Medical Affairs Teams Pay a “Tool-Switching Tax” for Fragmented AI

As AI tools spread across medical affairs workflows, AINGENS CEO Ome Ogbru warns that disconnected systems can increase cognitive load, weaken traceability, and make regulated scientific work harder to govern, audit, and defend.

COLUMBIA, Md., Sept. 28, 2026 /PRNewswire/ — By 2028, the average global Fortune 500 enterprise will have more than 150,000 artificial intelligence (AI) agents in use, up from fewer than 15 in 2025, according to Gartner. For medical affairs teams, that proliferation can create a governance problem when literature search, drafting, citation management, and review are handled through separate tools.

AINGENS developed its Medical Affairs Content Generator (MACg) to connect major stages of scientific content development within a single environment. Keeping those stages together can make it easier for teams to follow how scientific content was developed and governed. “When you use disconnected platforms, there’s a tax that you’re paying in time and effort,” said Ome Ogbru, PharmD, Founder and CEO of AINGENS, a life sciences AI company focused on governed scientific workflows. “Every time you have to stop and go to another platform, you’re starting all over again.”

Disconnected Tools Break the Scientific Trail

Only 13% of organizations say they have the right AI agent governance in place, according to Gartner. For medical affairs teams, disconnected systems can make it harder to trace how scientific content was developed. A literature search may occur in one tool, drafting in another, and citation management in a third, leaving pieces of the scientific record distributed across platforms.

“If that workflow were ever audited, it would be very cumbersome to show the custody and everything that happened from platform to platform,” Dr. Ogbru said. “When you have everything on one platform that supports the workflow, you can go back and see what was done, what came out of it, how the content was developed, and how the citations were added.”

Fragmentation can also increase the cognitive load on users. Each tool may operate differently, requiring employees to learn separate processes and interfaces. “You have to learn each one of these systems and tools,” Dr. Ogbru said. “If the cognitive load is too great, people just don’t use it.”

Connecting the Workflow Reduces the “Tool-Switching Tax”

Dr. Ogbru distinguishes workflow infrastructure from point solutions that address individual tasks. When major stages of scientific content development are spread across different tools, users must repeatedly transfer information, re-establish context, and keep track of what happened at each stage.

Connecting those stages can help medical affairs teams:

• Preserve evidence continuity: Search history and selected sources can remain connected to the content they support.
• Reduce repeated handoffs: Users spend less time moving information and re-establishing context between systems.
• Support traceability: Teams can more easily reconstruct how content was developed, cited and reviewed.

MACg applies this approach by bringing real-time PubMed search, literature summarization, evidence gathering, drafting, automatic citations, reference management, editing, collaboration, and presentation development into the same workflow. This reduces the orchestration required to move scientific content through multiple applications while preserving a record of the work.

Governance Starts Before AI Adoption Scales

The governance challenge grows as organizations add more AI tools. Different systems can have different capabilities, underlying models, and roles within a scientific workflow, which means a single set of controls may not address every use case. “There’s a general overall approach, and then there’s a tool-specific approach,” Dr. Ogbru said of AI governance. “The capabilities are different, and the workflow is different.”

For regulated content, teams need a clear record of sources, citations, review history, and auditability. “Users need to have accountability for the results,” Dr. Ogbru said. “In order to have that accountability, you need to know how the output was developed.”

He advises medical affairs leaders to address governance during technology evaluation, before a platform reaches broad adoption. Teams should test the system against the intended workflow, establish policies and procedures before expanding its use. “The model alone is not going to do it,” Dr. Ogbru said. “You need to add all these other things around it to support that specific workflow. It’s more than that. The model is just a piece.”

About AINGENS

AINGENS is a life sciences software company transforming how scientific and medical content is created in regulated healthcare environments. Founded by Ome Ogbru, PharmD, with more than 20 years of experience in pharma and biotech, the company combines deep life sciences expertise with advanced technologies to build integrated AI-powered platforms that streamline some of the most time-consuming steps in scientific, clinical, and medical workflows.

Its flagship platform, MACg (Medical Affairs Content Generator), is an end-to-end, evidence-based workspace that integrates real-time PubMed search, document-grounded reasoning, automated citation generation, drafting, slide generation and collaboration in a private, secure environment. By embedding traceability and source alignment directly into the workflow, AINGENS helps medical affairs and medical writing teams transform scientific evidence to publication and conference-ready content creation without compromising scientific rigor or regulatory integrity. Learn more at https://macg.ai.

References

• Gartner. (2026, April 28). Gartner identifies six steps to manage AI agent sprawl. gartner.com/en/newsroom/press-releases/2026-04-28-gartner-identifies-six-steps-to-manage-artificial-intelligence-agent-sprawl
• Gartner. (2026, April 2). Gartner expects most enterprises to abandon assistive AI for outcome-focused workflow by 2028. gartner.com/en/newsroom/press-releases/2026-04-02-gartner-expects-most-enterprises-to-abandon-assistive-ai-for-outcome-focused-workflow-by-2028

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SOURCE AINGENS