AI Delivery Sprint
One useful AI workflow, built through to a production handover.
Choose a recurring task with a clear owner and a measurable outcome. We turn it into a bounded implementation: one workflow, agreed integrations and a working production handover. The start date, price and delivery window follow the readiness assessment.
DIRECT ANSWER
An AI Delivery Sprint is a defined implementation engagement for one ready-to-build AI workflow. It includes technical scoping, development, evaluation, integration and handover against written acceptance criteria.
Best for
- Teams with one identified workflow and an owner ready to make decisions
- Companies needing a bounded first AI implementation before expanding
Consider another approach
- An open-ended programme covering many departments
- A feasibility question that needs research before a delivery commitment
Select a workflow with an available owner
A sprint needs a task that can be observed and accepted: triage incoming requests, prepare a draft for approval or extract records from a defined document family. We check access, sample inputs, integration constraints and who answers questions during delivery. Company-wide adoption and identifying many use cases belong with AI Enablement.
Write down the first release before quoting it
The scope names supported inputs, actions, users and exceptions. It also states what still requires a person. We agree the success measure and an acceptance set before finalizing price and timing. Uncertain integrations or poor data can require a separate discovery step; the sprint begins when those dependencies are sufficiently understood.
Review working behavior throughout the build
Implementation proceeds in reviewable increments. Your owner sees the workflow act on approved examples, including failures and handoffs, while there is still time to correct the direction. Authentication, data access, evaluation and operational logs belong in the release scope. A change to the agreed outcome is assessed openly for its effect on work and timing.
Illustrative acceptance: draft a response, require approval
An example first release classifies an incoming request, retrieves an approved policy and prepares a draft. A staff member approves sending; ambiguous requests stay in review. Acceptance checks the supported request types, grounding, correct queue assignment and the fact that no message is sent without approval. This defines the delivery boundary without promising a particular productivity gain.
Finish with an owner who can operate it
The handover includes code, configuration, evaluation cases, operating instructions and an agreed walkthrough. We demonstrate retries, a safe stop and how to handle exceptions. Ongoing changes or monitoring are separate decisions after acceptance. Broader product development remains with AI Agents & Products, while an engineer embedded for continuing delivery has a separate engagement model.
Questions before you start
No. We agree the delivery window after checking scope, access and dependencies. A short label is not a substitute for a credible implementation plan.
A fixed price can be agreed for the accepted scope. Discovery, third-party usage charges and later changes are identified separately in the proposal rather than assumed to be included.
The intended deliverable is the agreed production workflow and handover. If feasibility or access only supports a prototype, that is contracted explicitly instead of being presented as production delivery.
We recommend a simpler rule, integration or workflow change where it meets the outcome. The readiness assessment can also conclude that the proposed sprint should not proceed.
Scope one AI workflow
Tell us the recurring task, who performs it and what a useful first release would change. Include the systems involved and the person who can approve the workflow.