Pharma is rushing to adopt AI-driven tools in the hope of revolutionizing everything from drug discovery to clinical trials, regulatory submissions, and marketing efforts. While AI is the technology du jour, Saskia Steinacker, senior VP, global head strategy and digital transformation at Bayer, argues that successful AI implementation within Pharma is no walk in the park. Rather than merely making analogue processes digital, Steinacker feels that only a transformational approach to AI across organisations will lead to the desired results.


Longstanding Commitment to AI

Last year Bayer partnered with Google Cloud to improve its drug discovery processes using the firm’s Tensor Processing Units (TPUs), and its Vertex AI and Med-PaLM 2 to refine clinical trials, but AI adoption is not new at Bayer. And as Steinacker is quick to point out, it predates the current buzz around the technology. “The company has a longstanding commitment to AI, evident in our substantial investments in this area.”

Integrating AI into its processes is also part of a broader digital transformation at Bayer that was further pushed forward by the COVID-19 pandemic. “We intensified our digital transformation efforts across the entire business spectrum, from research and development to product supply and commercialization,” says Steinacker. “Embracing the latest technologies, we integrated them into our processes. We focused on impactful enablers, e.g., achieving significant advancements in moving to the cloud, ensuring high-quality and secure data assets.”


Medical Imaging, Consumer Health Marketing, Forecast Planning

Beyond Bayer’s AI integration efforts in drug discovery and clinical trials, the German life sciences giant has also focused on its uses in medical imaging. Working with Blackford Analysis, a recently acquired imaging AI platform and solutions provider, Bayer launched Calantic Digital Solutions in 2022, which offers a growing number of AI-powered radiology applications to support radiologists by automating  time-consuming tasks and accelerating workflows.

“In medical imaging AI has been instrumental for a while, contributing to an estimated 286 million contrast-enhanced X-ray, CT, and MRI procedures conducted annually worldwide,” Steinacker confirms. “This extends to diagnostic innovations and therapeutic applications, showcasing Bayer’s unique advantage in both areas.”

Other areas where Bayer is harnessing the power of AI is in marketing for consumer health and in forecast planning. “Leveraging historical sales data, marketing information, and various datasets, Bayer ensures accurate forecasts, enabling proactive management of demand fluctuations.”


Avoiding the Pitfalls of AI Adoption: The Importance of Partnerships

For Steinacker it is important for organizations to realize that AI implementation is not merely a digital transformation of analogue processes and requires a wider re-assessment of the processes involved.

She stresses the importance of partnerships when it comes to integrating AI. “The strategic partnerships we have forged play a crucial role in this journey,” she asserts. “Instead of attempting to tackle every aspect independently, we actively seek partnerships and collaborations within the AI ecosystem.” This approach has enabled Bayer to leverage the collective expertise of the company and its partners, contributing to a broader pool of knowledge and accelerating innovation.

In addition, in Steinacker’s view organizations should not start with AI, but with the challenges they need to resolve. “Rather than searching for problems to fit the AI solution, we remain centred on addressing specific challenges to the benefit of patients, consumers, and farmers,” she stresses. “This purpose-driven focus guides our utilization of AI as a strategic tool to solve identified problems effectively.”


Understanding Risks, Educating Employees

Organizations must also take into account AI-related risks when adopting the technology, Steinacker claims. “Stakeholders must have a deep understanding of these risks and be sure to address them, one means being explainability. Ensuring that AI models can be explained and understood is vital for transparency and regulatory compliance.”

Moreover, privacy concerns, especially regarding data and software codes used in generative AI, must be addressed. “The evolving regulatory landscape demands a careful examination of how AI practices align with privacy regulations, ensuring compliance in the face of increasing legal scrutiny,” says Steinacker.

Finally, Bayer’s digital transformation lead stresses the importance of education. “Adequate education and skill development are critical. Employers and employees need to be well-versed in AI applications and understand how to leverage AI tools responsibly and effectively. This involves training personnel to use AI as a solution to real-world challenges.”


Read PharmaBoardroom’s full interview with Saskia Steinacker here