Introduction

Key takeaways:

·      Agentic AI enables "silent transformation" requiring workflow integration, not technical expertise mastery

·      Pharma data remains frozen and noninteroperable; AI liquefies assets for cross departmental insights

·      Commercial operations lag manufacturing and R&D by 15-20 years in digital transformation

·      Territory expansion from 200 to 2,000 HCPs per rep reframes AI ROI

·      AI investment depreciates within three months; partner rather than build proprietary solutions

The pharma industry stands at an inflection point where commercial operations, long the "last frontier of disruption", are finally catching up to manufacturing and R&D in digital transformation. But Haider Alleg, General Partner at Allegory Capital, warns against a critical misconception: commercial teams don't need to become technology experts to harness agentic AI. Speaking at Pharma Customer Engagement Europe 2025, Alleg explained the transformation ahead resembles the smartphone revolution more than blockchain or metaverse hype. The key lies in understanding how to "liquefy" the industry's vast frozen data assets into actionable intelligence that transforms customer engagement without requiring commercial teams to become software engineers.

The Silent Revolution Pharma Didn't See Coming

Unlike previous technology waves that demanded wholesale process reinvention, agentic AI represents what Alleg calls an "assisted" or "silent transformation. " "We are not trying to be experts on RACI system and agentic and how it works behind the table or beneath the table, " Alleg explained. "We are basically living this like if you get from dumb phone to smartphone. " This fundamental shift in approach explains why agentic AI is gaining traction where blockchain and metaverse initiatives stalled.

The timing reflects a long-standing imbalance in pharma digital investment. "Commercial operation and scientific affairs is often the last frontier of disruption," Alleg noted. "We like tech ops, right? Factories. Remember digital twins 15 years ago, 20 years ago, we like R&D in silico modeling and somehow commercial and scientific affairs were kind of left over. " While factories adopted digital twins two decades ago and R&D embraced in silico modeling, commercial operations and scientific affairs remained largely analog. That gap is now closing rapidly as agentic AI technologies mature and customer expectations evolve.

Alleg uses an automotive analogy to illustrate the transformation commercial teams will experience.

"Imagine your sales rep was in a manual gearbox car and he start to go in an automatic shifting gear car,"

he said. Sales representatives will retain control and responsibility, but mundane tasks, like Sunday night sales reports, will be automated seamlessly. The technology works beneath the surface, orchestrating multiple specialized agents to achieve goals without requiring users to understand the underlying architecture.

This assisted approach addresses a critical adoption barrier: commercial teams fear they must become software engineers to remain relevant. In reality, agentic AI integration resembles upgrading from a flip phone to a smartphone, new capabilities emerge through intuitive interfaces rather than technical training. The transformation happens gradually, allowing organizations to adapt without the disruption that doomed earlier technology initiatives. For pharma companies that experimented unsuccessfully with blockchain for supply chain transparency or metaverse platforms for medical education, this represents a fundamentally different value proposition. Rather than creating parallel digital ecosystems that require behavior change, agentic AI wraps around existing workflows, making current processes more efficient and effective.

From Frozen Assets to Liquid Intelligence

Despite decades of data acquisition investments, pharma companies struggle to operationalize their information assets. "Your data is frozen. It's not liquid, it's not interoperable, " Alleg stated. "And those new system, especially the agentic AI layer and a revolution can help you liquefy that data. " This "frozen data" problem, where information exists but remains inaccessible for real-time decision-making, represents both a vulnerability and an opportunity for companies willing to invest in the AI infrastructure needed to unlock these assets.

The solution involves creating what Alleg calls the "A to A model" , AI-to-AI interactions across healthcare ecosystems. "You're going to have an AI for the hospital that is going to shape the AI on the iPhone of your HCP and you're going to have your own AI that is going to do handshakes with all those microservices in real time, " he explained. This microservices architecture, already proven in financial services two decades ago, enables autonomous coordination between pharma companies, healthcare providers, and individual physicians without manual intervention. The technology isn't entirely new, but its application to healthcare is accelerating faster than many industry observers anticipated.

The practical impact is already visible in specific implementations.

"I've seen one startup that implemented AI. They had 10 years of data. It was a dermatology mole scanning app, " Alleg shared. "And thanks to AI, now they've been able to diagnose better than the clinical practice standard of care. "

This example demonstrates how accumulated historical data becomes valuable only when AI provides the analytical layer to extract insights that were always present but previously inaccessible.

Similar transformations are emerging across commercial operations. Alleg described marketing teams conducting live coding sessions with general managers, prototyping Power BI dashboards in two hours rather than enduring two-week procurement cycles.

Startups are conducting market research with "synthetic Personas" of healthcare professionals, eliminating traditional whiteroom sessions while enabling rapid scenario testing for pricing strategies. The interoperability dimension extends beyond internal operations to external partnerships. As pharma companies increasingly combine molecules with digital health services to generate real-world evidence and patientreported outcomes, the ability to exchange data seamlessly with partners becomes strategically critical. Companies that maintain siloed, frozen data architectures will face compounding disadvantages as AI-enabled competitors iterate faster on market intelligence and clinical insights.

