Implement one to three usable workflows tied to a defined result such as faster lead response, stronger follow-through, greater delivery capacity, lower cost-to-serve, or more reliable execution. You finish with a working system, not another tool recommendation.
A 30-Day AI Workflow Sprint takes one well-scoped workflow from idea to daily use — with a defined business measure, mapped handoffs, approval rules, the smallest useful tool stack, and a team prepared to run it.
Confirm the workflow, map the task, handoffs, and approval rules.
Set up the right assistant, automation, or agent pattern.
Run it on real work, tune outputs, and fix the rough edges.
Document usage, train the team, and set the measurement loop.
Most teams keep improving after the first win. When you are ready to add use cases and tune adoption over time, Managed AI Operations picks up where the sprint ends.
See Managed AI OperationsBusiness-outcome definition, scope and design, implementation, handoffs, human approval rules, testing on real work, documentation, team training, and measurement.
One to three well-scoped workflows. Complexity, integrations, data readiness, and review requirements determine the final scope.
The smallest useful stack for the job: Microsoft Copilot, ChatGPT, Claude, Power Automate, Copilot Studio, Azure AI, or a custom agent only when needed.
Your team runs the documented workflow. Managed AI Operations is available for ongoing tuning and expansion.
See the practical decision framework for choosing an AI tool stack before adding more subscriptions.
Most sprints start from an AI Time Back Audit so the first candidate is already chosen and de-risked.
Scope a sprint