Implement one approved operating improvement
An Automation Sprint turns an agreed workflow scope into a working system. It begins only after the trigger, systems, responsibilities, exceptions, safeguards, and acceptance criteria are understood well enough to build responsibly.
What belongs in scope
A sprint should have a defined operational boundary. Examples include routing a qualified inquiry, assembling an intake record, coordinating a scheduling handoff, generating an internal review queue, or preparing a recurring operations report. The exact workflow depends on supported integrations, data quality, risk, and the people responsible for it.
Build requirements
- Written trigger, inputs, outputs, owners, and system-of-record rules.
- Expected cases, invalid inputs, duplicates, delays, vendor failures, and manual exceptions.
- Least-privilege access and an approved method for credential sharing.
- Human-review checkpoints for customer-facing, financial, legal, destructive, or otherwise sensitive actions.
- Logging, alerts, retries, recovery, and a clear way to pause or bypass the automation.
- Acceptance tests connected to the baseline established before the build.
Validation before launch
Testing covers more than the happy path. The sprint should exercise missing fields, duplicate events, unexpected values, unavailable vendors, revoked access, and the handoff to a person when the system should stop. Production launch happens only after the agreed acceptance checks pass.
Documentation and ownership
The handoff explains what runs, where it runs, which accounts and vendors it depends on, how failures surface, who responds, what data is retained, and how to disable or change the workflow. Client-owned accounts are preferred where practical so the business is not held hostage by an opaque implementation.
Measurement after launch
The first question is whether the workflow behaves reliably. The second is whether it improves the agreed operating measure without creating a new burden somewhere else. Expansion should wait until the initial implementation is stable and useful.
Start with an AI Workflow Audit when the boundary is still unclear, read the full delivery process, or request a discovery conversation.