Tom Hughes is the founder and CEO of Virtual Science AI.
2025 is going to be a big year for AI adoption in pharma. By now, most life science companies have at least experimented with one generative AI (GenAI) pilot; according to a 2024 report from Bain, 60% of pharma executives were already moving “beyond ideation and brainstorming to building out use cases,” while 40% had already calculated projected savings from AI tools into their budgets. In a McKinsey study published earlier this year, all respondents reported having piloted GenAI tools, with 32% stating that their focus had shifted to scaling those use cases—moving from experimentation to practical, integrated solutions across various pharma functions.
For the medical communication agencies that support the pharma industry, this evolution represents a turning point. These agencies work closely with the pharma industry to create content and support their efforts in bringing treatments to market. Traditionally, these agencies have relied heavily on human expertise, employing large teams of medical writers to document market research and develop content. However, there is now significant pressure to find efficiencies through AI to reduce costs and speed up processes, as well as to increase quality and streamlining-focused action.
These agencies are now reassessing their business models to stay competitive. They don’t want to be perceived as outdated. Instead, they need to modernize by integrating AI while maintaining the high standards expected in medical communications.
The Importance Of Insight Generation
Insight generation is one area where GenAI is tangibly altering the playing field. A great example of this change comes from the medical affairs arm of medical communications; this team serves as a bridge between pharma companies and the scientific community. Unlike commercial teams, medical affairs teams focus on scientific interactions, aiming to address or discover unmet needs. Here is where Gen AI insights bring higher value.
Medical affairs teams generate vast amounts of data—meeting notes, scientific discussions and research findings. Traditionally, only about 5% of this data is ever analyzed. However, with AI, pharma companies can now systematically process vast amounts of data, not just generating standalone insights, but connecting them across different sources and over time. This enables a more comprehensive, longitudinal understanding, leading to faster, deeper insights and more strategic decision-making.
Typically, in medical affairs, AI systems collect and analyze data related to scientific imperatives for specific disease areas and drugs. This is especially important in the prelaunch and launch phases when a drug is either awaiting regulatory approval or has just been approved. At this stage, pharma companies need to ensure their launch plans are sound and that their strategies are aligned with scientific and market realities. Many drug launches fail, often due to poor planning or speed of adaptation in launch years.
AI-driven insights help teams refine their approaches in real-time, increasing the likelihood of a successful launch for both patients and investors. They can identify new indications for existing treatments or refine strategies to target the right patient groups. Insight generation isn’t one-size-fits-all—it varies at different stages of a drug’s life cycle. But overall, AI is helping medical affairs teams unlock critical scientific and strategic information faster than ever before. It’s hard to overstate how game-changing this is: It means new treatments can be brought to market, and to the patients who need them, much more quickly.
Improving The Odds Of Breakthroughs
Another area where AI is creating a major shift is advisory boards. Traditionally, pharma advisory boards have relied on medical writers to document discussions and compile reports. This process could take up to 30 days before teams had access to this documentation and could generate actionable insights.
Now, with medically trained AI, we can capture and analyze scientific discussions much faster. Unlike general-purpose AI tools, specialized medical AI produces tailored, high-quality reports almost instantly. This allows teams to analyze a much larger volume of data faster, increasing their chances of uncovering valuable insights. Scientific breakthroughs are rare, but by processing more data, AI improves the odds of finding meaningful connections.
Beyond static reports, these AI systems offer dynamic intelligence. They track trends over time, allowing teams to build on existing knowledge rather than repeatedly asking the same questions. This eliminates inefficiencies and ensures that insights evolve in response to new data.
Things get even more interesting when AI aggregates broader datasets. Take medical science liaisons (MSLs), for example. Their job is to engage with top scientists and gather insights from key stakeholders. Until now, much of this information has been stored in CRM systems, making it difficult to analyze. AI can now extract and systematically analyze this data, revealing critical patterns and connections that would otherwise go unnoticed.
That said, human oversight is still crucial. AI-generated insights must be reviewed for accuracy, and pharma companies insist on maintaining high standards. Both pharma companies and AI providers acknowledge the need for human involvement to maintain quality, prevent hallucinations and uphold industry standards. AI needs guardrails to be truly effective.
A Reduction In ‘Dark Data’
AI has also been critical in reducing the amount of “dark data”—valuable data and insights that have been siloed in disparate systems, causing both problems and missed opportunities in our industry.
Many chief medical officers (CMOs) I speak with struggle to extract insights from their MSL teams’ data, despite investing significant amounts in these operations. AI platforms are now helping them unlock that information, leading to smarter decision-making that benefits both companies and patients. AI presents a real opportunity here to transform how pharma companies leverage data for better outcomes.
A Clear Path Forward
The key to successful GenAI implementation across pharma (and, likely, most other industries) is to identify the areas where these solutions can truly add value. This means ensuring the use case is both valuable and feasible. In the near term, customer insight generation is a strong starting point—there are many viable and impactful applications in that space.
It’s also important to take calculated risks, quantify the value through case studies and build clear value propositions to secure broader investment for scaling AI initiatives. Running a pilot is one thing, but scaling the benefits is where the real impact occurs.
Where an organization is in its AI journey matters. If they’re still in the early stages, running a few pilots is a good step—but there must be a clear pathway for scaling. Engaging the right stakeholders and implementing change management is crucial. For those who have already completed pilots, the next step is to quantify the value of scaling, build a strong investment case and secure buy-in to fully realize AI’s potential. Ultimately, AI at scale is where companies can gain a real competitive advantage.
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