Introduction

SPEAKERS:
Elizabeth Otterman, Principal, PwC US
Jacquie Newland, Director, PwC US

KEY TAKEAWAYS:
• AI-enabled micro-segmentation now updates daily versus traditional week-long processes
• Platform selection must protect existing AI investments rather than force costly rebuilds
• Successful AI pilots require direct business outcome alignment and top-line impact
• Pharma-patient-physician AI ecosystems will converge within 2-3 years from current silos
• Data governance and AI-ready infrastructure are prerequisites for deployment success

The session opened with a provocative scenario: an AI assistant detecting poor sleep patterns and autonomously prescribing medication and supplements before you recognize the need. While this sounds like science fiction, PwC's Jacquie Newland insisted the planning must begin now. "Elizabeth painted a picture of the future of the AI-enabled assistant at your home prescribing you the medication you didn't even know you needed," Newland explained at Pharma Customer Engagement USA in Philadelphia. Her co-presenter, Elizabeth Otterman, framed the challenge simply: "I love thinking about how do we actually change the dynamics for our consumers." That consumer-centric transformation requires rethinking CRM fundamentals today.

The Speed Imperative From Weeks to Real-Time

The traditional CRM model operates on static segmentation cycles that take weeks to refresh, creating a fundamental mismatch with HCP expectations shaped by consumer digital experiences. Newland described the transformation AI enables: "How do you start to look at that micro-segmentation that updates on a daily basis based on the interactions that they're having with your content, with your website, with your MSL, with your sales reps?" This shift from periodic batch processing to continuous personalization represents more than incremental improvement—it's a competitive necessity as physician preferences for digital engagement and personalized information accelerate.

The capability to track multi-channel interactions and adjust messaging in real-time directly impacts engagement relevance and prescribing influence. When segmentation refreshes weekly or monthly, content recommendations reflect outdated behavior patterns. Daily micro-segmentation ensures physicians receive information aligned with their current interests, recent content consumption, and demonstrated preferences across all touchpoints. Organizations gain the ability to respond to physician behavior while it remains contextually relevant rather than retrospectively analyzing patterns that no longer reflect current needs.

Newland emphasized that speed without strategy creates waste.

"What we're seeing be successful is really being able to tie it to business outcomes. So not just doing a pilot to say, 'Hey, let's try to use AI for this,' but how to make sure that it's tied into your overall business objective,"

she explained. Organizations must connect AI-enabled segmentation to measurable outcomes—script lift, engagement rate improvements, or sales cycle reduction—rather than pursuing technological sophistication for its own sake.

The discipline to tie initiatives to top-line growth separates organizations achieving ROI from those conducting expensive experiments. As AI discussions dominate pharma conferences, the differentiation comes from demonstrating clear business impact rather than technical capabilities. Newland pointed to the proliferation of AI pilots across the industry, noting that successful implementations share a common characteristic: explicit linkage to revenue growth, market share gains, or operational cost reduction that executives can track against investment.

The Foundation Crisis Why Data Readiness Determines Success

Beneath the excitement about AI capabilities lies an unglamorous prerequisite that determines success or failure: data infrastructure. Otterman emphasized this foundational requirement explicitly. "What we see is that a lot of these work just as much as things like process unification and data governance to consider AI," she noted, positioning data preparation as equally important as the AI technology itself. Organizations rushing to implement generative AI or agentic systems without addressing underlying data architecture discover that sophisticated algorithms cannot compensate for fragmented, inconsistent, or inaccessible information.

The challenge intensifies in pharma environments where data resides in disconnected systems—CRM platforms, content management tools, web analytics, medical information databases, and field force reporting applications. AI-powered personalization requires integrating these data sources to understand the complete picture of HCP interactions and preferences. Without proper data models and connections, AI systems cannot deliver on their promise of real-time, contextual engagement. A physician's recent website visit remains invisible to the field representative's next-best-action algorithm, and content preferences expressed through email engagement don't inform MSL outreach strategies.

