The new R&D skill stack: why the future of expertise looks different

2026-07-21 · Source: Everest Group Research Portal · Field: Business & Management — Human Resources & Workforce Development, Corporate Strategy & Leadership, Operations & Process Management · Depth: Intermediate, short

Summary

Expertise in Research and Development (R&D) is undergoing a significant transformation as Artificial Intelligence (AI) and digital capabilities reshape how work gets done. Organizations traditionally focused on deep scientific specialization are now seeing human value shift from routine execution to higher-order judgment. AI automates tasks like documentation and repetitive experimentation, while expanding possibilities through simulation and generative modeling. This necessitates a new R&D skill stack, complementing deep scientific knowledge with computational fluency, systems thinking, and strategic judgment. Companies like Amgen and Genentech are already adapting by hiring for emerging roles such as computational scientists and digital lab architects, signaling a move towards connected, multidisciplinary innovation models.

Key takeaway

For R&D leaders building talent models for future innovation, you must redesign roles to prioritize judgment, collaboration, and scientific decision-making. Focus on integrating computational fluency and systems thinking into your skill stack, complementing deep scientific expertise. This approach will yield greater returns from AI investments and ensure your organization's talent strategy aligns with evolving R&D value creation.

Key insights

AI is shifting R&D value creation from routine execution to human judgment, computational fluency, and systems thinking.

Principles

• Deep scientific knowledge remains foundational.
• AI redistributes human value in R&D.
• Human judgment is the limiting factor in AI-driven R&D.

In practice

• Redesign roles around judgment and collaboration.
• Expand hiring for computational scientists.
• Combine scientific expertise with computational capability.

Topics

• R&D Skill Stack
• Artificial Intelligence in R&D
• Computational Fluency
• Systems Thinking
• Strategic Judgment
• Talent Management

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

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Editorial summary, takeaway, and curation by AIssential. Original article published by Everest Group Research Portal.