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In January 2020, Stephen Hahn, M.D., had been running the FDA for just a few weeks when the COVID-19 virus sequence was published. Despite the odds, in just eleven months, the first mRNA vaccine was authorized. It was the fastest development of its kind in history. And the part Hahn can't stop thinking about isn't how hard it was. It's what it proved: medicine can move that fast, even though it usually doesn't. Now, as Chief Executive Officer of Nucleus RadioPharma, he makes a new argument for data science leaders: the hard part in medicine isn’t the science. It’s what we do with the data. Stephen brings: Why he calls AI in molecule discovery “table stakes”What leading the largest real-world experiment in history taught him How the FDA compressed years into months, and how we can do it again

Justin Johnson sat down one afternoon to build a new capability for ARIA, the AI research agent he runs on a supercomputer that sits on his desk. Before writing a single line, he checked what was already there and found that his own system had designed and built the exact thing he was about to start. Three months earlier. While he was asleep. That was the moment Justin went, in his words, from first mover to second mover on his own project, and he's still working out what that means. Justin brings: How the “second mover” gap is wideningWhere most senior data science leaders really fall on the copilot-versus-harness divideHow the old trade-off between mission and frontier technology is collapsing in biopharma Be sure to check out Justin’s book, Builder-Leader: The AI Exoskeleton That Crosses the Gap, and his blog, Run Data Run.

Dave Johnson’s first pharma job wasn't in a lab. It was in a room, dark and cramped, stacked floor to ceiling with 30 years of decaying discovery chemistry data on every storage medium imaginable. They called it “the cave.” A physicist turned data leader, Dave has spent two decades solving the same problem: scientific data is generated for one purpose, poorly captured, and left abandoned. Now co-founding Dash Bio after years leading data science at Moderna, Dave joins Thomas to make a case the industry would rather not hear: AI pilots keep failing because organizations can't understand their own data or tell whether anything they build improves how they work. Listen in to hear: Why "the cave" is still an accurate description of how most pharma companies manage dataWhy clinical trial numbers mean nothing without the metadata behind themHow the US needs to treat China’s clinical trial volume as a wake-up call

When Cindy Tu first stepped onto a conference stage, it wasn’t part of a long-term plan. It was a turning point. A single speaking invitation shifted her role from quietly reviewing AI systems to actively shaping how governance is practiced across financial services. With a background spanning IT, data, and audit, Cindy brought a rare systems-level view to the table. Now a rising voice in enterprise AI risk, she’s influencing how institutions think about oversight, how governance frameworks evolve, and why people are at the heart of successful implementation. Her perspective is informed not only by technical expertise, but by lived experience. Cindy brings: How to frame governance as an enabler and not a gatekeeperWhy third-party risk keeps rising as gen AI adoption acceleratesHow to flip governance from the “no” guys to the “yes” guys

When Shub Agarwal joined an early conversational AI startup, he was building products in uncharted territory with emerging technology few had heard of. By late 2019, Google and Meta were aggressively recruiting him. But a sudden personal loss made him rethink his priorities, leadership, and the impact he wanted to create. From fast-growth startups to a financial regulator, he's codified a nine-step AI product framework, teaches at USC, and developed practical approaches to trust, governance, and AI agents in organizations. His story reflects intentional leadership in a rapidly evolving field. You’ll hear: How leaders should think about AI agents as part of their teams and workflowsWhy stepping back at key moments makes you a better leaderHow to evaluate AI systems beyond performance metrics

Daniel Kraft, physician-scientist and founder of NextMed Health, joins Domino’s Chris McSpiritt to discuss how AI, data convergence, and systems thinking are accelerating healthcare’s shift from reactive treatment to proactive, personalized care. With a background spanning regenerative therapies, digital health, and aerospace medicine, Daniel offers a wide-lens view of the challenges and opportunities shaping the next era of clinical innovation. From the limitations of “sick care” to the promise of personalized poly-pills and AI-powered digital twins, this conversation explores how to unlock proactive, personalized, and accessible healthcare at scale. Join us as we discuss: How exponential tech is enabling predictive, preventative care and what’s slowing it downWhy “data-to-action” is the next frontier for AI in life sciencesWhat data scientists and tech teams can do now to drive clinical impact Get more of Daniel’s insights at DanielKraftMD.net.

