Transforming R&D Efficiency: The Impact of AI on Productivity

Innovation has historically driven human progress, but recent trends suggest that finding and developing good ideas is becoming increasingly difficult and costly. R&D productivity is declining across various sectors, including semiconductors and pharmaceuticals, with notable increases in the costs required to maintain performance levels, as evidenced by concepts like “Eroom’s Law.” The rise of AI presents a potential solution to these challenges by vastly improving R&D processes across industries. AI can increase the volume, velocity, and variety of innovation candidates, enhance evaluation methodologies through surrogate models, and streamline operations. These advancements could unlock significant economic value, estimated between $360 billion to $560 billion annually, primarily in sectors heavy on intellectual property. However, realizing this potential requires organizational changes—such as integrating R&D processes, building core competencies around AI models, and prioritizing human roles in innovation. For firms willing to adapt, AI offers pathways to revitalizing innovation and fostering new growth.

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