The Data Behind the Fastest Vaccine Ever

The tower stood ten meters up in a stretch of Spain close to the Portuguese border so remote there was no cell signal to be found. Every twenty-four hours someone had to climb it, pull the memory card, and come back the next day. The reason? How much groundwater were the oak trees here actually drinking? Since the ecologists couldn’t measure it directly, they measured everything around it instead.Over 10 years later, one of those ecologists, Leo Reyes, is Global Head of Data Science for R&D at WPP, where the measurement problem looks different, but is fundamentally the same. Identifiers are disappearing, client data will not leave client systems, and his organization has gone from one or two AI agents to nearly a thousand. He leads four hundred data scientists through it with an ecologist's instincts: think holistically, account for what you cannot see, and when the water keeps rising, stop building taller walls and go fix the forest upstream.Leo brings:Why the fastest way to measure something is often to measure everything that it isn'tWhat changes in AI governance when you hand teams a token budget instead of a policyWhy accuracy is table stakes and transparency is the thing clients actually buy

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 biopharmaBe 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 impactGet 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 beyondAlso, hear more of Lauren’s thoughts on national security and tech innovation on the Building the Base podcast.