Peter S. Kaplan: Humanizing AI in Pharma Transformation

The Human Element in AI-Driven Life Sciences

Artificial intelligence is transforming the life sciences, influencing everything from molecular research to market strategies. However, the most significant change isn't just in the technology itself but in how people engage with it. Peter S. Kaplan highlights that while machine learning has revolutionized discovery and development, the industry's long-term success depends on investing in human capabilities alongside technological advancements. “AI has proven it can scale science,” Kaplan states. “The harder question now is whether our organizations can scale learning, judgment, and collaboration at the same pace.”

From Scientific Breakthroughs to Organizational Change

AI’s impact in laboratories is already well-established. Algorithms can analyze extensive molecular datasets, predict binding affinities, and identify new therapeutic targets at a scale no single scientist could achieve. In 2020, Google DeepMind's AlphaFold system demonstrated the ability to accurately predict three-dimensional protein structures, sparking a wave of progress across biology and drug discovery.

This momentum is extending into development. Companies like Formation Bio, designed as AI-native organizations, are reducing trial costs and timelines while changing how teams operate. David Steinberg, Chief Business Officer, describes this shift as converting human bottlenecks into automated workflows, with AI completing tasks in minutes that once took months. While these gains are real, Kaplan believes they are incomplete without parallel investment in people. “Technology can compress timelines,” he says, “but it doesn’t automatically change how teams think, make decisions, or work together. That part still requires intention.”

Agentic AI Raises the Stakes for Human Judgment

The next phase of transformation is already underway with the emergence of agentic AI systems. These tools move beyond prediction and recommendation to action, autonomously redesigning clinical trials, rerouting manufacturing schedules, or cleaning regulatory data. As capability expands, Kaplan believes leadership responsibility grows, not shrinks. “The more autonomy we give machines,” he says, “the more important it becomes that humans understand the context, the trade-offs, and the consequences of what those systems are doing.”

This shift places renewed emphasis on cross-disciplinary collaboration. Paulo Fontoura, Chief Medical Officer at Xaira Therapeutics, has highlighted that AI engineers often lack deep biological or clinical expertise, while medical specialists may not be fluent in advanced computation. Expecting one person to master all domains is unrealistic.

Kaplan agrees. “The future isn’t about unicorn talent,” he says. “It’s about teams that can translate across disciplines and learn continuously together.”

What AI Cannot Replace in Healthcare

Even as AI takes on more technical work, healthcare’s human core remains irreplaceable. Empathy, ethical judgment, and sensitivity to patient needs cannot be automated. Fontoura has noted that understanding what patients want and safeguarding human well-being remains fundamental to pharma, regardless of how advanced the technology becomes.

Kaplan frames this as a defining boundary. “AI can optimize processes,” he says, “but it can’t replace moral responsibility. In healthcare, that responsibility always rests with people.”

Culture and Hiring as Strategic Levers

As roles evolve, hiring and culture become critical control points. Luba Greenwood, CEO of Gallop Oncology, has emphasized the need for careful human evaluation in talent decisions, including her “three-time rule” of multiple interviews to uncover authenticity beyond rehearsed answers. AI can assist with screening, but humans still assess motivation, integrity, and values. At Formation Bio, hiring reflects a similar philosophy. The company seeks entrepreneurial scientists who thrive in ambiguity, ask fundamental questions, and connect deeply to mission. Emily Gransky, Vice President of Talent, has described curiosity, courage, and adaptability as essential traits in AI-powered pharma organizations. Kaplan sees culture as a competitive differentiator. “The companies that pull ahead won’t just have better models,” he says. “They’ll have cultures where people feel empowered to question outputs, challenge assumptions, and learn faster than the technology changes.”

A Human-Centered Future for AI in Pharma

In a rapidly evolving industry, Kaplan argues that success will not be measured solely by technological mastery. It will be defined by an organization’s ability to blend technical excellence with emotional intelligence and ethical judgment. “The goal isn’t to make pharma more automated,” he says. “It’s to make it more effective, more responsible, and more human, with AI as an amplifier rather than a replacement.”

As AI takes on scale, complexity, and volatility, humans will continue to supply creativity, empathy, and the courage to rethink what is possible. The leaders who succeed will be those who integrate intelligent systems with human purpose, transforming pharma into a field driven not just by data, but by human ingenuity.