Transforming R&D with AI and Quantum Computing - with David Carmona of Microsoft (27 min)
- Release date: 2026-01-29
- Listen on Spotify: Open episode
- Episode description:
Today's guest is David Carmona, Vice President of Discovery & Quantum at Microsoft. David leads work at the intersection of AI, scientific discovery, and advanced computing, with a focus on scaling research innovation in complex, regulated environments. David joins Emerj Editorial Director Matthew DeMello to discuss how enterprise leaders should think about AI's role in transforming R&D beyond productivity gains—toward net-new discovery, augmented scientific reasoning, and structurally different innovation workflows. The conversation explores why R&D represents one of the highest-impact domains for AI adoption, and how coordinated systems of specialized models and agents are reshaping the scientific method itself. Want to share your AI adoption story with executive peers? Click emerj.com/expert2 for more information and to be a potential future guest on the 'AI in Business' podcast! If you've enjoyed or benefited from some of the insights of this episode, consider leaving us a five-star review on Apple Podcasts, and let us know what you learned, found helpful, or liked most about this show! Watch Matthew and David's conversation on our new YouTube Channel: youtube.com/@EmerjAIResearch.
Summary
- 🚀 Net New Discoveries: AI’s biggest R&D value lies in enabling breakthroughs impossible before, like generating millions of drug candidates, across industries beyond just productivity gains.
- 🔄 Workflow Redesign: Scale via specialized AI agents orchestrating hypothesis generation, simulation, and experimentation in collaboration with scientists, rethinking entire R&D processes.
- 🔒 Trust Foundations: In regulated spaces, explainability, traceability, and human-in-the-loop governance build compliance and free experts for high-level decisions.
- 📊 4-Pillar Strategy: Balance productivity, cognition augmentation, and moonshots with comprehensive execution, technology access, and centralized governance for R&D success.
- 👥 C-Suite Culture Shift: Leaders drive scale through cultural change, skilling, incentives, measurement, and top-down/bottom-up adoption to embed AI organization-wide.
Insights
Why does AI transform R&D more radically than other business areas by enabling impossible breakthroughs?
Time: 5:06 – 6:22
Category: AI-Driven Innovation EconomyAnswer: Unlike productivity boosts elsewhere, AI in R&D drives net new scientific discoveries, creating products and revenue lines previously unattainable across industries like healthcare, energy, and materials. This shifts focus from efficiency to innovation, powering every sector reliant on complex problem-solving. (Start at 5:06)
How can AI agents orchestrate end-to-end scientific workflows to scale R&D discovery?
Time: 7:05 – 9:46
Category: AI-Driven Innovation Economy, AI in Workforce DisruptionAnswer: AI mimics the scientific method by reasoning over literature, generating hypotheses, simulating experiments, and iterating results via specialized agents collaborating with scientists. This rethinks R&D processes beyond individual tools, generating millions of candidates and filtering them efficiently. (Start at 7:05)
What ensures trustworthy AI integration in highly regulated R&D environments like drug discovery?
Time: 10:06 – 12:08
Category: AI Governance & LawsAnswer: Explainability of every AI decision, full traceability, and constant human-in-the-loop oversight are essential to open the ‘black box’ and comply with regulations in fields like life sciences and chemistry. These enable safe collaboration between AI agents and scientists. (Start at 10:06)
How should R&D leaders balance short-term wins with moonshot breakthroughs in AI strategy?
Time: 13:15 – 17:13
Category: AI-Driven Innovation EconomyAnswer: Adopt a comprehensive strategy blending productivity gains, augmented cognition via copilots, and process redefinition for discoveries, ensuring short-term efforts ladder up to long-term visions. This is paired with organization-wide execution, technology democratization, and centralized governance. (Start at 13:15)
Why must C-suite leaders prioritize culture and measurement to scale AI across R&D organizations?
Time: 17:50 – 19:34
Category: AI in Workforce DisruptionAnswer: Scaling innovation requires top-down strategy, cultural shifts via skilling and incentives, accessible platforms, and governance for measurement—beyond isolated teams. Leaders drive enterprise-wide adoption by articulating target AI culture and blending top-down with bottom-up approaches. (Start at 17:50)
How does change management, not tech, now block enterprise AI adoption in R&D?
Time: 20:02 – 22:39
Category: AI in Workforce DisruptionAnswer: Cultural clarity on desired AI-embracing attributes, combined with top-down leadership and bottom-up employee involvement (as in Microsoft’s finance example), overcomes adoption hurdles. This shifts from past tech issues like MLOps to human-centered transformation. (Start at 20:02)