Gen AI for Biopharma: 5 Less Obvious Truths

In the rapidly evolving landscape of biopharma, generative AI (gen AI) is emerging as a game-changer. For companies like Amgen, which have proactively embraced gen AI, the benefits are already materializing, with even greater potential on the horizon. However, for other life sciences companies looking to leverage this technology, it’s crucial to act swiftly and with a different mindset. Here are five core truths that biopharma firms can adopt to create value-driven, scalable gen AI programs and secure a multi-year competitive advantage.

1. Every Successful Gen AI Strategy is Equal Parts Speed and Scale

To gain a competitive edge with gen AI, biopharma companies must focus on both speed and scale. Speed involves rapidly adopting and integrating AI technologies to stay ahead of the competition. Scale refers to the ability to expand these technologies across various functions and processes within the organization.

For example, Amgen’s success can be attributed to its swift adoption of gen AI, enabling it to streamline research and development processes, accelerate drug discovery, and enhance patient care. Other biopharma companies should follow suit by investing in AI technologies and creating a roadmap for rapid deployment. This includes training employees, integrating AI into existing workflows, and continuously iterating to improve performance.

2. Data is Everything

In the realm of gen AI, data is the lifeblood that fuels innovation. The more diverse and high-quality data a biopharma company can access, the more effective its AI models will be. This means that organizations must prioritize data collection, management, and analysis.

Biopharma companies should invest in robust data infrastructure that allows for the seamless integration of various data sources, including clinical trials, patient records, genomic data, and more. Additionally, implementing advanced data analytics tools can help extract valuable insights and drive decision-making. By harnessing the power of data, companies can develop more accurate AI models, leading to better predictions, personalized treatments, and improved patient outcomes.

3. Public vs. Private is a False Choice

When it comes to data and AI, the debate between using public or private data is often misguided. Instead of choosing one over the other, biopharma companies should leverage both to maximize the potential of gen AI. Public datasets can provide a broad foundation, while proprietary datasets offer specific insights unique to the company.

For instance, public genomic databases can be used to train AI models on general patterns and correlations, while proprietary clinical trial data can refine these models for specific therapeutic areas. By combining public and private data, biopharma companies can create more comprehensive and powerful AI solutions. Additionally, collaborations with academic institutions, research organizations, and other industry players can further enhance data diversity and model accuracy.

4. AI is an Emerging Technology, but One Where You Already Need to Show Results

While AI is still an emerging technology, biopharma companies cannot afford to wait for it to fully mature before showing tangible results. Stakeholders, including investors, regulatory bodies, and patients, expect to see the benefits of AI integration sooner rather than later.

To demonstrate value, companies should focus on quick wins and pilot projects that showcase the potential of gen AI. These initiatives can serve as proof of concept, illustrating how AI can improve efficiency, reduce costs, and enhance outcomes. For example, using AI to predict patient responses to treatments or identify potential drug candidates can provide immediate benefits and build confidence in the technology.

By delivering early successes, biopharma companies can secure buy-in from stakeholders and create momentum for broader AI adoption. This iterative approach allows organizations to learn and adapt, refining their AI strategies based on real-world feedback.

5. AI Will Redefine Work, But It Won’t Replace Humans (Yet)

One of the most significant truths about gen AI is that while it has the potential to redefine work in biopharma, it will not replace human expertise anytime soon. AI can automate repetitive tasks, analyze vast amounts of data, and provide insights that were previously unattainable. However, the human element remains crucial, particularly in complex decision-making and creative problem-solving.

For example, AI can help researchers identify potential drug candidates, but the nuanced understanding of biology, chemistry, and patient needs still requires human intervention. Similarly, AI-driven patient care tools can assist healthcare providers, but empathy, ethical considerations, and personalized care are aspects that machines cannot replicate.

Biopharma companies should view AI as an augmentation tool that enhances human capabilities rather than a replacement. By fostering a collaborative environment where AI and human expertise coexist, organizations can drive innovation and achieve better outcomes. Training and upskilling employees to work effectively with AI technologies is also essential for maximizing the benefits of this collaboration.


Generative AI offers transformative potential for the biopharma industry, providing opportunities to enhance drug discovery, streamline operations, and improve patient care. However, to fully realize these benefits, companies must embrace five core truths: prioritizing speed and scale, leveraging diverse data sources, combining public and private data, demonstrating quick wins, and fostering human-AI collaboration.

By adopting a proactive and strategic approach to gen AI, biopharma firms can create value-driven, scalable programs that deliver lasting competitive advantages. As demonstrated by Amgen’s success, the key lies in moving quickly, thinking differently, and continuously iterating to harness the full potential of AI. In doing so, biopharma companies can stay ahead of the curve, driving innovation and improving health outcomes for patients worldwide.

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