AI Operating Model
Once you have proof points, we help leadership turn scattered AI experiments into a repeatable operating model: ownership, governance, prioritization, rollout cadence, and the muscle to keep choosing the right workflows over time.
Structure ownership
The operating model clarifies who owns prioritization, who approves risk, who maintains the backlog, and how functions request new work.
- Decision rights and cross-functional ownership model
- Sponsor roles for operators, IT, security, and leadership
- Intake criteria for new AI requests
- Escalation paths for policy, quality, and adoption issues
Sequence the roadmap
Most teams do not need more ideas. They need a way to rank requests against actual business impact, data readiness, and organizational appetite.
- Prioritization rubric tied to measurable business outcomes
- Quarter-by-quarter sequencing of workflow bets
- Templates for deciding what stays manual, assisted, or automated
- Dependency mapping across teams and systems
Build adoption into the plan
A workflow only counts if people trust it and keep using it. We design the operating model around the managers and operators who have to live with the result.
- Manager enablement and workflow-owner expectations
- Communication plan for new automation rollouts
- Usage, quality, and exception-review rhythms
- Feedback loops that keep the backlog honest
Keep the backlog moving
The operating model is how leadership avoids random AI projects. It gives the company a way to keep choosing, measuring, and expanding the right work.
- Quarterly review and backlog refresh process
- Templates for evaluating new workflow requests
- A scorecard for value, readiness, and adoption
- A repeatable handoff from MRI to sprint to scaled rollout
What you get.
Every engagement produces tangible artifacts your team can operate on immediately.
- ✓AI ownership model and decision cadence
- ✓Workflow prioritization framework and intake process
- ✓Governance rules for data access, quality, approvals, and exceptions
- ✓Cross-functional rollout plan tied to real operating metrics
- ✓Enablement plan for operators, managers, and workflow owners
- ✓Quarterly roadmap reviews and backlog refresh
Frequently asked.
Direct answers to the questions we hear most about AI Operating Model.
Usually after the first one or two wins. That is the point where demand starts spreading faster than ownership and the company needs a common way to evaluate what gets built next.
No. The operating model is built from actual workflow evidence, actual deployment lessons, and the practical constraints of the teams doing the work. It is meant to reduce drift, not create a prettier slide deck.
It depends on the company. Sometimes it sits with the COO, BizOps, or Chief of Staff. Sometimes it is shared with IT or product leadership. Part of the engagement is clarifying that ownership model instead of assuming one title should run everything.
Most teams start with the AI MRI, prove one workflow in an implementation sprint, then formalize the operating model once demand and risk both increase. The offers are designed to stack in that order.
Book an AI MRI intro. We'll scope the engagement, align on outcomes, and propose a timeline.