Territory Economics and the Build-vs-Buy Imperative

General managers evaluating AI investments ask a straightforward question about return on investment. "If you worked with sales, imagine you have a GM and here asking you what's in it for me?" Alleg posed. "If I want to have to keep the same salesforce and I want to increase my sales, is AI going to change anything?" The answer lies in reframing AI's value proposition from cost reduction to revenue expansion through territory multiplication.

"If you had 200 HCPs in the call plan, maybe you'll have 2,000, " Alleg suggested. "The question will be okay if I'm selling this to a sales team that you'll have 2000 HCPs per rep. Am I getting the incentive on 2000 people territory?" This shift from 200 to 2,000 healthcare professionals per representative represents a 10x coverage improvement without headcount reduction. However, it requires rethinking compensation structures, call planning methodologies, and success metrics, organizational challenges that often prove more difficult than technology implementation.

The second critical strategic decision involves build-versus-buy approaches. Alleg's venture capital perspective reveals a striking reality about the pace of AI development. "I can tell you that if you build yourself any dollar invested in AI right now in three months, the value of your dollar or your pound is coming down very quickly because the technology is so fast that it's impossible almost to follow even for a venture capital firm, " he warned. This rapid depreciation makes proprietary AI development strategically inadvisable even for investment firms specializing in technology evaluation.

The recommended approach involves partnering with big technology companies making massive infrastructure investments while focusing internal resources on wrapping AI around existing high-value processes. Competitive differentiation shifts from AI technology itself to application design, process integration, and proprietary data curation. Alleg highlighted Astellas's sandbox model as an effective organizational approach, maintaining innovation teams "next to the mothership" that operate outside normal political constraints while preserving integration pathways for successful pilots. This structure enables rapid experimentation and failure learning without threatening core operations, then provides clear mechanisms for scaling validated innovations across the organization.

Implementation Roadmap and Workforce Implications

The practical path to agentic AI adoption begins with identifying specific pain points where technology can deliver immediate value. Compliance represents one high-impact use case that demonstrates how AI inverts traditional constraints.

"I've seen sales rep scared to take videos and send them by, you know, WhatsApp, messenger or whatever channel mail because they were scared of the compliance, they didn't understand the code of conduct, " Alleg recalled.

 "And some teams implemented agents to actually you push your video like if you talk to your grandmother, right. You push it to the chatbot, it will analyze the conversation and tell you you cannot speak about this. " This compliance-checking application inverts the traditional assumption that regulatory requirements constrain innovation, instead using AI to enable previously impossible engagement modalities.

The workforce implications diverge sharply from displacement fears that dominate public discourse about artificial intelligence. Alleg emphasized that every successful model maintains "a human in the loop" for insurance, legal, and quality assurance reasons. The technology isn't bulletproof, and organizations need personnel who can orchestrate AI systems rather than simply execute tasks. The transformation requires "smarter people" capable of managing expanded territories and interpreting AI-generated insights rather than larger teams performing repetitive manual work.

When asked directly whether AI will replace medical reps, Alleg was clear: "From every models we've seen, there will be always a human in the loop. " He explained that while Silicon Valley CEOs claim they'll replace four people with one using AI, the pharma reality looks different. "I see more. If you had 200 HCP in the call plan, maybe you'll have 2,000, " he said. The question becomes whether representatives receive appropriate incentives for managing dramatically expanded territories and whether they get help identifying where to spend their time on relationships that truly require face-to-face interaction versus digital engagement.

Success depends on four classical transformation pillars: people, culture, processes, and platforms. Implementation failures typically stem from organizational readiness rather than technology limitations. "If you have teams that are not scared or they're not scared for their own career to progress on this, then I think you're good to go, " Alleg explained. "If you start having teams that says, I need an AI project in my development plan, otherwise I don't move up, then there is a culture issue in the top. " Companies where teams fear AI will harm career progression face cultural obstacles that no technology can overcome.

Alleg noted that transformation must start from the boardroom, with executive commitment to supporting teams through the learning curve rather than punishing early failures. "It starts from the boardroom, " he emphasized, pointing to companies like Astellas that create sandbox environments where innovation can occur outside normal political pressures. When asked about success rates, Alleg defined pilot success through two metrics: adoption and scalability. "Can I go from one country to 15 countries for example, without any trouble? And can I get adoption from most of the departments?" The blockages remain the classical transformation challenges, ensuring goals and culture align internally rather than technical obstacles.

The pharma industry's commercial operations stand where manufacturing stood two decades ago, on the cusp of comprehensive digital transformation. The question isn't whether agentic AI will reshape customer engagement, but which companies will lead the transition and which will struggle to catch up as competitors leverage liquid data, AI-to-AI ecosystems, and multiplied territory coverage to capture market share. The companies that succeed will be those that recognize agentic AI as a silent transformation requiring organizational adaptation rather than technical mastery, that prioritize liquefying frozen data assets over acquiring new information, and that partner strategically rather than building proprietary solutions destined for rapid obsolescence.

In order to get you the highlights of Pharma Customer Engagement EU 25 faster, we are using generative AI technology to summarise the transcripts of the sessions. The conference organiser is checking the summary for accuracy. If you have any feedback about the summary, please contact Emma.Haslam@TR.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.

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