A few weeks ago, I was reading an article discussing how artificial intelligence is changing software development. The article was not really about technology. It was about talent.
The authors described a concern that had never occurred to me before. As AI becomes increasingly capable of writing code, some companies are hiring fewer junior developers. From a productivity standpoint, the logic is difficult to challenge. If technology can perform part of the work faster and at lower cost, organizations need fewer people to do it. What caught my attention was not the technology itself, but the implication of that decision. For decades, junior developers learned their profession by doing the work. They wrote code, made mistakes, fixed problems, received feedback, and gradually acquired the experience required to take on more complex responsibilities. If much of that work is now performed by AI, where will the next generation of experts come from?
The more I thought about it, the more I wondered whether Medical Affairs could eventually face a similar challenge.
Most medical leaders did not build their careers in strategic planning meetings. They developed their capabilities through immersion in the science. They reviewed publications, responded to medical information requests, attended congresses, supported advisory boards, prepared presentations, and spent countless hours trying to understand the evidence behind a disease area. Much of that work was repetitive and time-consuming, but it exposed people to scientific debate, conflicting interpretations, unanswered questions, and the uncertainty that accompanies almost every important medical decision.
That is one reason why I am not convinced the most important question surrounding AI in Medical Affairs is whether it will replace people. In most pharmaceutical companies, competitive advantage rarely comes from having access to information. Competitors attend the same congresses, read the same journals, review the same clinical trials, and monitor the same treatment guidelines. Two companies can look at exactly the same evidence and reach very different conclusions. The difference often lies in experience, context, and the ability to recognize what deserves attention and what does not.
At the same time, many of the activities that historically helped professionals develop those capabilities are becoming dramatically more efficient. Literature reviews that once required hours can be completed in minutes. Scientific summaries can be generated almost instantly, and information that previously required considerable effort to gather and organize is becoming increasingly accessible. From an operational perspective, this is unquestionably a positive development, and few organizations would voluntarily choose to return to slower and less efficient ways of working.
The question that stayed with me after reading the article was how future Medical Affairs leaders will acquire the same depth of experience if technology increasingly takes over some of the activities that traditionally served as part of that learning process. Organizations may become significantly more efficient, but they may also need to become much more deliberate about mentorship, scientific discussion, and creating opportunities for younger professionals to develop the judgment that comes from exposure to real-world decisions rather than simply access to information.
So, if you are an MSL worried that AI will replace you, you can probably relax. The day an algorithm can walk into a hospital, navigate a fifteen-minute conversation that turns into an hour, identify the one comment that nobody planned to make, understand why it matters, and then explain its implications to five different internal stakeholders, we will all have much bigger things to worry about.