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 modelsWhy organizations should embrace agentic AI and digital twins to stay competitiveWhat 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 visionHow to structure teams and incentives for lasting AI successHow to move from AI experimentation to real business impact

What does the global race for AI look like? How has the US Department of Defense been adopting AI by engaging the private sector? And how should we expect AI policy to shift under the new administration?   In this episode – the last in our series on the future of AI – guest host Joel Meyer, President of Public Sector at Domino, sits down with General Jack Shanahan (Ret.) former Director of the Joint Artificial Intelligence Center at the U.S. Department of Defense, and Lauren Bedula, Managing Director at Beacon Global Strategies. Join us as we explore: How the U.S. Department of Defense is accelerating AI adoptionThe global AI race: U.S., China, and alliesAI policy shifts under the new U.S. administrationPredictions for AI in the Public Sector for 2025 and beyond Also, hear more of Lauren’s thoughts on national security and tech innovation on the Building the Base podcast.

What does the near term future of Enterprise AI look like? A scramble to get valuable, cost-effective, enterprise-grade AI solutions into production, with rigorous governance at scale. If that sounds like a tall order - that’s because it is! However, large advanced organizations (yes, even outside of the tech sector) are already seeing success deploying AI solutions and we can expect the pace to accelerate rapidly in the next 1-2 years. To find out how enterprises are gearing up for this challenge we speak with Richard Swakla, AI and ML Specialist at NetApp and get the inside scoop on the trends that are underway and the strategies organizations are putting in place to operationalize meaningful AI outcomes.   Join us as we discuss:The pressure to shift from experimentation to productionBreaking down the data, infrastructure and organizational silosBest practices for tackling governance, security and cost challengesExamples of AI efficiency gains in healthcare, pharma, and beyond

AI is transforming financial services and insurance, but rarely in the headline-grabbing ways you might expect. From intelligent process automation to fraud detection and risk management, AI is being embedded across operations, but as AI adoption grows, so do the challenges—cybercrime, rapid technological changes, and ever-evolving regulatory frameworks. In this episode we sit down with Adam Gale, Field CTO for AI & Cyber Security at NetApp, and Mike Upchurch, VP of Strategy for Financial Services and Insurance at Domino, to explore:The "silent revolution" of AI in financeUnlocking the potential of unstructured dataAI governance for innovation and regulatory complianceStrategies for driving AI adoptionTop predictions for AI in 2025 in FSI

AI is transforming drug discovery and clinical trials, making what was once cutting-edge the new standard. Pharma companies are evolving into tech companies, leveraging AI to revolutionize every phase of drug development. In this episode, Chris McSpiritt, VP of Life Sciences Strategy at Domino, discusses how AI is no longer a niche capability for select firms but an essential capability for all biopharma organizations. We explore AI-based digital organisms, the increasing reliance on AI in clinical trials, and the shift toward personalized medicine. Join us as we dive into: How AI will drive the majority of new drug discoveriesThe role of AI in clinical trialsThe growing need for pharma companies to adopt a tech company mindsetThe impact of AI-driven automation on regulatory compliance and reporting

What’s next for artificial intelligence in 2025? In this episode, Dr. Kjell Carlsson delivers his annual predictions about the immediate future of AI. From AI becoming the universal corporate scapegoat to the rebranding of generative AI as "agentic AI.” Discover why commercial AI solutions remain scarce, the pivotal role of workforce upskilling in achieving transformative AI outcomes, and how governance is shifting from regulation-focused to impact-driven. Want to see how Kjell fared in his 2024 predictions? Look no further than Data Science Leaders podcast Episode 62: AI in 2024: Predictions on the Future of the AI Revolution.

Innovation and AI governance aren’t at odds. Done properly, governance practices can be the key to accelerating implementation of even the latest GenAI use cases. In this interview from RevX London with Raj Mukherjee, Head of Data Science and AI at Direct Line Group, we find out how they embraced the principles of a lean startup, adopted a product mindset and became the first major insurance company to implement a GenAI solution to accelerate customer service. Come find out how they were able to overcome the challenges of legacy infrastructure, build trust and do the seemingly impossible — make compliance fun.

How can organizations drive transformative AI innovation while effectively managing its inherent risks? Is governance a bottleneck or can it become a catalyst for AI success? In this webinar, we explore the critical role of AI governance with Brandon Purcell, VP Principal Analyst at Forrester Research, and Theo Linnemann, Data Scientist at the Federal Reserve Bank of New York. Together, they share actionable insights into the principles, practices, and policies necessary for ensuring the safe use of AI that drives real-world impact. Join us as we discuss: The evolution of AI governance and why it has become a top priority for AI teamsFrameworks and practices for identifying and managing AI risk The growing importance of automation for effective governanceFairness, business, and other metrics for AI governanceHow to govern an Agentic AI future