Mark Sullivan, President of Regulated Industries at Salesforce, shares insights on AI adoption in life sciences. He discusses the shift from experimentation to tangible impacts, emphasizing the importance of AI for augmenting rather than replacing the workforce. Sullivan highlights challenges with inaccurate agents and the need for high-quality data. He illustrates how agents can enhance patient engagement and streamline clinical trials while stressing the necessity of trust and governance in deploying these technologies effectively.
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insights INSIGHT
AI Proficiency Now A Baseline
The industry has reached an AI inflection point where pilots separate leaders from laggards.
Professionals who don't learn AI will be compromised in future workflows.
volunteer_activism ADVICE
Ground Agents In Trusted Enterprise Data
Ground agents in enterprise and unstructured data, not only generic LLM outputs.
Build a trust layer that screens for hallucinations and toxicity before deployment.
insights INSIGHT
Best Use Cases: Patient Triage And Trial Matching
Agents excel at routine patient tasks like triage, provider finding, messaging, and trial matching.
These use cases are becoming more accurate and are starting to deliver real customer value.
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As artificial intelligence continues to dominate conversations across life sciences, many companies are reaching an inflection point between experimentation and real-world impact. In a recent episode of The Top Line, Mark Sullivan, president of regulated industries at Salesforce, said the industry is moving past broad promises toward a clearer divide between organizations that have laid the groundwork for AI and those still struggling to operationalize it. While early pilots have delivered mixed results, Sullivan said the next phase will be defined by how effectively companies use AI to augment their workforce, not replace it. Much like laptops or smartphones, AI proficiency is quickly becoming a baseline expectation for professionals, shaping productivity, decision-making and resilience across drug development, manufacturing and commercialization.
Sullivan emphasized that success with AI agents hinges on trust, governance and high-quality data—particularly in an industry where accuracy and compliance are nonnegotiable. Life sciences organizations are beginning to see value in agents that support patient engagement, clinical trial matching and commercial execution, but only when those agents are grounded in enterprise data and clear guardrails. With as much as 80% of industry data unstructured, he said, the process of deploying agents often exposes deeper data and architecture challenges that must be addressed. Companies that solve for these issues can unlock both efficiency and growth, using AI not just to cut costs, but to strengthen patient relationships and drive innovation. The conversation offers a practical look at how life sciences leaders can move beyond the hype and build an agentic strategy that delivers measurable results—making it a must-listen for anyone navigating AI adoption in a regulated environment.
The Top Line