How AI Is Transforming Scientific Intelligence, Stakeholder Engagement and Strategic Decision-Making

Medical Affairs professionals have been the scientific bridge between pharmaceutical companies and the broader healthcare ecosystem for decades, connecting clinical evidence to healthcare professionals, researchers, patients, and internal stakeholders. But the environment in which they work is changing rapidly. There is a proliferation of scientific publications, an increase in real-world data, a personalization of stakeholder expectations, and a pace of emerging new evidence that traditional processes have difficulty accommodating.

Artificial intelligence is an increasingly important part of this transformation. Artificial intelligence is able to do more than automate administrative work. It has the potential to change the way that Medical Affairs teams find information, generate insights, engage stakeholders, and make strategic decisions. Technologies like natural language processing, machine learning, generative AI, and AI agents are creating opportunities to move Medical Affairs from information management to more intelligent, insight-driven operations. The challenge is to make sure this transformation happens responsibly.

 

From AI Hype to Practical Medical Affairs Value

AI is often discussed as one technology, but the landscape is much broader. Machine learning finds patterns in big data, natural language processing understands human language, generative AI is able to make and summarize content, and new AI agents can coordinate multiple tasks in a workflow. The fundamental question for Medical Affairs is not, What can AI do?, but rather, Where will AI add meaningful value, without jeopardizing scientific integrity?

The pharma industry is already heading in this direction. The U.S. Food and Drug Administration has seen a rapid rise in submissions of drugs and biological products that contain artificial intelligence components. From 2016 to 2023, it had more than 500 submissions with AI components across nonclinical, clinical, post-marketing, and manufacturing activities. "This is a clear sign that AI is no longer in the theoretical realm. It is moving into the real-world development and regulatory environment.

The same pattern is seen in investment by industry. According to research by Deloitte, almost 60% of surveyed life-sciences executives intend to boost investment in generative AI throughout the value chain, signaling a move for organizations from experimentation to implementation. This is an opportunity for Medical Affairs to identify practical applications to solve real business and scientific challenges rather than jumping on the bandwagon because AI is trendy.

 

 

From Information Retrieval to Scientific Intelligence

One of the most promising applications of AI in Medical Affairs is to revolutionize the management of scientific information. Medical teams are faced with a complex, expansive ecosystem of information such as peer-reviewed publications, results from clinical trials, conference abstracts, treatment guidelines, regulatory updates, real-world evidence, and emerging science. Much of this work has traditionally involved searching, reading, categorizing, and manually synthesizing information. AI may assist in moving the process from intelligence retrieval to intelligence generation.

For example, an AI-enabled literature monitoring system can continuously search for relevant publications, categorize them by therapeutic area or scientific topic, summarize key findings, and highlight emerging patterns. Instead of bombarding a Medical Affairs professional with hundreds of articles, the system could help to pinpoint what developments are worth paying attention to. This is not to say that AI replaces the judgement of science. Instead, it can reduce repetitive processing of information, allowing experts to spend more time interpreting evidence.

This is an important point. The value of Medical Affairs isnt just in knowing what a publication says. This is about understanding what the evidence means, how robust it is, how it compares with existing knowledge, and what implications it may have for patients, healthcare professionals and the organization. PwC has identified AI-assisted scientific literature search and document summarization and medical strategy analysis among emerging capabilities in connected Medical Affairs models.

 

Generative AI and the New Medical Communications Landscape

Theres another great opportunity with Generative AI that opens the door for us to interact with complex information in natural language. Well-governed artificial intelligence systems could be used by Medical Affairs professionals to summarize scientific publications, compare documents, organize evidence, prepare initial drafts, or retrieve information from approved internal knowledge bases.

The potential gains in productivity can be large. Deloitte notes that medical writing could see a reduction of around 20% to 30% in medical writer effort in relevant activities, thanks to AI-driven automation, which could save large biopharmaceutical organizations a substantial amount. Medical Affairs is not a place for speed over accuracy.

Generative artificial intelligence systems can generate information that is believable but false, sometimes referred to as hallucinations. They also may misinterpret scientific context, inappropriately mix findings, or report an unpredictability with too much confidence. Thats why medical content generated by AI needs to be properly reviewed and verified by humans. So, the best model is not AI instead of experts. Its AI, with specialists. Medical Affairs professionals are responsible for evaluating whether information is scientifically accurate, clinically relevant, appropriately contextualized, and appropriate for its intended audience.

 

Reimagining KOL Mapping and Stakeholder Engagement

Stakeholder engagement is another area of Medical Affairs that could be significantly affected by AI. Traditionally, key opinion leader (KOL) identification has been based on publications, clinical expertise, congress participation and professional networks. However, influence in a therapeutic area is increasingly multi-dimensional. AI is able to scan huge amounts of structured and unstructured data to help spot rising experts, chart scientific networks and gauge areas of influence.

AI-assisted KOL mapping helps to find researchers who are gaining prominence in a specific scientific niche, even if they are not yet among the most cited experts. Network analysis also helps to identify relationships between researchers, institutions, and areas of scientific interest. This makes for a more dynamic approach to stakeholder strategy.

