AI in Pharma: Transforming the Future of Sales Excellence and Operations

Jack Lampka, a keynote speaker with 27 years of experience in technology and AI, cautioned against the common mistake of starting AI initiatives by focusing on the technology, which often results in a “solution looking for a problem.” Noting that 70% of AI projects fail—often for this very reason—he instead advises companies to start with concrete challenges that need to be addressed in the business. In the pharmaceutical industry, this means decision-makers should first identify specific areas where AI can deliver measurable value—such as optimizing sales strategies or streamlining research and development—and establish clear KPIs to ensure focus and accountability. 

Lampka also notes that bringing the business side into the early stages of AI initiatives is essential. Because success hinges not just on the quality of the AI solution, but on whether it is actively used, it’s crucial to involve business stakeholders in setting usage goals and make them co-owners of the AI-driven outcomes. But Lampka emphasized that adoption will not be automatic. AI leaders should develop a well-thought-out internal marketing strategy to effectively communicate the benefits of their initiatives to stakeholders and ensure solutions are both understood and embraced across the organization.

Lampka emphasized the importance of distinguishing between different forms of AI, including Machine Learning (ML), Deep Learning (DL), and Generative AI (Gen AI). While ML and DL have been used for years in contexts such as predictive analytics for sales and marketing, Gen AI, the “new kid on the block,” excels at creating content such as text, images, and videos. For pharmaceutical companies, understanding these distinctions helps in selecting the right AI technology to meet specific business needs. 

Lampka also noted that while Generative AI is currently generating buzz, traditional ML and DL have the potential to provide even greater business value. While McKinsey estimates that Gen AI could contribute up to $4 trillion annually to the global economy, it’s possible that ML and DL could generate up to four times that amount. Accordingly, Lampka cautioned companies to not let the hype around Gen AI overshadow the proven impact of ML and DL, which continue to drive significant value across industries. 

AI strategy expert Dr. Marlon Rück stressed the importance of and challenges around regulatory compliance when integrating AI into the pharmaceutical industry. Given the high stakes, particularly around patient safety and treatment efficacy, AI systems must be thoroughly tested at all levels to ensure safe and reliable implementation.  

Rück also highlighted that compliance must be considered from the very early stages of AI planning. Organizations should prioritize transparency and closely collaborate with authorities to ensure their AI solutions meet all regulatory standards. By embedding these practices into AI development, companies can mitigate risks and ensure successful, compliant AI integration that delivers real value.

AI offers significant opportunities to streamline production, from planning and optimization to maintenance scheduling. Automating routine tasks and using AI-driven analytics enables better resource allocation, quicker market responses, and a stronger competitive edge. Furthermore, AI can optimize production planning, process workflows, procurement, and energy consumption, ensuring smoother operations and timely availability of materials. 

Rück also highlighted how AI-driven knowledge management can prevent knowledge silos, while automating manual tasks like document writing speeds up processes. This combination of automation and optimization can help companies increase efficiency, maintain compliance, and reduce costs throughout the production cycle.