision that regulation reserves for humans; measuring activity instead of outcomes; extending discovery indefinitely when the cause is already evidenced; giving a first solution write access to customer environments.
- 10
Model choice, architecture pattern and infrastructure are downstream decisions validated against the defined problem; they should not appear in the problem definition itself.
Test yourself on Translate business problems into Claude-based AI solutions
Ten questions, with the answer and explanation after each one.