In Thursday’s live session, Dominique Shelton Leipzig is the CEO of Global Data Innovation, where she advises CEOs, boards and legal leaders on AI governance, data privacy and digital trust. Drawing on research from thousands of AI incidents worldwide, she helps organisations build practical governance frameworks that reduce risk while enabling responsible AI adoption. Throughout the session, she emphasised the importance of learning from real-world AI failures to create trustworthy and commercially successful AI programmes.
Session Overview
In the session, Dominique Shelton Leipzig explored how AI regulation is evolving across Europe and the United States following her participation in the Digital Trust Summit in Brussels and the United Nations AI for Good Summit in Geneva. While the EU AI Act remains the most comprehensive framework, she noted that many jurisdictions are adopting similar risk-based approaches, with the US progressing through state legislation, executive orders and industry standards.
A key message was that trust is becoming the foundation of AI innovation. Policymakers, regulators and technology leaders increasingly agree that effective governance enables, rather than hinders, AI adoption. Organisations that establish clear governance, transparency and accountability will be better positioned to realise AI’s benefits while reducing legal and operational risks.
For legal professionals, the focus should be on practical governance rather than regulatory complexity. Shelton Leipzig highlighted common causes of AI failures, including poor data quality, inadequate testing, cybersecurity weaknesses and model drift. With transparency and cybersecurity obligations under the EU AI Act now taking effect, lawyers have an important role in helping organisations prioritise the highest risks and build governance programmes that support both compliance and innovation.
Key Takeaways
- Trust is becoming the foundation of successful AI adoption, with governance enabling innovation rather than restricting it.
- AI regulation is converging globally around risk-based governance, transparency and accountability.
- Lawyers should prioritise the risks that cause most AI failures, including data quality, monitoring, cybersecurity and privacy.
- AI governance requires continuous testing, monitoring and auditing, not a one-off compliance exercise.
- Board-level accountability for AI is increasing, making proactive governance more important than ever.