"The importance of being able to then have that AI-ready data is extremely important from a data readiness standpoint," Otterman stated. Organizations rushing to deploy AI without addressing underlying data quality, standardization, and integration issues face failed implementations and inability to scale pilots to production. The excitement around generative AI and agentic agents often overshadows the essential work of master data management and cross-system integration. Otterman described clients discovering months into AI initiatives that foundational data work should have preceded algorithm development.

In highly regulated pharma environments, poor data governance combined with AI amplifies risk exponentially compared to traditional systems. AI models trained on incomplete or inaccurate data can generate compliant-seeming but factually incorrect responses, creating regulatory exposure. Data privacy requirements add another layer of complexity, as AI systems must access sufficient information for personalization while maintaining appropriate boundaries around protected health information and competitive intelligence. The unsexy work of data preparation ultimately determines whether AI investments deliver value or become expensive cautionary tales.

Platform Decisions as Strategic Lock-In

When asked directly about Veeva versus Salesforce for AI-enabled CRM, Otterman avoided vendor advocacy and focused on strategic considerations. She explained that the two platforms take fundamentally different approaches—one emphasizing internalized AI development, the other pursuing partnership-based integration. The critical question isn't which approach is superior in the abstract, but which aligns with existing organizational investments. Companies that have already deployed large language models or built custom AI capabilities need platforms that leverage rather than replace these assets.

"What you don't want to do is have to rework those investments, and that is actually the consideration," Otterman advised. Organizations that have established specific data architectures or proprietary models face platform decisions that can force abandonment of these assets and millions in sunk costs. Platform decisions made without inventorying existing AI infrastructure create 12-18 month delays as systems are rebuilt to accommodate vendor-specific requirements. The technical debt from misaligned platform choices compounds over time as AI capabilities become more deeply embedded in engagement workflows.

The platform choice increasingly determines which AI capabilities are accessible, deployment speed, and whether existing investments amplify or conflict with vendor roadmaps. As AI shifts from supplementary feature to foundational capability, CRM platform selection carries greater strategic weight and longer-term lock-in implications. Otterman cautioned that organizations treating platform selection as primarily a feature comparison exercise miss the architectural implications that constrain or enable future AI evolution.

Newland provided evidence that AI clinical integration is already achieving measurable outcomes, not future aspirations.

Don't just read about it, be in the room at Pharma USA 2026 (March 17-18, Philadelphia). Visit the website here.

"ShockMatrix was recently tested in France hospitals to be able to see how AI could predict the ability for somebody to have a bleeding episode. And they were the same, if not better, than the physicians predicting that,"

she reported. This performance parity demonstrates that physician-facing AI systems are moving from experimental to operational, making platform architecture decisions urgent rather than theoretical.

The 2-3 Year Convergence Timeline

Newland described the current state as siloed AI applications—pharma companies optimizing next-best-action algorithms, patients receiving automated prescription reminders, physicians using AI-integrated EHRs—operating independently. The transformation she outlined involves convergence at the intersection of all three stakeholders within 2-3 years, creating an interconnected ecosystem where AI operates across pharma-patient-physician touchpoints simultaneously. This timeline reflects not technological readiness but market pressure as stakeholders experience AI-enabled interactions in consumer contexts and expect equivalent sophistication in healthcare.

This convergence enables scenarios like agentic agents that understand physician specialty, access approved content libraries, deliver information directly without human intermediation, and seamlessly escalate to MSL involvement when needed—all while integrating with clinical workflows. Organizations designing CRM strategies for isolated stakeholder groups risk building systems unable to participate in this emerging ecosystem. The architectural decisions made today—data models, integration approaches, compliance frameworks—determine whether organizations can adapt to interconnected engagement or require costly rebuilds.

The implications extend beyond technology architecture to compliance frameworks, as AI-mediated physician interactions require guardrails that don't yet exist in most organizations. Otterman emphasized bringing compliance teams into AI design as partners rather than gatekeepers, enabling education about how to leverage information while maintaining data privacy and regulatory standards. In highly regulated environments, establishing these frameworks proactively rather than reactively separates organizations that move quickly from those bottlenecked by risk management concerns. The compliance function's evolution from approval authority to design collaborator represents a cultural shift as significant as the technological transformation.

