Do we need a complete AI policy before starting?
No. Begin with one consequential workflow, identify its authority and failure modes, and use that evidence to shape a policy that can actually be enforced.
Strategic technology advisory
AI Governance Advisory
Practical governance for AI systems that connects authority, data, controls, human oversight, evidence, and measurable value.
Book an intro callWho it is for
Built around a real decision.
For Canadian organizations moving from experimentation to consequential AI use across enterprise, public-sector, security, and operational workflows.
Decision outcomes
What becomes clearer
- Define which AI uses are acceptable, conditional, prohibited, or require explicit approval.
- Separate authentication and access from an agent or system’s authority to act.
- Bind controls to real workflows, tools, data, approvals, limits, and evidence.
- Design governance that can enable delivery instead of remaining a policy-only layer.
Engagement output
Evidence people can act on
- AI use-case qualification and risk decision framework.
- Authority, approval, and human-oversight model.
- Minimum control and evidence requirements.
- Pilot-to-production governance roadmap.
Questions leaders ask
Is this limited to generative AI?
No. The approach applies to predictive, generative, and agentic systems, with controls scaled to the system’s data, autonomy, and consequences.
How is governance kept practical?
Every requirement must connect to a decision, an enforceable control, an accountable owner, or evidence that can be reviewed after the system acts.
Start a conversation
Make the ai governance decision explicit.
A short first conversation can establish the decision, the available evidence, and whether an independent review would be useful.