LLMs excel at linguistic intelligence, but humans uniquely possess multiple intelligences (interpersonal, intrapersonal, spatial) that they compound in real time using sensory input. This allows humans to retain a monopoly on strategy, judgment, and nuanced human connection, which AI cannot replicate on its own.
The initial use of AI in life sciences is a passive copilot, like a smarter search bar. The next leap is to 'agentic AI' which proactively closes knowledge gaps, simulates conversations, and provides real-time visibility. This shift is about preparing teams, not just arming them with information.
Instead of a swarm of disconnected task agents, a safer architecture uses a central "super agent" (Queen Bee) as an orchestrator. This Queen Bee delegates tasks to worker agents, then acts as a quality and compliance checker on their outputs before they are sent to the human user, creating built-in guardrails.
Agentic AI has the potential to dramatically lower the cost of post-market commercialization. This could enable promising molecules from underfunded biotechs to reach patients, breaking the dependency on a CEO's ability to raise massive funding rounds and creating a more equitable path to market for new therapies.
To manage compliance risk in regulated industries, treat AI agents like new employees. Before deployment, the agent must pass the same knowledge assessment a human would take. This quantifies the risk, turning a 'black box' AI into an observable and testable system with a verifiable accuracy score.
When CEOs tell teams to 'figure out AI,' it's not just about task automation. Facing shrinking headcounts and high expectations, they are implicitly asking leaders to define the future of work for their teams and create a new human capital strategy that integrates AI for the new agentic era.