Objective:

To explore the rise of Shadow AI in biopharma R&D and its implications for scientific practice and infrastructure, particularly how it highlights the disconnect between compliance-focused tools and scientists' practical needs.

Key Findings:

• Only 7% of scientists can configure assays in their ELN without specialist support.
• 65% of scientists repeat experiments due to difficulty in finding and interpreting prior results.
• 77% of scientists use public AI tools in their lab work, often through personal accounts.
• Only 5% of scientists can analyze experimental results independently within official tools.

Interpretation:

Shadow AI reflects unmet demands within official systems, indicating a significant disconnect between compliance-focused tools and the practical needs of scientists.

Limitations:

• Organizational responses to Shadow AI often focus on restriction rather than addressing underlying issues.
• Use of personal accounts for AI processing introduces risks related to visibility and integrity, as unreviewed AI outputs can undermine scientific credibility.

Conclusion:

The future of AI in biopharma R&D lies in embedding intelligence within official systems to enhance scientific reasoning and maintain compliance, balancing both needs.