AI governance for innovation

What you can expect

We’ll set the context for the session, align on objectives, and explore why AI governance is now a leadership and operating-model issue, not just a compliance exercise. 

Why AI changes the governance picture 

To get started, we’ll establish a shared understanding of what AI governance needs to achieve in practice. This will cover: 

• how AI changes the governance picture through speed, opacity and autonomy 

• why existing delivery, security and data fundamentals matter more with AI 

• the difference between governance-as-gate and governance-as-accelerator 
• how to create the conditions for safe AI adoption without slowing progress 

Making sense of the landscape without drowning in detail 

We’ll give an executive-level view of the regulatory and standards environment shaping AI adoption, including: 

• EU AI Act considerations 

• UK regulatory direction and sector expectations 

• ISO/IEC 42001 

• NIST AI Risk Management Framework 

• where organisations risk duplicating effort across controls, evidence and assurance

Participant activity: Where governance enables vs throttles 

Participants will explore the operating-model patterns that help AI move safely from experiment to production. This includes: 

• the approval committee anti-pattern 

• clear lanes for standard, high-risk and prohibited use 

• reusable evidence for security, risk and audit 
• decision rights: who decides what, when, and on what evidence 

What keeps you awake? 

A facilitated discussion on the AI concerns leadership teams are facing right now, including: 

• shadow AI and employee use of public models 

• agentic systems and accountability for autonomous decisions 

• third-party model providers and supply-chain risk 

• speed of approval versus quality of assurance 

Participant activity: Turning insight into action 

Participants will map their current governance approach against the enable-vs-throttle pattern and identify the changes that would most accelerate safe AI adoption. 

This will help surface: 

• where governance is helping 

• where governance is slowing delivery 

• the most material risks and blockers 

• practical changes that could improve confidence and speed 

Recap, Q&A and practical next steps 

We’ll close with key takeaways, follow-up options and a clear view of where internal effort or external support could help. 

After this session, you’ll walk away with: 

A shared leadership view of AI governance 

A clearer understanding of how governance, security and delivery need to work together for AI to scale. 

A practical view of your current blockers 

Insight into where your current approach enables progress, and where it may be slowing delivery. 

Clearer decision-making and accountability 

A view of who needs to decide what, when, and on what evidence. 

A stronger route from AI experiment to production 

Practical next steps for moving AI forward safely, confidently and with the right foundations in place.