The State of Trust & Speed in Life Sciences: Data, AI, & Compliance
Welcome to the Life Sciences Dreamin’ 2026 Video Series!
Immerse yourself in the incredible keynotes, expert panels, and breakout sessions recorded live at LSD 2026. As the only conference dedicated exclusively to the intersection of Salesforce and the life sciences industry, Life Sciences Dreamin’ brings together visionaries, admins, and tech executives to explore how modern technology transforms pharmaceutical, biotech, and medical device organizations.
Kick off the series with our opening keynote delivered by Rosalind Beasley, MBA, PMP®, CSM®, as she tackles one of the most urgent conversations in technology today: The State of Trust and Speed in Life Sciences.
In this engaging talk, Rosalind explores why data cannot remain locked in departmental silos, breaking down the critical overlap between commercial CRMs, quality management systems, safety data, and AI agents. She outlines real-world risks—including a recent FDA finding caused by unverified AI output—and shares practical frameworks for implementing technical guardrails, governance programs, and Salesforce observability tools.
Watch now to discover how establishing data integrity, context, and clear accountability empowers life sciences teams to innovate faster without sacrificing compliance.
Watch now!
VIDEO TRANSCRIPT:
The State of Trust and Speed in Life Sciences
Everything is data. Data can be sourced from anywhere across an organization, meaning that decisions made with data in your specific business unit directly impact other business units—whether you realize it or not.
In a quality management role, data is routinely pulled from across the entire enterprise to make decisions, making data accuracy in source systems critical. A typical quality management setup pulls data from CRM systems, safety systems, claims records, and increasingly, connected Internet of Things (IoT) devices like hospital diagnostic machines or continuous glucose monitors. The sheer volume of data connections is proliferating rapidly.
Oftentimes, organizations face challenges convincing auditors that all these connected systems are under control. Even if a CRM system doesn’t seem like it needs regulatory control on the surface, it depends entirely on whether other parts of the organization are sourcing data from it. As connected environments grow, this complexity will only increase.
Defining Data Trust Across Three Pillars
Trust in data can be viewed through three distinct lenses:
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Internal Trust: Does your team trust its own data?
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Partner Trust: Do business partners trust that you are using their data appropriately?
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Regulatory Trust: Can you prove to regulators that your data is managed within a controlled environment?
Artificial intelligence is projected to be embedded across most organizations within two years, yet only about 19% of organizations currently have formal AI policies or governance programs in place. While many companies attempt to define policies to control AI usage, a policy can only accomplish so much—the human behind it and the technical guardrails in place are what ultimately matter.
How Trust Breaks: Real-World Risks and FDA Findings
Data trust breaks down across organizations in predictable ways, such as duplicate records, AI outputs lacking verified sources, or reliance on “shadow spreadsheets” where teams create their own version of the truth.
However, regulatory consequences are already taking place. The FDA recently issued a finding against an organization that used AI agents to generate product specifications and production records without incorporating a human in the loop to confirm the output. Because there was no process or human verification to prove the data was valid, accountability failed—and accountability always stays with the organization.
The Four Layers of Data & AI Governance
To govern data and AI effectively, organizations must manage four distinct layers:
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Data Integrity: Adhering to GxP regulations, electronic record standards, and electronic signature requirements.
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Privacy and Consent: Complying with regulatory frameworks like GDPR and HIPAA.
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Commercial Conduct: Ensuring promotional reviews, Sunshine Act reporting, and adverse event reporting are handled ethically.
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AI-Specific Standards: Adhering to emerging frameworks like the EU AI Act and the NIST Risk Management Framework.
Governance must follow three core tenets: Know it, own it, and trace it. Organizations must be able to prove that business decisions were made using verified data from traceable sources.
Breaking Down Silos Between Commercial and Quality Teams
Trust can no longer exist within departmental silos. In the past, commercial teams lived in the CRM and quality teams lived in separate systems, with a rigid wall built between them.
When commercial CRM data is integrated directly into quality systems, regulatory authorities will inevitably ask: Who has control over that data, and are proper electronic record regulations being followed? Commercial, Quality, Regulatory, IT, and Security teams must collaborate closely to ensure data integrity across the pipeline.
Context Engineering and the Balance of Speed and Trust
Teams are under immense pressure to move fast, but AI does not fix bad data—it amplifies it. The industry does not have a data storage problem; it has a quality and context problem.
Context engineering is critical when deploying AI agents. For example, if a sales representative is preparing for a meeting with a physician, an AI agent providing advice without context leaves the representative blind. True context requires combining CRM data with quality and safety records—such as identifying an open complaint that has been pending for 90 days, which drastically alters how the meeting should be approached.
To manage this complexity, modern platforms utilize evaluation and observability tools—essentially using AI to evaluate and observe other AI agents for accuracy. While automated evaluation helps manage massive data volumes, human review by subject matter experts (“the human in the loop”) remains essential for maintaining a golden set of baseline data.
Conclusion: Trust as the Ultimate Accelerator
The future of life sciences relies on connecting data, the agents that utilize that data, the signals that trigger those agents, and the actions those agents take.
When clean, connected, and compliant-by-design guardrails are established, trust ceases to be a bottleneck—it becomes the ultimate accelerator that allows organizations to innovate and move fast safely.