The session's core message: the planning horizon for AI-enabled CRM isn't 5-10 years but 2-3 years for ecosystem convergence and immediate for data foundation work. Organizations treating AI as future-state experimentation rather than current operational priority will face competitive disadvantage as physician expectations for personalized, AI-enabled engagement become standard. The time to establish data infrastructure, platform architecture, and compliance frameworks is now, before market pressure forces rushed implementations that sacrifice strategic coherence for speed.

To get you highlights of Pharma Customer Engagement USA, we are using generative AI technology to summarise the transcripts of the sessions. If you have any feedback about the summary, please contact lucy.fisher@thomsonreuters.com.

"My team tells me, oh, but we don't have a seat at the table. My advice is to be proactive, go find the table and get a chair and share how our expertise can support shared goals. Maybe they were not aware or did not understand. Maybe you have to make it easier for them."

This partnership model extends beyond technology. "Our customers don't see the different roles that we all represent," Kelly reminds. "Our customers see the company that we represent and the products." Internal functional distinctions become invisible to customers experiencing unified company touchpoints. When medical, commercial, and market access messages arrive uncoordinated, customers perceive organizational dysfunction regardless of the sophisticated rationales justifying each department's independent optimization.

Building Capability for Digital-First Engagement

The shift toward increasingly digital go-to-market models requires systematic capability building across three dimensions. Novo Nordisk started with foundational work: creating a glossary ensuring consistent terminology. "Can we all talk about the same thing in the same manner to ensure that when we're meeting a customer journey means a customer journey and not a pathway, not a digital asset," Gala explains. Without shared vocabulary, cross-functional collaboration stumbles on semantic confusion.

Training extends beyond front-line commercial teams to back-office functions. "This needs to be much more a company mindset," Gala argues. Technology upskilling ensures infrastructure readiness for emerging capabilities. Change management acknowledges the emotional reality of transformation: "I haven't met anyone that says that. Oh, I love not knowing what is going to come. I love not knowing what is going to be my job or how to do it."

Perez Casesmeiro adds perspective on navigating constant change: "The only constant is change. So I don't think there's anything new. The difference here is that it's happening very fast."

From Aspiration to Operation

The transformation journey requires patience and persistence. Organizational change messages need approximately eight repetitions before achieving stickiness. "Someone said that for people, for a message to stick, repeat eight times. So probably do 16," Gala advises. Starting with believers before scaling broadly allows proof points to accumulate. Simple tools, Excel-based editorial calendars, transparent customer journey maps, can drive meaningful coordination improvements without waiting for sophisticated technology solutions.

Hergli shares an example of cross-company collaboration that demonstrates possibilities beyond internal silos. Working with a partner holding commercialization rights in Japan for a Chiesi product, the companies coordinated regulatory filings for a new indication. "We have been helping them from a medical perspective with discussing with the healthcare professional and key opinion leaders, but also from regulatory perspective, from marketing perspective," she explains. The cross-functional, cross-company collaboration succeeded because shared purpose transcended organizational boundaries.

Kelly synthesizes the discussion into actionable principles: have one clear goal, communicate prolifically across functions, ensure everyone has voice in strategic conversations, and use meetings to hold up mirrors when customer-first principles are violated. "I think there are some practical things that we can be doing on a daily basis to achieve this vision," she notes. "We're trying new things and we're having to break down the silos global to regional, regional to local, but also within our functions, within our organization."

The imperative is clear: move from customer-first language to customer-first operations, one coordinated touchpoint at a time. As Perez Casesmeiro reminds, overlapping functions create organizational resilience: "No one operates alone. Shared responsibility is crucial in ensuring we don’t fail. There is too much at the stake for patients and healthcare professionals." The challenge isn't eliminating complexity but learning to navigate it effectively, transforming bureaucratic friction into collaborative strength that ultimately serves patients and healthcare professionals better.

To get you highlights of Pharma Customer Engagement EU faster, we are using generative AI technology to summarise the transcripts of the sessions. If you have any feedback about the summary, please contact lucy.fisher@thomsonreuters.com.

Text

Discover more on this topic at Pharma USA 2026 (March 17-18, Philadelphia) - North America's largest cross-functional pharma gathering. Visit the website here.