Most Claude initiatives don't fail at the demo — demos almost always impress. They stall in the gap between "it worked on twenty examples" and "the support team relies on it every day." That gap is bridged by ordinary engineering and change-management work, and it compresses well into roughly ninety days if you run it in phases with explicit go/no-go gates. The plan below assumes you have already picked a use case and a platform; if not, start with the one-week proof of concept plan and the platform decision framework.
Days 1–30: Prove it on real data
The first month turns a demo into a measured prototype. Concretely: get platform access set up properly — real cloud accounts, least-privilege credentials, no shared API keys in notebooks — and build the thinnest version of the workflow end to end. Then spend most of the month on the unglamorous core: assemble an evaluation set of representative real inputs (including the ugly ones), define what a good output is with the business owner, and iterate the prompt against that set until the numbers stabilize. Involve security early; a thirty-minute conversation about data flows in week two prevents a blocking review in week ten.
Gate 1 (end of month one): quality on the eval set meets the threshold the business owner set in advance; cost per request is estimated from measured token usage; security has seen the data-flow design and raised no blockers. If quality misses the bar, iterate or stop — do not proceed on the promise that production data will somehow be easier.
Days 31–60: Make it operable
Month two is about everything around the model. Build the production plumbing: retries and timeouts for transient errors, streaming if users wait on responses, logging of requests, token usage, latency, and outcomes. Pin your model version so behavior is stable, and wire your eval set into the deployment path so no prompt or model change ships unmeasured. Design the human-in-the-loop shape deliberately — which outputs release automatically, which queue for review, and who works that queue (see human-in-the-loop design). Set up cost tagging and a budget alert so the bill is visible from the first real traffic. Then run a limited pilot with a friendly internal group and treat their corrections as gold: every recurring fix becomes an eval case or a prompt improvement.
Gate 2 (end of month two): the pilot group is getting real value (measured by usage and edit rates, not testimonials); operational basics — monitoring, alerts, an incident runbook, a rollback plan — exist; quota headroom for launch volume has been confirmed with the platform.
Days 61–90: Widen deliberately
Month three is a staged rollout, not a launch event. Expand the user base in steps — one team, one region, one traffic percentage at a time — watching the same dashboard at each step: quality signals, latency, error rates, escalation volume, cost per unit of work. Train the humans in the workflow on what the system is good at and, more importantly, what it is not; honest expectations do more for adoption than enthusiasm does (the change management article covers this). Keep a weekly review of escalations and corrections, and feed them back into prompts and evals. By day ninety the system should have a named owner, a support path, and a documented steady-state: this is what "production" actually means.
Staffing: smaller than you fear
A rollout of this shape typically needs a product owner who owns the use case and the quality bar, one or two application engineers, a part-time slice of security and platform/DevOps time, and genuine hours from subject-matter experts to build eval sets and review pilot output. Note what is absent: ML researchers. The work is prompting, evaluation, and integration — engineering and product work, not model science. The SME time is the item most often under-budgeted; protect it.
Where to go next
Two companions round out this plan: evaluating LLM features for the measurement backbone the gates depend on, and the ten most common Claude 3P mistakes for the failure patterns this plan is designed to avoid. The launch checklist condenses the pre-production items into one page.