Recap: Domino Data Lab Panel, Governance Strategies to Mitigate AI Risks
I recently had the privilege of attending a fascinating panel discussion hosted by Domino Data Lab, where industry leaders came together to share their thoughts on one of the most pressing topics in technology today: AI governance. A huge thank you to panelists Diya Wynn, Dr. Kjell Carlsson, Bill Hostmann, and Leila Nouri for their valuable insights and expertise.
The conversation made one thing clear: AI offers transformative potential, but harnessing it responsibly requires proactive leadership, cross-functional collaboration, and adaptive frameworks. Governance isn’t a roadblock—it’s a value driver. When done right, it builds trust, inspires change, and ensures that innovation flourishes sustainably.
Organizations that will thrive in this new era are those that balance innovation with accountability, invest in skills development, and foster a culture of continuous learning. It’s not just about the technology—it’s about people, processes, and partnerships aligning to unlock AI's full potential.
In this post, I'll walk through the key insights shared during the discussion.
Panel:
Diya Wynn: Responsible AI Lead. AWS
Dr. Kjell Carlsson: Head of Data Science Strategy, Domino
Bill Hostmann: VP & Research Fellow, Dresner Adivsory Serviers
Leila Nouri: Director of Product Marketing, Domino
1. Market Shift in AI Governance
Commercial viability and business value:
AI has shifted from theoretical to practical, scalable applications.
Organizations must now balance value with evolving risks.
One-size-fits-all solutions no longer work in this changing environment.
Some organizations are moving quickly to capitalize on AI, while others are struggling due to a lack of vision.
2. Challenges in Governance
Emerging risks and responsibilities:
Diya Wynn:
Governance is still in its early stages, and risks need better understanding.
Non-tech organizations now see themselves as tech companies but are unprepared to act accordingly.
Efforts must focus on balancing cost-benefit with efficiency creation.
Dr. Kjell Carlsson:
Large companies, even those further along with technology, face challenges and can become too cumbersome.
Bill Hostmann:
Accessing the right data remains difficult.
Modernizing platforms and skills demands significant investment.
Data Access Challenges:
Democratizing data introduces complexities around access control.
Governance must allow innovation while ensuring responsible data management.
Diya Wynn emphasized the importance of asset cataloging and readiness for data migrations.
Regulatory pressures and expertise gaps:
Bill Hostmann: Organizations are navigating legal uncertainties and need a more holistic regulatory view.
Leila Nouri: Many discussions are led by people lacking governance expertise.
3. People and Skill Gaps in Governance
Leadership and communication needs:
Bill Hostmann:
Data leaders may not fully understand all organizational initiatives, so clear communication is essential.
Understanding upstream and downstream processes is key.
Dr. Kjell Carlsson:
Senior leaders need to take ownership and provide support for governance efforts.
There is often a “white space” where leadership is needed to consolidate priorities.
Diya Wynn:
Governance is linked to digital transformation and requires alignment across people, processes, and technology.
Clear roles and responsibilities throughout the data lifecycle are essential, mirroring cloud transitions.
Bill Hostmann:
The emergence of new industries will demand new skills, and while experience with AI exists, future needs will evolve.
Diya Wynn:
Lessons can be learned from past transformations to prepare for future shifts.
4. Assessing Governance Maturity
Frameworks, risk management, and planning:
Dr. Kjell Carlsson:
Governance frameworks often start with committees and principles, but execution gaps exist.
Auditability and proactive risk management are crucial to preventing misuse or connections to risky environments.
Processes need automation to scale efficiently while maintaining flexibility.
Bill Hostmann:
Scenario planning is essential for governance, balancing driving forces and constraints.
Leaders should work backward from specific use cases to assess governance maturity.
Diya Wynn:
Many organizations are just beginning to ideate their AI governance frameworks.
Governance efforts must cut across all teams, not just tech functions, with a clear understanding of current status and future needs.
Regulatory compliance will increasingly align with risk-based approaches.
5. Solutions for Effective Governance
Foundation in data governance:
Bill Hostmann:
Data governance is a critical starting point for AI governance, laying the foundation for platforms and models.
Dr. Kjell Carlsson:
Governance must follow the data lifecycle, evolving based on project needs.
Continuous feedback loops are essential to mitigate and manage risks.
Leila Nouri:
A proactive and holistic governance approach ensures sustained effectiveness.
6. Key Advice for Governance Implementation
Diya Wynn:
Start immediately with education and leader-led strategies.
Governance must focus on people, with diversity to bring varied perspectives and understand stakeholders' needs.
Safe systems and robust infrastructure are essential.
Bill Hostmann:
Governance solutions are not one-size-fits-all.
Different data use cases require diverse expertise and tailored auditing methods.
New competencies will need to be identified and nurtured.
Dr. Kjell Carlsson:
Governance is not a burden on innovation; it is a value driver.
Trust fosters adoption, motivates change management, and encourages investment.
The right governance mindset ensures all stakeholders are aligned and engaged in decision-making.