Instead of asking simply, Who are the top experts today? Medical Affairs teams are increasingly able to explore questions such as:

 

• Who is leading the scientific discussion?
• Who are the rising stars in a specific field?
• What topics are of the greatest interest?
• What is missing in the evidence?
• Which stakeholder perspectives are likely missing?

 

IQVIA has pointed to AI-assisted KOL mapping as an example of a practical application that can visualize expert networks, identify emerging voices, and help with more strategic engagement.

But the goal should not be to automate relationships. Fundamentally, medical engagement is human. Artificial intelligence can help find patterns and prep insights, but trust, scientific dialogue and professional relationships still rely on human interaction.

 

AI Agents: Moving From Individual Tasks to Intelligent Workflows

The next evolution could come from AI agents. In traditional generative AI, the AI generally responds to single prompts. AI agents might be able to perform chains of related tasks, using information and tools to reach a given objective.

Imagine a Medical Affairs workflow where an AI agent tracks newly published literature, identifies relevant developments, compares them to an internal evidence base, categorizes the findings, and prepares a structured briefing for expert review.

 

 

Another workflow could be to analyze insights from healthcare professionals, identify themes that are common, and prepare an initial summary of insights for the Medical Affairs team. These applications are beginning to move from the conceptual level to practical testing. IQVIA points to agentic AI as an emerging opportunity to simplify literature review and accelerate insight generation.
Deloitte has also pointed to multi-agent approaches for real-world evidence, where conversational AI may assist teams in moving faster from a research question to relevant evidence and analysis. Agentic AI is important because it could change the unit of automation from a single task to a whole workflow.

 

From AI Experimentation to Strategic Implementation

There are opportunities, but not all Medical Affairs activities are good candidates for AI. Organizations need a structured way of deciding where to invest.

A practical framework for AI prioritization can consist of four key dimensions:

 

Impact: What scientific, operational or strategic value could the initiative deliver?

Feasibility: Are the data, technology and capabilities of the organization able to enable implementation?

Risk: What are the potential scientific, regulatory, privacy, security, or reputational impacts?

Scalability: Is the solution scalable to other teams, markets or therapeutic areas?

 

With this approach, Medical Affairs experts can avoid the common mistake of selecting AI projects based on novelty alone. For example, a simple literature summarization workflow may be a lower risk starting point than deploying AI to make decisions with direct downstream impact on regulatory or clinical conclusions. The FDAs current approach further emphasizes the significance of the context of use definition and credibility assessment of AI models based on the risk of their intended use.

 

 

Trust, Governance and Responsible AI

Ultimately, trust may be the biggest barrier to AI adoption in Medical Affairs. Medical Affairs teams work with highly sensitive information, including unpublished research, clinical data and commercially confidential material. Generative AI is also linked to risks of data leakage, privacy, hallucinations, bias, intellectual property, and inappropriate use. This necessitates governance.

The FDA and European Medicines Agency have come up with guiding principles for good AI practice in drug development that highlight human-centric design, risk-based approaches, clear context of use, multidisciplinary expertise, data governance, performance assessment, and lifecycle management. These principles are very relevant to Medical Affairs.

Organizations need clear policies on which AI tools can be used, what information can be entered into them, how outputs should be validated, and when human approval is needed. The aim should not be to smother innovation. It should be to set the conditions where you can have faith in innovation.

 

The Medical Affairs Professional of the Future

Medical Affairs expertise will not be replaced by AI. Rather, it will probably alter the character of expertise. With AI taking on repetitive information processing tasks, professionals may spend more time on activities such as scientific interpretation, critical thinking, strategic planning, and relationship building.

Future Medical Affairs professionals might need a mix of capabilities:

• Solid scientific and clinical knowledge
• Critically evaluate information generated by AI
• AI and data literacy
• Strategic insights generation
• Ability to engage with interested parties
• Knowledge of AI governance and ethics
• Proven ability to work with medical, technology and data teams

 

In this environment, competitive advantage will not necessarily be with the organizations with the most AI tools. It may be the domain of organizations that bring together the right technology and the right human expertise.

 

 

Building the Next Generation of Medical Affairs

AI is already transforming Medical Affairs. AI is creating opportunities to make the function more connected, responsive and insight-driven, from scientific literature monitoring and knowledge management to KOL mapping, medical communications, real-world evidence and intelligent workflows. But the goal should never be technology for technologys sake.

The real opportunity is in providing Medical Affairs professionals with greater access to evidence, more time for meaningful scientific engagement and more powerful tools to turn complex information into actionable insight.

The scale of this transition is already visible in the pharmaceutical industry. FDA experience shows AI is increasingly becoming embedded in the drug development lifecycle, and industry research indicates increasing investment and experimentation across life sciences. So, the next generation of Medical Affairs wont be defined simply by whether organizations adopt AI. It will be measured by how smartly, responsibly and strategically they use it.

AI is able to process information at a scale that is phenomenal. But Medical Affairs offers something equally important: scientific judgement, human relationships, context and trust. Bringing those together could change Medical Affairs from a function that is managing rising volumes of information to one that is actively converting information into scientific intelligence, improved stakeholder engagement and better strategic decisions.

By Sasly Ahmeth, Social Media Executive, GLC Europe, Colombo Office, Sri Lanka.