Governance operating model
Define roles, forums, decision rights, escalation paths, and accountability across business, technology, risk, and legal teams.
AI OPS GURUAI governance consulting
Scale AI with governance that protects momentum and trust.
We help organizations establish practical AI governance—clear enough to manage risk and accountability, and usable enough that teams can still innovate, implement, and improve.
Discuss your initiativeWhat we deliver
Governance should make responsible delivery easier.
Effective governance translates principles into decisions, controls, evidence, and ownership across the AI lifecycle. We build operating practices that fit your risk profile and delivery environment.
AI inventory and risk tiering
Create a usable view of AI systems and use cases, with proportionate review requirements based on impact and risk.
Lifecycle controls
Establish requirements for design, data, testing, security, documentation, approval, monitoring, and retirement.
Adoption and enablement
Equip delivery teams with practical standards, templates, training, and support that turn policy into daily behavior.
Operational outcomes
Designed to create value that shows up in the work.
AI use cases and ownership are known
Controls align with material risk
Governance becomes operational practice
How we work
From strategic intent to sustained operational capability.
- 01
Baseline
Assess current AI activity, policies, stakeholders, controls, obligations, and risk posture.
- 02
Design
Define the governance model, risk tiers, lifecycle requirements, decision rights, and evidence standards.
- 03
Activate
Launch processes, templates, inventory, forums, training, and delivery-team support.
- 04
Assure
Monitor adoption, exceptions, control effectiveness, emerging risks, and opportunities to simplify.
Common questions
What leaders should know before getting started.
What does practical AI governance include?
Practical AI governance defines ownership, risk tiers, review requirements, lifecycle controls, documentation, testing, approval, monitoring, incident response, and retirement. It also gives delivery teams usable standards, templates, and support.
How do you avoid slowing AI delivery with governance?
Use proportionate controls. Low-risk use cases should follow a lighter path, while higher-impact systems receive deeper review and evidence requirements. Clear decision rights, reusable patterns, and defined service levels reduce uncertainty and delay.
What belongs in an AI inventory?
An inventory should capture the use case, business owner, technical owner, models and vendors, data categories, affected users, decision impact, risk tier, current status, approvals, key controls, monitoring, and material dependencies.
How often should AI systems be reviewed?
Review frequency should reflect impact, rate of change, model and data drift, incident history, regulatory obligations, and vendor changes. High-impact systems need continuous monitoring plus scheduled review; lower-risk uses may be reviewed less often.
AI Ops Guru