Interview with Dr. Dennis Janning, Head of Life Science Services & Medtech, on the Transformative Power of Generative AI in the Pharmaceutical Industry

Dr. Janning, how do you assess the long-term potential of Generative AI (GenAI) for the
pharmaceutical industry?

Generative AI has the potential to fundamentally transform the pharmaceutical industry—and at a speed far surpassing previous technological advancements. However, its transformative power lies not in the technology itself but in the radically changed processes it enables. Similar to how the internet revolutionized the way we share information and conduct business, GenAI will redefine value chains in the pharmaceutical sector.

The development of GenAI follows a typical technology adoption curve. After a technological breakthrough, many applications enter the market, often before the technology is fully mature, leading to overly optimistic expectations. Only after the hype subsides and the “trough of disillusionment” is overcome does the true potential of the technology reveal itself through practical applications.

The industry now faces the challenge of integrating GenAI responsibly while considering its stringent regulatory and process frameworks.

What does this mean for companies, particularly given the strict regulatory requirements in the pharmaceutical industry?

Companies need to evaluate the use of GenAI from both a regulatory and efficiency perspective. The pharmaceutical industry’s regulatory requirements, while demanding, also provide a clear framework for innovation. Companies must identify where GenAI can deliver genuine value—either by disruptively replacing traditional methods or by incrementally improving already efficient processes.

There are already examples of promising technologies, like blockchain, that have not been widely implemented in the pharmaceutical sector. Although blockchain could theoretically enhance transparency and security in pharmaceutical supply chains, the existing processes are often well-established and sufficiently efficient, making the added value negligible relative to the effort required.

GenAI should therefore not be implemented for its own sake but only where it offers clear advantages over existing solutions.

How can companies identify the right use cases for GenAI?

The key is to think in terms of processes. Where precise inputs and outputs are required, predictive models or traditional IT systems are more appropriate. Companies should focus on areas where unstructured data plays a role and where creative solutions are needed. GenAI excels at extracting new insights from large, complex information streams and improving process efficiency.

For example, GenAI can analyze patient feedback and clinical studies, identifying patterns that would be difficult to detect manually. This is particularly relevant in areas requiring substantial manual effort to process texts, studies, or reports—where GenAI can significantly reduce time and improve efficiency.

Another promising area is data-intensive fields such as drug discovery or clinical study analysis, where GenAI processes vast amounts of data to recognize patterns and uncover new scientific insights. By combining structured and unstructured data, such as side effects and clinical outcomes, GenAI can accelerate results.

Conversely, GenAI is less useful in environments that require precise and structured data processing with standardized processes, such as production or logistics.

Can you provide examples of where GenAI will have the greatest impact along the pharmaceutical value chain?

I see two key areas where GenAI is already driving significant progress:

Research and Development: GenAI can analyze, summarize, and synthesize large volumes of scientific studies. This accelerates information processing and, as a result, the discovery and development of new active substances.

Commercial Operations: GenAI unlocks new opportunities for personalizing customer interactions and automating processes. One example is the “Compliant Content GenAIrator,” a solution we developed with AWS to address bottlenecks in the MLR (Medical, Legal, Regulatory) review process.

GenAI allows marketing teams to create content quickly, which undergoes an initial MLR review, significantly accelerating the process from idea to approval. Content creation, which previously took 1-3 weeks, is reduced to 1-3 days. Simultaneously, pre-screening by GenAI helps avoid errors, reducing the number of MLR review iterations and shortening overall process times.

The result is not only faster approvals and publications but also significant cost savings due to fewer manual review cycles. This solution demonstrates how GenAI achieves dual benefits: it accelerates creative processes while ensuring compliance with strict regulatory requirements.

What role does the human element play in this new era of Generative AI?

Humans remain the central architects and curators of GenAI applications. GenAI operates on a probabilistic basis, meaning its results are not always accurate or unambiguous. In highly regulated environments, such as the GxP sector, the “Human in the Loop” remains essential to ensure the quality and accuracy of results. AI will not replace humans but will augment their capabilities and improve efficiency. Collaboration with AI will become central, but we must not relinquish control.

Responsible use of GenAI also requires addressing ethical and data privacy considerations to ensure the integrity of AI. This is particularly critical when handling sensitive patient data. Companies must establish strict ethical standards and governance mechanisms to ensure transparency, traceability, and compliance with regulations such as GDPR and the forthcoming EU AI Act.

How can companies prepare their employees for these changes?

By fostering a culture of continuous learning and creating the right framework. Companies should establish safe test environments where employees can gain hands-on experience with the technology without risking sensitive data or regulatory compliance.

At the same time, they must implement training programs that combine technological skills with ethical and regulatory aspects. To maintain control over AI outcomes, employees need not only technological expertise but also a deep understanding of GenAI’s limitations and risks.

Finally, what is your vision for the future of the pharmaceutical industry with GenAI?

My vision is a pharmaceutical industry that becomes more efficient, agile, and patient-centric through the intelligent use of GenAI. I believe we are entering a new era of medicine. GenAI will enable us to develop personalized therapies, detect diseases earlier, and treat them more effectively. The boundaries between research, development, and application will blur, making the entire pharmaceutical value chain more efficient and flexible.