Summary

Life sciences organizations are moving from AI experimentation toward practical use. The report finds rising investment in Generative and Agentic AI, with slower movement toward Autonomous AI because of regulatory, data, and oversight concerns.

The research shows AI is gaining traction in documentation, investigations, validation, quality, manufacturing, supply chain, and regulatory workflows. Progress depends on better data quality, governance, integration, and workforce readiness.

Key takeaways

• Generative AI is the main entry point for adoption, while Agentic AI is emerging in workflow coordination and Autonomous AI remains early stage.
• Data quality, privacy, governance, and integration with existing systems are the main barriers to scaling AI across life sciences.
• Quality, CQV, documentation, investigations, and operational workflows are among the clearest near-term areas for AI value.

Who is this for

• Quality leaders and QMS owners

• Regulatory affairs professionals

• Validation and CQV teams

• Manufacturing operations leaders

• R&D and product development teams

• Digital transformation and IT leaders

• Data governance and analytics teams

• Supply chain and asset management professionals

• Life sciences executives evaluating AI investment

• Technology providers serving regulated life sciences companies