Data Science Leaders – Détails, épisodes et analyse

As organizations race to become AI-driven, data has emerged not just as a resource, but as a strategic advantage. In this episode, Kjell Carlsson sits down with Doug Laney, author of Infonomics and Data Juice, to explore how generative AI is accelerating the journey toward data monetization and organizational autonomy. Doug shares why traditional approaches to data management no longer suffice, how digital twins and agentic AI are redefining business models, and why the future may belong to self-driving organizations.

Tune in to hear:

• How AI is reshaping the economics of data and enabling new business models
• Why organizations should embrace agentic AI and digital twins to stay competitive
• What leaders must do today to build autonomous, AI-native enterprises

AI strategy isn't just about technology. It's about transformation.

In this episode, CDAO expert, Dr. Shahram Ebadollahi, joins to discuss what it takes to lead AI in an enterprise and why that leadership must go far beyond just selecting the right tools or models. Drawing on his experience building AI teams, Shahram explains why the Chief AI Officer role is one of the most complex, and most critical, positions in modern organizations. Here we talk about the cultural, organizational, and strategic shifts needed to embed AI deeply and responsibly, and how to move from experimentation to value delivery at scale.

Whether you're leading AI or supporting it, this conversation offers a clear-eyed view of what success really requires.

Tune in to hear:

• Why the Chief AI Officer must balance tech depth with business vision
• How to structure teams and incentives for lasting AI success
• How to move from AI experimentation to real business impact

• How leadership and experimentation drove the success of the Defense Innovation Unit
• The importance of developing new frameworks for procurement for AI adoption
• Insights from modern warfare: drone technology and the shift to consumer-driven innovation
• Best practices for scaling technological transformation across any large organization

• The core requirements of the act and how they impact your use cases
• Gray areas to look out for in the act
• Compliance with existing regulations such as DORA and GDPR
• Core governance capabilities that enable compliance for the EU AI act and future regulation

• The value of AI governance for driving adoption and innovation
• How governance principles apply to traditional ML and genAI
• The common challenges of governance 
• Real-world examples and best practices from advanced teams integrating AI governance
• How automation helps scale governance and provide the flexibility to support future regulation

• Why you should care about AI governance (and why it goes beyond ethics and regulation)
• What AI governance is (and how it differs from ethical AI, responsible AI, and trustworthy AI)
• Why AI governance is difficult (and the gap between frameworks and practices)
• What AI governance looks like in practice (across the AI/ML lifecycle)
• What we need for AI governance (and the capabilities to do it at scale) 

• The value of privacy (~5% of annual profit)
• The many definitions of privacy.
• The top 3 misconceptions: no one cares, it can be fixed with a privacy policy, and privacy is a blocker.
• Who should own privacy in your organization.
• PETs vs. data hygiene

• The AI talent gap and the strategies that firms are using to close it
• The myriad AI capabilities that high-readiness firms are implementing
• The need to manage AI costs upfront to ensure deployment
• The perpetual question of build vs. buy (or both!)
• The crucial need for AI governance to ensure approval and adoption

• The role of AI in digital strategy today.
• Overcoming the challenge of aligning AI to business value.
• Experimenting efficiently with digital twins and a contrarian in the loop.
• Breaking hierarchies with multifunctional pods for faster impact.

• The greatest GenAI opportunities in marketing and beyond
• How to maximize AI impact with decision optimization
• Responsible AI and the challenges of AI systems going very right
• The emerging field of AI consciousness
• The Chief AI Officer: why you need one and the prerequisites for success