Who this is for
This article is for VP-level leaders in Operations, Customer Success, Product, Engineering, and Technology who are responsible for turning AI investments into measurable business outcomes. It assumes you have moved past the proof-of-concept stage and need a repeatable system for intake, evaluation, and rollout.
The problem in plain terms
Most organizations treat AI as a technology problem. They spin up pilots, experiment with tools, and celebrate demos. Then nothing scales. The issue is rarely the model. It is the absence of a business system around the capability.
AI does not create value on its own. Value comes from changing how work gets done, which requires coordination across product, engineering, operations, security, legal, and frontline teams. Without clear ownership, intake discipline, and rollout governance, AI initiatives stall in pilot purgatory or ship without adoption.
The symptoms are predictable:
- Multiple teams evaluating overlapping tools with no shared criteria
- Pilots that succeed technically but fail to change behavior
- Legal and security reviews that block launches at the last minute
- Frontline staff who were never trained and quietly abandon the tool
- No one who can answer whether the investment actually worked
This is an operating model problem, not a technology problem.
The framework
An AI operating model answers seven questions:
1. Ownership
Who is accountable for AI outcomes at the portfolio level. This is not the person who runs infrastructure. It is the person who owns intake, prioritization, and business impact. In most organizations, this sits in Product, Operations, or a dedicated AI or Automation function.
2. Intake
How use case requests enter the system. You need a single, visible channel, not scattered Slack threads and side conversations. Intake should capture the business problem, the proposed scope, the expected benefit, and the sponsoring team.
3. Prioritization
How you decide what to work on. Prioritization should weigh impact, feasibility, risk, and strategic alignment. Without explicit criteria, you will optimize for whoever talks loudest.
4. Evaluation
How you test whether a solution works before full rollout. Evaluation includes technical performance, workflow fit, compliance review, and user acceptance. Define pass or fail criteria before the pilot starts.
5. Rollout
How you move from pilot to production. Rollout includes infrastructure, monitoring, documentation, and change management. A working model is not the same as a deployed capability.
6. Change management
How you ensure adoption. This means training, incentives, workflow redesign, and ongoing support. If the operating model stops at deployment, adoption will fail.
7. Support
Who handles issues after launch. Support includes troubleshooting, escalation paths, model drift monitoring, and feedback loops for improvement.
Common failure modes
Shadow AI
Teams procure and deploy tools outside the intake process. You lose visibility, create compliance risk, and duplicate effort.
Governance theater
Review boards that meet monthly but have no authority, no shared criteria, and no fast-track path for low-risk use cases. Speed dies. Teams route around the process.
Pilot addiction
Organizations that run pilots endlessly because no one owns the decision to scale or kill. Resources scatter. Nothing compounds.
Deployment without adoption
Technical teams ship a working solution. No one trains the users. No one adjusts the workflow. Usage flatlines within 60 days.
Missing feedback loops
No mechanism to capture whether the deployed solution is actually delivering value. You cannot improve what you do not measure.
What good looks like
A mature AI operating model is lightweight, visible, and accountable.
Clear roles
- Product owns intake, prioritization, and roadmap
- Data and Engineering own build, integration, and infrastructure
- Operations own workflow design and adoption
- Security and Legal own risk review with defined SLAs
- Frontline teams own feedback and continuous improvement
Lightweight governance
- Tiered review: low-risk use cases get fast-tracked, high-risk use cases get deeper scrutiny
- Standing authority for common patterns so you do not re-litigate the same decisions
- Escalation paths that are clear and rarely used
Operating cadence
- Weekly intake review: 30 minutes, triage new requests, assign owners
- Biweekly prioritization and status: 45 minutes, update the backlog, unblock stalled work
- Monthly outcomes review: 60 minutes, review deployed solutions against success criteria, decide what to scale, sunset, or iterate
Artifacts
- Use case backlog: single source of truth for all requests, status, and owners
- Evaluation rubric: scoring criteria for technical fit, business impact, risk, and feasibility
- Pilot plan: scope, success criteria, timeline, and decision rights for each experiment
- Adoption plan: training, workflow changes, incentive alignment, and rollout schedule
- Support playbook: escalation paths, known issues, and feedback channels
Adoption: the part everyone skips
Deployment is not adoption. Adoption requires three things:
Training
Users need to understand what the tool does, when to use it, and when not to. Training should be role-specific and embedded in existing onboarding, not a one-time webinar.
Incentives
If the tool adds friction and no one adjusts targets or workflows, users will abandon it. Align incentives to the new way of working. Remove metrics that reward the old behavior.
Workflow fit
AI tools that require users to change context, open new applications, or remember extra steps will fail. Integrate into existing workflows. Reduce clicks, not add them.
A practical starter checklist
- Appoint a single owner for AI intake and prioritization
- Publish a visible intake channel and form
- Define evaluation criteria before starting any pilot
- Create a tiered governance model with fast-track for low-risk use cases
- Establish a weekly intake review and monthly outcomes review
- Build a use case backlog as the single source of truth
- Require an adoption plan before any production rollout
- Assign support ownership before launch
- Instrument deployed solutions for usage and outcome tracking
- Schedule quarterly retrospectives on the operating model itself
When to call for help
You do not need outside help to run intake meetings or maintain a backlog. You may need help when:
- Multiple functions are blocked on unclear ownership or decision rights
- Governance has become a bottleneck and you need a tiered model designed
- Pilots keep succeeding but nothing scales to production
- Adoption is failing and you cannot diagnose why
- You need to stand up an operating model quickly for a board or investor commitment
The right advisor will help you build the system, not run it for you.
Closing
AI capability is becoming table stakes. The model you use matters less every quarter. What matters is the business system you build around it: intake, prioritization, evaluation, rollout, adoption, and support.
Get the operating model right and AI becomes a compounding asset. Get it wrong and you will keep funding pilots that never ship and tools that no one uses.
Start simple. Own the backlog. Run the cadence. Measure outcomes. Iterate.