Introduction

AI is transforming pharmaceutical customer engagement strategies, but leaders emphasize it's no magic solution. As one panelist noted, "AI is only just as good as its user," highlighting the importance of thoughtful implementation across content creation, customer engagement, and operational excellence.

Finding the Optimal Human-AI Balance

Pharmaceutical companies are discovering that AI's greatest value comes from augmenting human capabilities rather than replacing them. Conor Riordan, Director of Customer Service at Pfizer, emphasized this point:

"Talking to our customers is a competitive advantage... We're trying to do the work faster but not necessarily eliminate people because people are the gold dust here. It's relationships that matter."

This perspective represents a significant evolution in thinking about AI implementation. Rather than pursuing complete automation, leading organizations are focusing on using AI to handle routine tasks while empowering humans to make critical decisions and maintain customer relationships.

Data-Driven Customer Profiling and Engagement

AI is revolutionizing how pharmaceutical companies understand and engage with healthcare professionals. Berkan Aysan, Global Head of Medical Engagement Excellence at Merck, described their approach: "We came up with a three-step framework. First, we make sure that the data we are giving into is right. Then through different poly data sources, we take the insights ourselves, we mine it. Third, we take action."

This methodical approach ensures AI-driven engagement remains relevant and valuable. Companies are increasingly able to create comprehensive HCP profiles that consider multiple factors, resulting in more personalized interactions.

Nataliya Andreychuk, CEO of Viseven, highlighted how AI is connecting previously siloed data: "The beauty is because the technology is generative, it is growing. The more we work in that direction, the more accurate mechanism we will have in place." This evolution enables increasingly sophisticated personalization while maintaining compliance.

Streamlining Operations Through Predictive Intelligence

One of the most concrete applications of AI involves optimizing supply chain and forecasting capabilities. At Pfizer, which ships approximately 1.4 billion product packs annually across 200 countries to 130,000 customers, AI has transformed operations.

"We have about 16,000 SKUs that we have to forecast. We have about 700 brands we sell, and at this point in time, about 75%—about 12,000 of 16,000—we are now forecasting using AI,"

explained Riordan. More importantly, "The AI forecasts are better than the human forecasts," resulting in fewer manufacturing disruptions and improved on-time delivery.

This operational excellence directly impacts customer satisfaction, as Riordan noted their customer experience metrics show that "the number one key differentiator in terms of net promoter score in CSAT is the metric on time in full."

The Critical Role of Data Stewardship

All panelists emphasized that effective AI implementation requires robust data governance. Pfizer's "System Care" process exemplifies this approach, defining "which data objects are critical to decision making within the process" and implementing "an annual review process on that data object."

This foundation enables what Riordan described as the "wisdom lifecycle"—converting "data into information, information into knowledge, and then knowledge into action." While AI excels at the first conversion, humans remain essential for the critical step from knowledge to action.

Andreychuk echoed this sentiment regarding content compliance: "The human is in the driver's seat. But being in the driver's seat doesn't mean that we do not need a very experienced assistant." She described how AI can streamline MLR (Medical, Legal, Regulatory) processes by checking references, regulations, and previous approvals, allowing humans to focus their attention on areas requiring critical judgment.

Bridging Silos Through Integrated Intelligence

AI is helping pharmaceutical companies discover unexpected synergies across traditionally separate business areas. Aysan shared how at Merck, AI has revealed connections across their diverse portfolio: "Many of these patients are under polypharmacy. By using AI and different data sources, we realize that there are a lot of synergies within our portfolio."

This integration enables more holistic approaches to patient care and HCP engagement, aligning with the industry's move toward more patient-centered models.

The Future of Pharmaceutical Engagement

As AI tools continue to evolve, pharmaceutical leaders are rethinking fundamental aspects of customer engagement. Andreychuk challenged conventional thinking: "Let us imagine the email of the future. Will this email have interactive elements of communication? Questions, answers? Maybe this will be a future email we will be crafting."

Aysan emphasized that AI should ultimately enhance the most important healthcare relationship: "As a medical doctor myself, we lack soft skills speaking with patients. As we know, patients are strong emerging stakeholders in the industry, and we are not talking about the communication between HCPs and patients enough. We as pharma have the responsibility to enhance that communication as well."

The panel concluded with Andreychuk's reminder about maintaining human direction: "The content needs to be relevant. This is what is important. And relevancy of our communication is driven by us, humans... We need just to make sure we are in the driver's seat."

Key Takeaways:

1) Human-AI collaboration delivers the best results: "People are the gold dust here. It's relationships that matter," emphasized Riordan, highlighting that AI should enhance rather than replace human interactions.
2) Data quality determines AI effectiveness: Proper data stewardship and governance are essential foundations for any AI implementation, requiring systematic processes to maintain data integrity.
3) AI excels at forecasting and operations: "The AI forecasts are better than the human forecasts," noted Riordan, demonstrating how AI can deliver measurable improvements in supply chain management.
4) Customer-centricity requires both operational metrics and experience measurement: Organizations must combine internal performance indicators with direct customer feedback to understand the full picture.
5) Content relevance remains paramount: "The content needs to be relevant. This is what is important. And relevancy of our communication is driven by us, humans," reminded Andreychuk, emphasizing that AI tools must ultimately serve human objectives.

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 "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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