Navigating the Challenges of Fragmented AI Tools in Medical Affairs

As artificial intelligence (AI) technologies increasingly infiltrate various sectors, medical affairs teams within Fortune 500 companies are facing unique challenges related to the proliferation of these tools. According to insights from Gartner, by the year 2028, global enterprises will be employing over 150,000 AI agents— a significant increase from fewer than 15 in 2025. While this growth offers enhanced capabilities, it also brings to light the issues of fragmented systems, especially concerning governance, traceability, and accountability in scientific work.

AINGENS' CEO, Ome Ogbru, PharmD, emphasizes that when medical affairs processes such as literature search, drafting, citation management, and review are conducted using separate platforms, it can lead to an increase in the “tool-switching tax,” which ultimately heightens cognitive load for users and complicates governance of research work. Without a cohesive approach to AI deployment and management, teams may struggle to maintain a clear record of how scientific content is developed, which can pose problems during audits and reviews.

The Tool-Switching Tax

“Users need to have accountability for the results. In order to have that accountability, you need to know how the output was developed,” states Ogbru. The fragmentation of AI tools results in a scenario where processes are disjointed, making it difficult to trace scientific methodologies across different platforms. For instance, searching for literature may occur in one system, drafting in another, and citation management in a third. This disconnection could lead to significant hurdles when the time comes to provide a detailed account of the workflow.

In fact, only 13% of organizations reportedly have the appropriate governance strategies for AI agents, a statistic that highlights the urgency of addressing these gaps. Ogbru points out that simply implementing multiple tools without a cohesive strategy can easily overwhelm personnel, who must adapt to various interfaces and procedures. “If the cognitive load is too great, people just don’t use it,” he cautions.

AINGENS’ Response: The Medical Affairs Content Generator (MACg)

To combat these challenges, AINGENS has introduced the Medical Affairs Content Generator (MACg), designed to unify critical stages of scientific content development. By providing a single platform where teams can handle literature searching, evidence gathering, drafting, and citation management, MACg streamlines the workflow significantly. This integration helps reduce the repetitive task of switching back and forth between platforms, saving both time and effort for medical affairs teams.

The unified nature of MACg also addresses the traceability of scientific content. The ability to quickly reference history and sources linked to content diminishes the burden of reconstructing the workflow during audits. Ogbru highlights that connecting various stages of work can enhance accountability, allowing teams to easily track development processes, source citations, and reviews.

Moreover, MACg’s architecture is geared towards minimizing repeated handoffs—a common issue in fragmented systems—thereby enhancing workflow efficiency.

Governance in an Expanding AI Landscape

As the number of AI tools grows, so too does the complexity of governance. Different platforms can bring about varying capabilities and workflows, meaning that a one-size-fits-all governance approach is inadequate. Ogbru notes that “There’s a general overall approach, and then there’s a tool-specific approach,” highlighting the need for detailed governance structures tailored to specific use cases.

For teams dealing with regulated content, it is imperative to establish clear records of sources, citations, and reviews before the adoption of AI technologies becomes widespread. Ogbru advises leaders to incorporate governance considerations during the evaluation stage of technology adoption, ensuring that policies and procedures are established before any platform becomes widely implemented.

“It’s essential to test the system in line with the intended workflow, making sure it can be trusted for the processes it will oversee,” he recommends.

Conclusion

As medical affairs teams navigate the integration of AI in their operations, the importance of cohesive tools and sound governance cannot be overstated. With platforms like AINGENS’ MACg, organizations can enhance the traceability and accountability of their scientific content, paving the way for more efficient, effective medical affairs workflows. As the landscape continues to evolve, ensuring that governance remains a core focus will be vital for success in the AI-driven future.