Scaling smarter: How pharma leaders are embedding AI across the enterprise
Executive interview
Pharma 2025 made good on its mission to help industry leaders turn promise into practice with a jam-packed schedule filled with sessions delivering impactful solutions. During the event, Reuters interviewed Slalom’s global industry lead for life sciences, Johanna DeYoung, and Joe Ferraro, VP of product, life sciences cloud, Salesforce. They provided recommendations for how life sciences enterprises can futureproof to withstand change and disruption.
Don’t have time to watch? Get our key takeaways:
1. Anchor AI to business strategy from day one
With close to 30% of generative AI (Gen AI) projects being abandoned after proof of concept, Slalom’s Johanna DeYoung noted that success depends on looking beyond the pilot phase—accounting for total cost of ownership, cross-year ROI, and strategic alignment. Joe Ferraro from Salesforce emphasized getting back to the fundamentals: defining your KPIs, understanding risks, and measuring what matters. AI must serve a clear business purpose to become more than a one-off experiment.
2. Make agility a cultural habit, not a buzzword
Futureproofing life sciences organizations goes beyond technology. It requires embedding agility into leadership behaviors, workforce skills, and decision-making structures. Johanna emphasized that cross-functional readiness and scenario planning are essential in addressing geopolitical instability and evolving regulatory landscapes. Strategic adaptability isn’t reactive—it’s built-in.
3. Treat governance as an enabler, not a gatekeeper
AI is only as good as the data it runs on. Pharma organizations must shift from data custodianship to data connectivity—breaking down silos across functions like commercial and clinical. Joe underscored the need to challenge long-held compliance assumptions. Johanna added that governance must evolve to promote access and usability, not just control—ensuring the right people have the right data at the right time.
4. Know when to build—and when to partner
New technologies are arriving faster than most enterprises can absorb. Joe highlighted the growing need to align with infrastructure and platform leaders rather than reinventing the wheel. Offloading complexity to trusted partners allows pharma companies to focus on their differentiators while maintaining speed and trust as agentic AI and probabilistic workflows become more prevalent.
5. Rethink operating models to unlock cross-functional value
Legacy org models—often built around function-first priorities or protective data silos—are no longer fit for purpose. Johanna pointed out that many of these models are rooted in what she calls "ego architecture," where decisions are made to preserve control rather than create value. As demands on speed, collaboration, and adaptability increase, organizations need to redesign workflows and governance to support enterprise-wide problem-solving and shared accountability.
Panel discussion
Pharma 2025 also hosted a panel discussion titled AI: From promise to practice, moderated by Chetak Buaria, VP, global commercial operations, oncology, Merck Healthcare, and featuring Jörg Schüttrumpf, chief scientific innovation officer, Grifols, Alyssa Fenoglio, VP, global head of digital commercial, Teva Pharmaceuticals, Joe Ferraro, VP product, life sciences cloud, Salesforce, and Johanna DeYoung, managing director, life sciences, Slalom. They explored how AI is shifting from experimentation to enterprise-wide transformation in pharma, with technologies like agentic AI reshaping workflows, reducing operational friction, and enabling faster, more personalized innovation across R&D and commercial operations.
Don’t have time to listen? Get our key takeaways:
1. Anchor AI to business strategy from day one
With close to 30% of generative AI (Gen AI) projects being abandoned after proof of concept, Slalom’s Johanna DeYoung noted that success depends on looking beyond the pilot phase—accounting for total cost of ownership, cross-year ROI, and strategic alignment. Joe Ferraro from Salesforce emphasized getting back to the fundamentals: defining your KPIs, understanding risks, and measuring what matters. AI must serve a clear business purpose to become more than a one-off experiment.
2. Make agility a cultural habit, not a buzzword
Futureproofing life sciences organizations goes beyond technology. It requires embedding agility into leadership behaviors, workforce skills, and decision-making structures. Johanna emphasized that cross-functional readiness and scenario planning are essential in addressing geopolitical instability and evolving regulatory landscapes. Strategic adaptability isn’t reactive—it’s built-in.
3. Treat governance as an enabler, not a gatekeeper
AI is only as good as the data it runs on. Pharma organizations must shift from data custodianship to data connectivity—breaking down silos across functions like commercial and clinical. Joe underscored the need to challenge long-held compliance assumptions. Johanna added that governance must evolve to promote access and usability, not just control—ensuring the right people have the right data at the right time.
4. Know when to build—and when to partner
New technologies are arriving faster than most enterprises can absorb. Joe highlighted the growing need to align with infrastructure and platform leaders rather than reinventing the wheel. Offloading complexity to trusted partners allows pharma companies to focus on their differentiators while maintaining speed and trust as agentic AI and probabilistic workflows become more prevalent.
5. Rethink operating models to unlock cross-functional value
Legacy org models—often built around function-first priorities or protective data silos—are no longer fit for purpose. Johanna pointed out that many of these models are rooted in what she calls "ego architecture," where decisions are made to preserve control rather than create value. As demands on speed, collaboration, and adaptability increase, organizations need to redesign workflows and governance to support enterprise-wide problem-solving and shared accountability.
Panel discussion
Pharma 2025 also hosted a panel discussion titled AI: From promise to practice, moderated by Chetak Buaria, VP, global commercial operations, oncology, Merck Healthcare, and featuring Jörg Schüttrumpf, chief scientific innovation officer, Grifols, Alyssa Fenoglio, VP, global head of digital commercial, Teva Pharmaceuticals, Joe Ferraro, VP product, life sciences cloud, Salesforce, and Johanna DeYoung, managing director, life sciences, Slalom. They explored how AI is shifting from experimentation to enterprise-wide transformation in pharma, with technologies like agentic AI reshaping workflows, reducing operational friction, and enabling faster, more personalized innovation across R&D and commercial operations.
Pressed for time? Here’s what you need to know:
1. Scale beyond pilots: Embedding AI across functions is the next frontier
The pharma industry is shifting from experimentation to enterprise AI. "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."
2. Prioritize measurable use cases to drive commercial outcomes
AI is most impactful when tied directly to business problems. At Teva, the team used AI to uncover revenue missed between pharmacy sales visits. “We identified a problem... and created an AI model that produced personalized recommendations for upselling and cross-selling,” said Fenoglio.
3. Accelerate scientific discovery through AI-powered R&D
AI is enabling breakthroughs in complexity and scale. “In R&D, AI makes a qualitative jump. You can do things you couldn’t do before,” said Jörg Schüttrumpf, chief scientific innovation officer at Grifols, describing retrospective analysis models that detect disease signals years before symptoms emerge.
4. Build AI on a strong foundation of connected, trusted data
“We need to move from custodians of data to connectors of data so data sets can flow across functions and departments,” said Slalom’s Johanna DeYoung. A unified data infrastructure—interoperable, real-time, and well-governed—is critical to enterprise-scale success.
5. Innovation only sticks when culture is ready for change
“It’s all about people. That’s our biggest barrier to success and our key to success,” said Alyssa Fenoglio, emphasizing that mindsets and behaviors—not just tools—determine AI’s staying power. Joe Ferraro from Salesforce added that success depends on creating space for experimentation tied to business value.
