Whitepaper

Build smarter pharma operations with AI

Pharmaceutical manufacturing is entering a new era – one shaped by intelligent automation, data-driven decision-making, and real-time responsiveness. AI is already helping leading companies like Pfizer, Merck, and Gilead unlock new levels of efficiency, quality, and agility. This whitepaper, created by Zühlke and the University of St. Gallen, focuses on how to get started. Rather than rehashing AI’s potential, it offers concrete steps, frameworks, and case studies to help pharma decision-makers turn ambition into action.

Download the whitepaper

Pharmaceutical manufacturing is entering a new era – one shaped by intelligent automation, data-driven decision-making, and real-time responsiveness. AI is already helping leading companies like Pfizer, Merck, and Gilead unlock new levels of efficiency, quality, and agility. This whitepaper, created by Zühlke and the University of St. Gallen, focuses on how to get started. Rather than rehashing AI’s potential, it offers concrete steps, frameworks, and case studies to help pharma decision-makers turn ambition into action.

Key takeaways from the whitepaper

The whitepaper provides a comprehensive overview of the current state of AI in pharmaceutical manufacturing, alongside practical insights into implementation. Below, we highlight a selection of key takeaways that offer a clearer understanding of the trends, strategies, and requirements shaping the path forward for industry leaders.

AI is already making an impact

From optimising bioreactor conditions to reducing false reject rates and accelerating data access, leading pharma companies are seeing measurable results. Pfizer, for example, is using machine learning and digital twins to enhance bioprocessing. Merck has halved false reject rates through AI-powered inspection, while Gilead has cut data search times significantly through enterprise AI. These examples show that AI is not a future vision – it’s delivering value now.
Getting started requires structure

Successful AI adoption doesn’t start with technology – it starts with process. The whitepaper introduces a practical implementation framework that covers model lifecycle management, data governance, compliance alignment, and validation steps. It helps organisations define the essential foundations for deploying AI in a regulated manufacturing context.
Regulatory readiness matters

AI needs to work within the strict confines of GxP and GMP. The paper provides insight into what regulators expect when it comes to documentation, validation, and version control – and explains why static models may be easier to approve than adaptive ones. It also offers suggestions for change management and auditability.
Organisational readiness is critical

AI initiatives succeed when they’re supported by the right people, roles, and infrastructure. The paper discusses how cross-functional collaboration – between data teams, QA, regulatory affairs, and IT – is essential for scaling AI safely and sustainably. It also outlines the importance of internal education and data maturity.
Start small, scale smart

Broad AI transformation doesn’t happen overnight. The paper advocates for a phased, use-case-driven approach that allows teams to learn, adapt, and build trust step by step. By focusing on specific, measurable outcomes, pharma leaders can demonstrate early wins and lay the groundwork for broader implementation.

About the whitepaper

This paper brings together research, industry insights, and expert guidance from Zühlke and the University of St. Gallen. It’s designed to help decision-makers in pharma get a clearer view of the building blocks for compliant, scalable adoption – without compromising compliance or quality.

Download the whitepaper

Meet the authors

Dr. Gabriel Krummenacher

Head of Data Science

CV

Gabriel Krummenacher leads the Data Science Team at Zühlke and has several years of experience in conducting data analytics and machine learning projects. His main focus is on medical machine learning applications and bringing prototypes to production. He holds a PhD and M.Sc. from the Institute for Machine Learning at ETH, where he worked on scalable methods for large-scale and robust learning, wheel defect detection and sleep stage prediction with deep learning.

View LinkedIn profile of Dr. Gabriel Krummenacher
Jessica Helbling

Research Associate & PhD Candidate (Hochschule St. Gallen)

View LinkedIn profile of Jessica Helbling
Angeli Möller

Group Chief Health Officer

View LinkedIn profile of Angeli Möller
Prof. Dr. Thomas Friedli

Director, Institute of Technology Management (Hochschule St. Gallen)

View LinkedIn profile of Prof. Dr. Thomas Friedli