Effective AI governance requires phased implementation, ongoing optimisation and measurable results. Rushing the process leads to gaps while a structured rollout ensures long-term impact and compliance.
Phased Rollout
- Foundation Phase (2–3 months): Begin by forming a cross-functional governance committee with representatives from legal, privacy, IT, security and business units. Audit current AI usage to uncover AI tools and high-risk applications. Draft core policies, such as Acceptable Use, Privacy and Data Classification and launch employee awareness training.
- Control Phase (2–3 months): Configure and deploy monitoring solutions to track AI tool usage and data flows. Formalise procurement processes to ensure only approved AI platforms are used. Create incident response plans tailored to AI-related events, such as data leaks or unauthorised model training. Security teams may set up alerts for unusual data access, while business units document approved use cases.
- Optimisation Phase: Refine policies based on feedback and best practices. Expand the portfolio of approved AI tools as new solutions prove their value. Regularly gather user feedback and monitor compliance rates. Treat this phase as continuous improvement, review policies quarterly and update training as regulations evolve.
Measuring Success
Track both risk reduction and business enablement:
- Shadow AI usage decline: Fewer unauthorised tools in use.
- Time to approve new tools: Streamlined, not obstructive.
- AI-related incident frequency: Fewer and less severe compliance breaches.
- Training completion rates: High participation and understanding.
- Audit success and user satisfaction: External validation and positive feedback.
Balanced metrics build a governance program that supports innovation, not just compliance.
Frameworks to Follow
Choose frameworks based on industry and maturity:
- ISO 42001: Certifiable, risk-based governance
- NIST Framework: Flexible, widely adopted guidelines for mapping, measuring and managing AI risk.
- IEEE Standards: Technical and ethical guidance for applications.
- ENISA Guidelines: AI cybersecurity best practices, especially relevant for European operations.
Highly regulated industries may prefer ISO; others may start with NIST, with a more accessible framework.
Culture & Future-Proofing
Governance success depends on people. Leadership must model responsible AI usage and communicate its importance. Empower employees with clear escalation paths for AI concerns and recognise compliance excellence. Regular feedback loops and policy reviews help adapt to new risks and technologies.
Contact our team to discuss your AI governance implementation strategy, schedule a governance workshop or get tailored advice for your industry.
