Advisory Offer
Responsible AI Governance Framework
Establish responsible AI principles, risk classification, intake, review, decision rights, human oversight, monitoring and accountability.
Where this sits in a D² engagement
- Assess
- ArchitectThis offer
- Enable
The challenge
AI experimentation is spreading across the organization faster than the controls around it. Nobody is certain who approves AI use, how risk is classified or how outcomes are monitored.
What D² delivers
A right-sized responsible AI governance framework covering principles, risk classification, intake and review processes, decision rights, human oversight, monitoring and accountability, all proportional to your risk profile and regulatory context.
Typical activities
- AI use inventory and risk landscape review
- Responsible AI principle definition
- Risk classification model design
- Intake, review and approval process design
- Decision rights and accountability mapping
- Human oversight and monitoring design
Expected outputs
- Responsible AI principles and policy foundation
- AI risk classification framework
- Intake and review workflow
- Governance roles, decision rights and council design
- Monitoring and accountability model
Ideal client profile
- Regulated organizations adopting AI
- Enterprises scaling AI beyond initial pilots
- Leaders accountable for AI risk and compliance
Engagement format
A framework-design engagement typically followed by supported adoption of the first governance cycles.
Discuss whether this engagement fits your situation.
Scope, depth and format are right-sized in the first conversation, before any commitment.