Jan 10, 2025 • AI, Automation

How to Launch AI-Powered Ops in 30 Days

A practical sprint plan we use with clients to land their first AI workflow without pausing delivery or risking production stability.

Why it works

  • Small, well-defined workflow instead of “AI everywhere.”
  • Shipping a thin slice early keeps stakeholders aligned and unblocks feedback.
  • Guardrails and observability are first-class, not an afterthought.

Week 1: Define the workflow

  • Pick a single manual process (support triage, SDR follow-ups, status reports).
  • Document inputs, outputs, and decision rules – keep it under one page.
  • Select a data source and owner; decide what “done” means for the workflow.

Week 2: Build a thin slice

  • Ship a minimal API/worker that runs the workflow once per day.
  • Instrument logs and latency; add alerts for failures only.
  • Keep prompts/config in version control; no manual edits in prod.

Week 3: Hardening

  • Guardrails: input validation, rate limits, and fallback paths.
  • Red/blue testing against a human baseline; measure accuracy and time saved.
  • Access control: separate service account; rotate keys; review audit logs.

Week 4: Rollout

  • Create a simple UI or Slack command to trigger the workflow.
  • Publish runbooks and on-call expectations.
  • Add post-deploy review: what to automate next?

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We design, build, and ship AI workflows with production guardrails for teams that need results fast.

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