build an AI operating model explains how an organization turns AI ambition into owned decisions, reliable delivery, responsible operation, and measurable business results.
This guide is written for Canadian organizations. The recommended structure should be adapted to company size, sector, portfolio, skills, sourcing, existing controls, and the potential impact of each use.
Table of contents
- 1. Start with outcomes
- 2. Review the current state
- 3. Define required capabilities
- 4. Set decision rights
- 5. Design the lifecycle flow
- 6. Choose funding and sourcing
- 7. Define measures
- 8. Test before scaling
- Practical checklist
- Frequently asked questions
- References
“Do not begin by drawing boxes. Begin with the decisions and evidence required to move a use case safely from a business problem to a sustained result, then organize people around that flow.”
Mehrzad Verdizadegan,
CEO, Praevion Consulting Inc.
1. Start with outcomes
To build an AI operating model, define the customer, employee, operational, financial, or risk outcomes that AI should support.
Give each priority a baseline, target, deadline, and executive owner.
2. Review the current state
Map the portfolio, workflows, data, platforms, vendors, skills, governance, funding, and decision bottlenecks.
Identify capabilities that already exist before creating new teams or titles.
3. Define required capabilities
List the recurring work needed for strategy, product ownership, data, engineering, change, governance, vendor management, and value measurement.
Design around real work, not fashionable structures.
4. Set decision rights
State who recommends, approves, executes, must be consulted, and can pause or retire a system.
Use risk tiers so routine low-risk work moves quickly and material uses receive stronger challenge.
5. Design the lifecycle flow
Connect idea intake, discovery, procurement, data access, validation, build, approval, deployment, adoption, monitoring, change, and retirement.
Every gate should have an owner and evidence requirement.
6. Choose funding and sourcing
Decide which work receives central funding, which business units fund, and when external expertise is used.
Keep enough internal capability to own outcomes, challenge vendors, and operate responsibly.
7. Define measures
Track decision time, rework, adoption, workflow results, cost, incidents, control findings, and realized value.
Activity alone does not show whether the model works.
8. Test before scaling
Run the model through two or three different use cases. Repair unclear handoffs and duplicated approvals before reorganizing widely.
A Canadian SME may use one accountable executive and a virtual team, while a large enterprise may need shared platforms and dedicated assurance.
Questions for the next operating-model review
Ask whether decisions sit with people who have authority, delivery teams can access the capabilities they need, governance matches risk, and business owners can show realized value. Check delays, rework, duplicated tools, control gaps, weak adoption, and systems that remain in operation without a clear owner.
build an AI operating model checklist
- Define the outcomes, portfolio scope, and accountable executive.
- Assign business, product, technical, data, and control ownership.
- Map the lifecycle from idea and procurement through operation and retirement.
- Set risk-based decision rights, evidence, approval, and escalation.
- Provide shared data, technology, learning, vendor, and governance services.
- Measure decision time, adoption, outcomes, cost, incidents, and realized value.
Related Praevion guidance
- Read the related Praevion operating-model guide
- Explore the next related article
- Explore Praevion Consulting Inc. digital transformation services
Frequently asked questions
How long does design take?
A practical first design can take six to twelve weeks, followed by testing and refinement.
Who should lead the work?
A senior business executive should sponsor it with business, technology, data, people, finance, and risk leaders.
Should the company reorganize immediately?
No. Test decision flows and capability needs before making large structural changes.
Executive takeaway
How Do You Build an AI Operating Model? The practical answer is to design around decisions and workflows, not titles alone. Keep business value close to operating leaders, share capabilities that benefit from scale, and make accountability visible from discovery through retirement.
To discuss your needs, contact Praevion Consulting Inc..
References
- Statistics Canada, Analysis on artificial intelligence use by businesses in Canada, second quarter of 2026
- OECD, BCG and INSEAD, The Adoption of Artificial Intelligence in Firms, 2025
- OECD, Skills in the AI Age, 2026
- NIST, Artificial Intelligence Risk Management Framework
- ISO/IEC 42001:2023, AI management systems
- Government of Canada, Guide on Departmental AI Responsibilities
