Unlocking the human + AI agent workforce in life sciences

As AI agents become commonplace in life sciences, leaders will face a host of new questions and considerations.

The next breakthrough in the life sciences industry may not be a drug, but rather a workforce model.

Agentic AI—autonomous systems that can plan, reason and act on a user’s behalf—are poised to unlock steep efficiency gains for life sciences organizations across the business and throughout the drug development lifecycle.

But as AI agents become more embedded in day-to-day operations, an interesting set of questions may emerge:

Should agents be part of the org chart?
Can you fire an agent for noncompliance?
Should AI agents receive some form of compensation tied to performance?
Are they included in team-building efforts?
If something goes wrong, who do you report issues to?

While these questions are abstract hypotheticals at this point, they offer a window into the kinds of issues organizations will need to consider as they train, manage, evaluate and integrate this new workforce segment.

Source: Cognizant and Oxford Economics
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IT is the new HR: How agentic AI will reshape organizational structure and culture

Earlier this year, NVIDIA's CEO Jensen Huang made headlines when he said: “The IT department of every company is going to be the HR department of AI agents in the future.”

One of the first companies to put that idea into action was Moderna. In May, the pharma giant announced the merging of its technology and human resources functions to more effectively coordinate traditional human workers and advanced technologies. At the helm, in the newly created role of chief people and digital technology officer, is Tracey Franklin, the company’s former HR lead.

Moderna’s early move underscores the idea that AI agents are emerging as a distinct segment of the workforce that must be onboarded, trained, integrated and evaluated just like any other employee group. Whether responsibility for this falls to existing IT or HR teams, or to a hybrid “AI Resources” group, is debatable, but the need for oversight is undeniable.

Take, for example, a Compliance Agent. In the early stages of deployment, this AI tool might assist a human compliance manager by automating repetitive tasks like monitoring regulatory updates, flagging documentation inconsistencies and generating audit reports. As the system matures, the AI agent could handle more complex responsibilities, such as conducting initial risk assessments, cross-referencing evolving global regulations and drafting compliance responses.

But who is overseeing both the tasks managed by the agent and the agent’s evolution? Who ensures the agent is trained on the right standards and protocols? Who defines the ethical boundaries, validates the output and steps in when course correction is needed?

These issues are not the musings of a futurist, but in fact the operational imperatives that business leaders need to address.

Rethinking rules and norms: 5 key considerations for a hybrid workforce

While life sciences organizations needn’t necessarily take the drastic step of merging two major business functions, the most effective approach will likely be a blended model that incorporates HR, IT and functional leaders. This joint team will provide the digital acumen, domain expertise, organizational awareness and human touch needed to facilitate accurate output and maintain high standards of quality, while also ensuring that AI agents are dealt with consistently within the IT architecture and organizational structure.

To navigate the shift, organizations should consider five key areas of focus:

1. Culture and team dynamics. AI integration isn’t just a technical challenge—it’s a cultural one. IT, HR and functional leaders must work together to create a cohesive, collaborative environment that unites human and AI agents. That means revisiting management practices, team structures and workplace norms to accommodate new types of contributors and acknowledge the impact they will have on traditional employees.
   
   
2. AI workforce support. From onboarding and offboarding to training and upskilling, AI agents require a new kind of workforce support. Companies must create a dedicated team that will think through these processes, as well as underlying policies on digital compensation (e.g., computing resources as performance incentives), ethical guidelines, and continuous model refinement and development.
   
   
3. Governance and oversight. AI governance can’t simply mimic existing human frameworks. Organizations will need to develop layered oversight models that account for both human and non-human actors and maintain strong compliance. One crucial challenge is finding cost-effective ways to manage this oversight without offsetting the efficiency gains AI is meant to deliver.
   
   
4. Workforce evolution and organizational structure. The rise of agentic AI is introducing “agent workers” in many organizations. At the same time, it is prompting the emergence of new roles, including Chief AI Officers, human-AI interface specialists and AI team leads. Updating the organizational structure, as well as human job roles and responsibilities, to reflect these changes will be essential to ensuring the organization works efficiently and effectively.
   
   
5. Human-centric strategy and clear communication. As life sciences organizations introduce agentic AI, it’s essential for leaders to communicate transparently with employees about their plans and how these changes will affect some roles. While the impact may vary—from augmenting some jobs to repositioning others—our research reveals it’s unlikely that AI agents will outright replace humans in the near term. The most successful organizations will communicate the overall value of these tools to the business, to patients, to the health system and to human employees.

Embracing the opportunity of agentic AI in life sciences

The life sciences industry is entering a new era—one where the workforce isn’t just human. Powered by an emerging ecosystem of intelligent AI agents, this “digital talent collective” is poised to transform everything from discovery to commercialization.

But capturing the full value of this transformation will require more than technology alone. Success depends on how effectively organizations can govern, scale, train and integrate these AI workforces, embedding them responsibly into the fabric of their operations as trusted, tireless partners in innovation.

With technology advancing rapidly, the very real question facing life sciences organizations now is: Who—or what—will your next hire be?