The AI Trust Paradox: Life Sciences Leaders Push for Strict Governance

TORONTO and NEW YORK – October 28, 2026 — For years, the life sciences sector has treated artificial intelligence as a promising, if highly regulated, experiment. Now, as the technology matures from isolated pilots to autonomous, enterprise-wide systems, the industry is confronting a critical bottleneck. The primary obstacle to scaling AI is no longer technical readiness, but a profound deficit of trust and a fragmented landscape of accountability.

According to the newly released Responsible AI in Life Sciences (RAIL) Report 2026 by ACTO Technologies, 64% of life sciences organizations are currently establishing or developing formal AI governance processes. The research, which draws on roundtables and surveys of senior leaders across North America and Europe, reveals an industry aggressively moving to harness agentic AI for commercial, medical, and patient-facing work. Yet, as companies race to deploy these systems, a stark reality has emerged: 58% of executives believe the industry still lacks clear, agreed-upon standards for responsible AI.

This gap between technological capability and operational trust is reshaping how pharmaceutical, biotech, and medical device companies approach digital transformation, forcing a pivot from rapid adoption to rigorous oversight.

The Shadow AI Dilemma

Beneath the surface of corporate AI strategies lies a complex psychological and organizational disconnect. The RAIL Report highlights a striking paradox among life sciences executives: while 69% report high personal comfort using AI, 54% express deep concern regarding how the technology is being utilized across their own organizations.

This apprehension is largely driven by the proliferation of "shadow AI"—the unauthorized use of artificial intelligence tools by employees outside of sanctioned corporate channels. Nearly a quarter of organizations (23%) admit they do not monitor or even discuss employee usage of unauthorized AI agents. Roundtable discussions revealed that employees are increasingly constructing their own custom agents to streamline daily tasks, operating entirely outside the purview of legal, compliance, and IT departments.

In a highly regulated industry that answers directly to patients, this unmonitored usage presents severe liabilities. Autonomous agents generating content or making recommendations without oversight risk violating stringent regulatory guidelines, such as those governing off-label promotion or patient data privacy. Industry analysts note that without a centralized, monitored infrastructure, companies lose control over the accuracy and ethical alignment of the information being disseminated, creating vulnerabilities that could lead to significant reputational damage and compliance breaches.

Life sciences leaders are acutely aware of these stakes. When asked to define responsible AI, executives prioritized validation (81%), ethics (79%), and safety (75%)—principles that reflect the foundational tenets of healthcare delivery rather than mere software efficiency.

Passing the Blame in an Accountability Vacuum

As organizations attempt to rein in shadow AI and deploy official agentic systems, they are colliding with a high-stakes accountability vacuum. When an autonomous AI agent makes a commercial or clinical decision, who is ultimately responsible for the outcome?

The industry remains deeply divided on the answer. According to the data, leaders are split on who owns an AI agent's actions: 34% point to the end user, 28% to legal and compliance teams, 22% to the third-party AI vendor, and 16% to the IT department. This fragmented ownership is paralyzing deployment efforts. In fact, executives cite regulatory, legal, and compliance hesitation as the single biggest internal barrier to scaling AI (21%), while technical readiness ranks near the bottom (9%).

"The discussions reflected a wide range of perspectives and experiences related to AI understanding, adoption, and oversight across the industry," said Lisa DeMarco, general counsel and chief business officer at Arctic Therapeutics and a moderator at the RAIL roundtables. "An important theme in the conversations was the need for practical guidance to help organizations assess AI trustworthiness while maintaining a focus on patient safety."

This hesitation aligns with broader regulatory trends. While specific guidelines from the FDA and the European Medicines Agency (EMA) regarding autonomous agentic AI in commercial functions are still evolving, the underlying principles of existing frameworks—such as the FDA's Digital Health Center of Excellence guidelines on software as a medical device—emphasize rigorous validation and human oversight. Recent independent consulting studies, such as the ZS 2027 CDIO report, corroborate this caution, noting that while AI adoption is surging in pharma manufacturing and supply chains, critical decisions still mandate human sign-off.

In the commercial and medical affairs arenas, this translates to a persistent demand for a "human-in-the-loop." Organizations are discovering that governance cannot be a one-time IT approval; it requires an ongoing operating model with named business owners, clear standard operating procedures, and the technical ability to instantly revoke an AI agent's access.

Standardizing Trust: The RAIL Score

The life sciences industry is rapidly transitioning its focus from where to start with AI to what to build next. Most organizations have initiated AI projects in lower-risk, easier-to-govern areas such as review and approval cycles, pre-call preparation, and insight logging. However, as attention turns to complex tasks like chaining multiple agents together to support full product launches, the demand for standardized evaluation metrics has become critical.

Currently, executives prioritize trust and adoption (24%) over efficiency (23%) and ROI (18%) when judging the success of an AI pilot. Furthermore, four in five leaders (79%) agree that AI risk and returns should be measured against today's human-led baseline, complete with its own inherent error rates.

To address the 58% of executives who cite a lack of industry standards, ACTO—a technology provider recognized by Everest Group for its AI investments in life sciences—has introduced the RAIL Score. The framework offers an 18-point rubric designed to grade agentic AI systems across six core components: Governance, Security, Testing & Certification, Human Centricity, Value Creation, and Orchestration.

Crucially, the rubric treats Security and Governance as hard floors; a low score in either category renders the entire system unfit for use in a regulated environment. While the RAIL Score is a vendor-initiated framework, it directly targets the industry's most pressing pain point: the need to translate subjective vendor claims into objective, evidence-based trust.

"The industry has moved past whether to use agentic AI. Leaders now want to know which systems they can trust enough to scale, and today there's no shared bar to answer that," said Parth Khanna, co-founder and CEO of ACTO. "Any agentic AI system has to get security and governance right. The RAIL Score gives teams a consistent way to grade systems against the standard a regulated industry demands, so trust can rest on evidence instead of vendor claims."

As the industry navigates this transformation, workforce redesign remains a looming bottleneck. Executives acknowledge that to successfully scale AI, organizations must meticulously redraw job descriptions, separating automated tasks from the deeply human skills required for the business. When asked which capabilities humans most need to retain, leaders overwhelmingly pointed to judgment (45%), human connection (31%), and strategic execution (24%).

The findings of the RAIL Report 2026 make one thing abundantly clear: the future of AI in the life sciences will not be dictated by the sophistication of the algorithms, but by the robustness of the governance structures that surround them. As companies prepare to chain complex autonomous agents across global supply chains and commercial networks, establishing an evidence-based foundation of trust is no longer just a compliance requirement—it is the definitive competitive advantage.