Introduction

Artificial intelligence is reshaping how healthcare organisations think, plan, and connect. This year’s AI in Action finalists show what happens when AI stops being a buzzword and starts becoming a practical tool for improving patient access, professional efficiency, and commercial impact. From smarter field engagement to precision-targeted communication, these projects prove that data can be both powerful and human.

Takeda - NURA
Takeda’s NURA platform puts artificial intelligence directly in the hands of field representatives, transforming how they prepare for and conduct their conversations with healthcare professionals (HCPs). Instead of generic outreach, representatives now have a clear, data-driven view of each HCP’s patient population, prescribing patterns, and access barriers. That means better identification of patients who could benefit from Takeda’s therapies and faster access to savings and support programs.

The impact has been immediate. Since launch, the brand has exceeded its forecast in 10 of 11 weeks, with nearly a 20 per cent increase in HCP sample requests and a measurable lift in patient access. Representatives describe NURA as "a coach in my pocket," a partner that surfaces relevant insights in seconds and builds confidence before every interaction. Built on Takeda's Patient, Trust, Reputation, and Business framework, NURA has set a new benchmark for responsible AI adoption - transparent, compliant, and scalable across global markets.

Grünenthal - Pragmatic AI for Commercial Excellence
Grünenthal is taking a disciplined, practical approach to AI - focusing on what directly improves outcomes for healthcare professionals and, ultimately, patients. Two pilot initiatives have already transformed the company's commercial operations. The Content Tag & Trace system automates the tagging of promotional and educational materials, achieving 100 per cent coverage and cutting manual work by 74 per cent. Meanwhile, the Pre-Call AI Assistant helps field representatives prepare more effectively, offering tailored insights from customer data and prior interactions.

The results speak for themselves: 97% adoption among reps, a 13% increase in call-plan adherence, and a Net Promoter Score of 75. By saving thousands of hours and improving call quality, Grünenthal has demonstrated how targeted AI applications can deliver measurable value. Its AI Governance Framework now provides a clear path for scaling these tools responsibly, ensuring that innovation remains ethical, efficient, and aligned with real healthcare needs.

Currax Pharmaceuticals - Small but Mighty
Currax Pharmaceuticals faced a challenge shared by many smaller players in the obesity treatment market: how to compete with big-budget brands. With AI and machine learning at its core, Currax built a precision-targeted campaign for Contrave, an FDA-approved oral therapy for weight management. The system used predictive modelling and privacy-safe geotargeting to connect patients and doctors at exactly the right moment. When the patient was planning a visit, the physician could discuss treatment options.

The results were striking: a more than 15 per cent increase in prescriptions, a 20 per cent rise in revenue from targeted prescribers, and engagement rates up to 90 times higher than industry averages. Reaching 69,000 additional healthcare professionals who were previously outside the reach of sales teams, Currax showed how a smaller company can use AI not to automate but to amplify human connections. The project also created a scalable framework for future campaigns - proof that strategic data use can level the playing field.

A Common Thread: Responsible Intelligence
Across all three projects, there is a consistent theme: AI delivers its greatest impact when it is guided by purpose and built on trust. Takeda uses it to improve patient access, Grünenthal to enhance professional engagement, and Currax to create intelligent outreach on a tight budget. Each company shows that the next wave of digital transformation in healthcare will not replace people - it will empower them to make faster, fairer, and more effective decisions for patients everywhere.

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"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.

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