AI-native companies face a distinct governance challenge — they're not just users of AI, they're the vendor everyone else is doing due diligence on. Model risk, training-data provenance and defensible AI claims aren't optional extras; they're what closes enterprise deals.
Typical investment: CAD 12,500-90,000 initial engagement, CAD 8,000-25,000/month ongoing
Every sector treats AI governance differently — what counts as a real risk, who signs off on it, and which frameworks actually get referenced in a Board conversation. The breakdown below isn't generic: it reflects how AI governance plays out specifically for organizations in Artificial Intelligence, from the challenges that show up first to the people who typically need to be in the room.
Artificial intelligence management systems — organizational accountability, AI policies, lifecycle governance, risk management and continuous improvement.
The Govern, Map, Measure and Manage approach for identifying and controlling risks arising from AI systems.
Risk-based AI regulation addressing prohibited practices, high-risk systems, transparency and general-purpose AI obligations across the AI lifecycle.
The stakeholders typically at the table for an engagement in this sector.