Transforming R&D with AI and Quantum Computing - with David Carmona of Microsoft

2026-01-29 · Source: The AI in Business Podcast · Field: Business & Management — Corporate Strategy & Leadership, Operations & Process Management, AI & Quantum Innovation · Depth: Intermediate, extended

Summary

David Carmona, Vice President of Discovery & Quantum at Microsoft, discusses how AI and quantum computing are transforming R&D beyond mere productivity gains, focusing on net-new scientific discovery and augmented reasoning. He highlights R&D as a high-impact domain for AI adoption, particularly in regulated sectors like life sciences, materials, and energy. Carmona explains how AI orchestrates the scientific method, from hypothesis generation and simulation to scaling experimentation using specialized AI agents. The conversation emphasizes the need for explainability, traceability, and human-in-the-loop governance in compliance-heavy environments, stressing that AI integration requires rethinking entire R&D processes rather than just augmenting individual scientists. He also provides a four-point framework for R&D leaders covering strategy, execution, technology, and governance.

Key takeaway

For R&D leaders navigating AI adoption in regulated industries, prioritize investments that enable entirely new scientific discoveries and industrial breakthroughs, rather than solely focusing on productivity gains. Your strategy must encompass a comprehensive approach to execution, technology democratization, and robust governance, ensuring explainability and human oversight are built into AI-driven workflows from the outset. This shift requires redesigning core R&D processes to integrate specialized AI agents, fostering a culture where every R&D role is skilled in AI and empowered with the necessary tools.

Key insights

AI in R&D drives net-new discovery and industrial breakthroughs, not just incremental productivity.

Principles

• AI should enable new discoveries, not just efficiency.
• Rethink R&D processes for AI integration.
• Trust is paramount in regulated AI adoption.

Method

AI orchestrates the scientific method by generating hypotheses, simulating experiments, and learning from results, integrating specialized AI agents with human scientists in a continuous lifecycle.

In practice

• Balance moonshots with short-term AI applications.
• Implement explainability and traceability for AI decisions.
• Democratize AI tools across the R&D organization.

Topics

• AI in R&D
• Scientific Discovery
• AI Agents
• Regulated Environments
• AI Governance

Best for: Executive, Director of AI/ML, VP of Engineering/Data, CTO

Related on AIssential

• Our Guide to the Summer 2026 Issue — Effective leadership and structured approaches are key to utilizing advanced technologies and navigating complex business challenges.
• Demis Hassabis On What AI Will Do Next — AI is evolving into a co-creative partner, poised to accelerate scientific discovery and revolutionize human health within decades.
• Is the discourse around AI getting too black-and-white? — Responsible AI integration requires moving beyond panic to focus on minimizing harm and protecting livelihoods.
• AI Scientists as Engines of Discovery: A Case for Development within Reformed Institutions — Agentic AI systems are evolving into "AI scientists," necessitating institutional reform for responsible scientific discovery.
• AI agent adoption: From scientists to CFOs — AI is rapidly compressing expertise and democratizing access to specialized knowledge across diverse professional domains.
• A Big Shift in the AI Race — The AI race is shifting, with national security, economic realities, and regulatory clarity becoming central to frontier model development.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by The AI in Business Podcast.