From Experimental to Essential: How AI is Transforming Business Models
The pharmaceutical industry stands at an inflection point in its AI journey, with companies rapidly transitioning from exploratory pilots to strategic implementation. As organizations increasingly embed AI across functions, industry leaders are discovering transformative applications that deliver measurable business impact and reshape traditional approaches.
Current State of Implementation
A real-time poll during the session revealed most pharmaceutical companies are currently piloting specific AI use cases, signaling the technology's growing tangibility. This transitional phase reflects the industry's recognition that AI represents more than hype.
"We're already beyond the hype," noted Alyssa Fenoglio, Global Head of Digital Commercial at Teva Pharmaceuticals. "When we look across industries, 75% of companies are already embedding AI or gen AI in some part of their business. We just hit 500 million weekly users of ChatGPT last month. It is now the fastest growing consumer app ever."
Transformative R&D Applications
The most profound impacts may be occurring in research and development, where AI enables capabilities previously unimaginable. Dr. Jörg Schüttrumpf, Chief Scientific Innovation Officer at Grifols, explained how his company leverages its vast repository of more than 100 million plasma samples collected over nearly 15 years.
"In R&D, AI makes a qualitative jump. You can do things you couldn't do before. You can make sense out of very complex data sets," Dr. Schüttrumpf said. "We are partnering with The Michael J. Fox Foundation for Parkinson's Research to go back in time and actually see in the plasma samples, 10 years before the disease came, what happened and how it developed over time with really deep proteomics."
This retrospective analysis capability has spawned an entirely new discovery platform that connects Grifols sample repository with real-world health data to identify disease biomarkers and potential treatments long before symptoms appear.
Reimagining Commercial Operations
On the commercial side, companies are applying AI to enhance field team effectiveness and customer experiences. Johanna DeYoung, Managing Director of Life Sciences at Slalom, highlighted an implemented solution providing real-time guidance for field teams.
"We created this genius support bar prompting for field teams that prompted them on what to focus on, what to say, how to make personalized outreach based upon live realtime data," DeYoung explained. "This moved them away from manual territory planning or static NBA models and really into dynamic insight-to-impact guidance."
The results were compelling—effectively giving each field team member their own dedicated sales operations support that never sleeps. This approach broke through data silos while delivering more relevant customer engagement.
Similarly, Teva Pharmaceuticals implemented an AI solution addressing a specific revenue challenge. "We identified a problem—we were missing sales revenue with our pharmacy sales force between sales visits," Fenoglio shared. "Our expert data science team looked at historical transaction data and lookalike data from pharmacists with similar profiles to create an AI model that produced personalized recommendations for upselling and cross-selling."
Accelerating Innovation Cycles
For technology providers, AI is transforming product development itself. Joe Ferraro, VP Product for Life Sciences Cloud at Salesforce, described how AI tools accelerate the innovation process.
"We're able to have a conversation on a Monday about an idea—maybe an autonomous AI agent for an MSL—and turn something around in days using prompt engineering to build rapid prototypes," Ferraro said. "That feedback loop is faster and faster. We're getting to value and ultimately products that solve real business problems more quickly."
This acceleration is yielding tangible results: "We're able to deliver about 20% more in initial releases than we initially anticipated just due to being an AI-powered product organization."
From Generative to Agentic AI
A significant paradigm shift is underway as organizations move from generative AI to more sophisticated agentic AI. While generative AI focuses on content creation and information retrieval, agentic AI represents a fundamental evolution.
"When you look at innovation as a framework, first step is digitization, then modernization, then transformation," DeYoung explained. "Where GenAI was about data and questions and prompt engineering, agentic AI will be about flow engineering and a very different cognitive architecture."
This evolution enables AI to act, not just inform—transforming entire workflows rather than simply augmenting individual tasks.
Implementation Challenges
Despite enthusiasm, significant challenges remain. Panelists highlighted three primary obstacles: data integration, governance frameworks, and workforce readiness.
"If you don't have your data foundation in place, it's going to go much slower," cautioned Fenoglio. "You're having to first build the structured data in one place and then add AI on top."
Governance presents another critical consideration. "You need to partner with compliance, legal, and IT on golden rules," Fenoglio advised. "You need to set in place governance to inspire innovation and experimentation with your employees but also minimize risk."
Perhaps most crucially, success depends on human factors.
"It's all about people. That's our biggest barrier to success and our key to success," Fenoglio emphasized. "You have to surround the entire program with culture and capability to change mindsets and behaviors."
Sustainable Implementation Strategies
As organizations look to embed AI sustainability in their strategy, panelists offered clear guidance: "Start now. If you haven't started now, you're already behind," urged Fenoglio. "As leaders, we need to role model the behavior and encourage experimentation and innovation in our teams, create psychological safety, make sure teams can experiment in a safe way to minimize risk."
Ferraro emphasized the importance of measurable outcomes: "You need to be really thoughtful about the challenges you're taking on and ultimately tie it back to business value first. Then make these things measurable."
DeYoung stressed the foundational importance of data infrastructure: "AI requires modern interoperable, real-time, trusted data to be able to move from isolated insights to enterprise value. We need to move from custodians of data to connectors of data so data sets can flow across functions and departments."
Key takeaways:
· Scale beyond pilots: Most GenAI strategies lack cross-unit integration and transformative impact.
· Focus on business problems: Target specific challenges where AI delivers measurable value.
· Prepare for agentic AI: It will reduce operational friction, enhancing agility and personalization.
· Fix data foundations: Modern, interoperable data systems are essential for enterprise-wide AI value.
· Build human capabilities: Surround technology with culture that changes mindsets and behaviours.
In order to get you the highlights of Pharma EU faster, we are using generative AI technology to summarise the transcripts of the sessions. If you have any feedback about the summary and the post-event report, please contact lucy.fisher@thomsonreuters.com.
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