Author(s): Bardella, Fernando^1,*, Fernandes, Stacey ², Bilotta, James ^3
1 Research Fellow, Disruptive Technologies Lab (DTL) at Nuclear and Energy Research Institute (IPEN-CNEN);
² Chief Technology Officer (CTO), Versetal Information Systems;
³ Subject Matter Expert (SME), Independent Advisor;
* Correspondence: bardella@ipen.br (F.B.);

This whitepaper examines how regulated life sciences organizations can govern artificial intelligence (AI) as a sociotechnical enterprise capability rather than as a standalone model, vendor, or platform decision. As AI moves from experimentation into clinical, regulatory, quality, pharmacovigilance, commercial, manufacturing, safety, software development, and operational workflows, organizations face a broader governance challenge: not only which AI technologies to adopt, but whether those technologies can be used safely, repeatedly, defensibly, and in ways that create measurable value.

The paper argues that durable AI advantage in life sciences will depend less on access to any single frontier model and more on the organizational “plumbing” required to operationalize AI responsibly. This includes trusted data products, secure architecture, access controls, prompt and configuration governance, lifecycle validation, human oversight, named accountability, cost management, portfolio discipline, and value-chain impact. It also emphasizes that AI-enabled workflows must be governed in relation to their context of use, decision impact, regulatory exposure, patient relevance, data sensitivity, and operational consequences.

Drawing on regulatory guidance, standards, academic literature, and insights from the SIM Boston Life Sciences CIO Roundtable, the whitepaper presents a practical operating model for responsible AI scale. It translates AI risk into executive governance questions, lifecycle gates, evidence artifacts, and enterprise controls that can help organizations move beyond pilots toward auditable and accountable production use. The intended audience includes CIOs, technology executives, board members, quality and regulatory leaders, data and digital leaders, and cross-functional teams responsible for AI readiness in regulated life sciences.

The central call to action is for life sciences CIOs and executive teams to treat AI readiness as an enterprise agenda item. Responsible scale requires more than tool access or experimentation; it requires governance that protects patients, preserves trust, supports regulatory defensibility, and connects AI adoption to meaningful enterprise and societal value.