April 2024 - Artificial Intelligence | Digital Ecosystem | Digital Business Models

The Potential of AI in the Life Sciences

Saskia Steinacker, Senior VP Strategy at Bayer AG, reveals how Bayer is using AI and provides practical examples from the life sciences sector.

The Potential of AI in the Life Sciences -web

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Saskia Steinacker is Senior Vice President Strategy & Digital Transformation at Bayer AG. In this article on the potential of AI, based on a recent interview with Sidonie Krug in the eco Association’s German-language podcast, she reveals how Bayer is using AI and provides exciting practical examples from the life sciences sector.

Digital transformation is not an end in itself. It should have a strategic goal and a focus on serving the customer. Let's take Bayer as an example. We are active in the health and agriculture sectors, and digital technologies help us to tackle the major challenges our world is facing. These include providing medical care for the world's growing and ageing population and ensuring high-quality food, all while addressing climate change.

The key to meeting these challenges is to harness data and digital technologies, and to maximize the potential of AI. With more data, we can make better decisions. Digital technologies play a critical role in developing better solutions to the complex challenges facing our company and the industries we serve. High-performance computing is the foundation of Bayer's digital transformation. We have moved to the cloud, ensuring the establishment of a robust technology ecosystem with a focus on high-quality and secure data.

A concrete example of one of our digital solutions is Climate FieldView. This digital platform helps farmers collect and analyze vast amounts of data from their fields. By integrating this data with weather and other information, farmers can determine the optimal product placement, timing, and quantity, leading to significant cost reductions and better use of resources. Implementing such solutions not only benefits the farm economically, but also helps minimize environmental impact.

Significant potential for AI in the life sciences

Practical applications of AI at Bayer include its use in medical imaging, particularly in radiology. The challenge is the increasing demand for contrast-enhanced examinations (X-ray, CT, MRI). A typical radiologist in the U.S., for example, would have to interpret an image every three to four seconds over the course of an eight-hour day to keep up with demand. It is like working on an assembly line! Bayer has developed AI-based tools to help radiologists make accurate and timely diagnoses by highlighting specific areas of interest in images.

AI is also being used to facilitate demand planning in consumer health, particularly relevant during a pandemic. Using AI tools, Bayer uses historical sales, marketing, and advertising data to make forecasts for different scenarios. This helps to ensure timely product supply to meet fluctuating demand so that products reach store shelves and pharmacies efficiently.

Garbage in, garbage out

There are several key considerations when implementing and using AI tools. The right data is critical to AI. Precise and effective insights and predictions can only be achieved with high-quality data that is accurate and complete – after all, "garbage in, garbage out." To this end, Bayer has developed a group-wide data strategy that focuses on aspects such as security, data protection, and ethics in order to use AI responsibly and comply with laws and regulations in the various countries in which the company operates.

It is important not to get carried away with the trendiness of AI. There needs to be a strategic focus on high-value use cases, rather than experimenting for the sake of experimenting with AI. Sometimes a simple algorithm or rule-based system is sufficient. Not every situation requires a complex AI application. Generative AI, in particular, can be costly. There needs to be a holistic approach to considering the desired outcomes and justifying the use of AI to solve specific problems.

Navigating the skepticism around AI

There is also still a lot of skepticism around AI, and the acceptance of AI varies from country to country. This makes it even more important to validate results and incorporate additional data sources and verification to increase the credibility of AI applications. Providing more education and access to information about AI, especially in Germany where skepticism is high, could contribute to greater acceptance of AI applications among the general population.

This needs to be addressed on two fronts. Externally, the aim is to convince patients, farmers, and consumers by offering innovative, high-quality products and services based on AI and digital technologies. The focus is on demonstrating how these technologies solve real-world problems and deliver tangible benefits, with the goal of building acceptance through real-world demonstrations.

Internally, the company has recognized the importance of getting employees on board with AI initiatives. While Bayer is generally positive and open-minded about the potential of AI, employees may have questions about what it means for their daily work and how to use it effectively. The company is actively addressing this by encouraging experimentation, testing and continuous learning. There is a strong emphasis on communication with employees, including initiatives such as the in-house IT Academy for continuous education.

Harnessing the potential of AI in the coming years

We are excited about the future and expect to see advances and positive impacts from AI and digital technologies across multiple sectors in the coming years. One of Bayer’s strategic priorities is to help shape regenerative agriculture by investing in innovations that increase food production, farm incomes and climate resilience, while also protecting and restoring nature. Bayer aims to use AI in research and development to improve breeding techniques and create the next generation of seeds tailored to specific conditions. The goal is to increase the efficiency and speed of the breeding process.

In the pharmaceutical sector, particularly in cell and gene therapy, Bayer has made significant investments in companies such as AskBio and BlueRock Therapeutics. These companies are pioneers in gene therapy and PSC-based cell therapy and contribute to Bayer's optimism about the potential of these technologies.

In the Consumer Health division, Bayer plans to position itself more broadly in precision health and self-care solutions. The focus is on personalized, data-driven recommendations to help patients optimize their health, including managing vitamin intake. AI-enabled applications are seen as critical to achieving these goals.

In closing, I must reiterate my strong belief that digital transformation and AI should not be pursued as an end in itself. Instead, their implementation must always serve people and be aligned with fundamental business decisions. The focus must be on adopting appropriate solutions that take into account financial, environmental, and social costs, and deliver real benefits to users and businesses.


Saskia Steinacker currently serves as the Senior Vice President Strategy & Digital Transformation at Bayer. In 2018, she was nominated for the European High-Level Expert Group for Artificial Intelligence, which included recommendations on future AI-related policy development and on ethical, legal, and societal aspects related to AI. She also serves on the Presidency Committee of eco – Association of the Internet Industry as well as a board member of the Y-USA National Board. 

She graduated from Ecosign Academy with distinction in Communication Design and holds a Master’s in Business Administration from Liverpool University.

Please note: The opinions expressed in Industry Insights published by dotmagazine are the author’s own and do not reflect the view of the publisher, eco – Association of the Internet Industry.