SPEAKERS:
Danilo Pagano, VP Global Head of Customer Engagement Technology, Sandoz
Riccardo Calliano, VP Finance GenAI Commercial Investments, GSK
Kate Francis, VP Capabilities and Marketing, ViiV Healthcare
Cliff Walton, Director Digital & CRM Strategy, Syneos Health
KEY TAKEAWAYS:
• Only 5% of pharma GenAI projects successfully scale to demonstrate business value
• ViiV Healthcare's AI coaching assistants analyze physician calls in real-time without field accompaniment
• Sandoz's billion-patient base generates inquiry volumes positioning AI triage as high-impact entry point
• Regulatory ambiguity on AI-generated content accountability prevents dynamic omnichannel deployment
• Change management outweighs technical barriers as primary constraint on AI ROI
The pharma industry is drowning in AI pilots but starving for results. Research from MIT reveals a sobering reality: only 40% of GenAI projects even reach pilot stage, and a mere 5% successfully scale to demonstrate business value. "Out of all projects companies start in GenAI, only 40% make to pilot," Riccardo Calliano explained at Pharma Customer Engagement Europe 2025. "So they don't even reach the PoC and only 5% are scaled up and demonstrate value." At the London conference, executives from Sandoz, GSK, and ViiV Healthcare dissected why promising proof-of-concepts rarely escape the lab and what separates experimentation from enterprise impact.
"I see this about balancing automation and empathy," Cliff Walton noted. "So tech feels human and humans feel empowered."
The Compliance Paradox Blocking Dynamic Engagement
AI promises to transform pharma customer engagement from quarterly adjustment cycles to real-time personalization. Current omnichannel journeys take months to launch, analyze, and optimize, a rigid process that cannot respond to emerging physician behaviors or market signals.
"Imagine a kind of AI agent that can dynamically adjust these journeys," Danilo Pagano proposed. "Dynamically means I don't need months and I don't need to go through a very deep analysis of the insights, but within hours, days or hours, I can adjust the tone of my conversation."
AI agents could theoretically reshape content, swap channels, and customize narratives at individual levels within hours.
But this capability collides with an unresolved question: when AI dynamically combines modular content in ways humans never reviewed, who bears responsibility if the meaning changes or compliance is violated? "If something goes wrong, who is responsible for this?" Pagano asked. "If the AI combine things in a way that the meaning is different, someone has a point on this." The regulatory landscape remains ambiguous. Agencies like EMA and FDA have not provided clear guidance on AI-generated content accountability, leaving organizations to self-regulate in a high-stakes environment where enforcement actions could set precedents retroactively.
The infrastructure challenges compound the compliance uncertainty. Technology stacks remain siloed across CRM systems, marketing automation platforms, content management, and MLR processes, preventing the cross-channel optimization AI theoretically enables. "Is technology ready today to have a full AI implementation?" Pagano questioned. "From what I see, it's not. I mean it's still very much siloed, right? CRM is in one environment and then we have the old marketing automation tools and then we have the DAM MLR in another environment."
Organizations attempting dynamic AI deployment confront not just regulatory ambiguity but fundamental technology architecture limitations that prevent the seamless data flows AI requires. The result is a paradox. AI possesses technical capabilities to personalize engagement at unprecedented scale and speed, yet the combination of unclear accountability frameworks and fragmented systems constrains deployment to controlled experiments rather than production environments.
Enterprise Value Demands Strategic Discipline
The pharma industry has embraced AI experimentation with enthusiasm, running multiple tens of initiatives simultaneously across countries and achieving localized successes that do not translate to enterprise impact. But this scattershot approach consumes technical resources, change management capacity, and organizational attention without delivering ROI. The root cause is not technological but strategic. Most organizations lack capabilities to translate proof-of-concepts into production systems, a gap extending beyond technology to encompass business process redesign, user co-development, and what Calliano termed business translation skills bridging technical and commercial domains.
The waste is staggering.
"70% of the material created by organization never reaches HCPs," Calliano noted. "And I think that as pharma industry we have an obligation wherever we invest money that this money is invested in a productive way."
AI's promise lies not in creating more content faster but in ensuring the right messages reach the right patients through optimized resource allocation.
The solution requires a fundamental mindset shift. Rather than innovation-focused experimentation, scaling demands enterprise-grade thinking around infrastructure investment, compliance frameworks, cost management, and workflow embedding. "AI for me compared to traditional ERP project is essentially a co-development exercise where you're working with your customers to develop something that adds value for them," Calliano explained. Organizations that develop repeatable scale-up methodologies will compound advantages over time, as each successful deployment builds platform capabilities reducing cost and risk of subsequent initiatives.
The discipline extends to use case selection. High-impact applications combine clear business problems, measurable outcomes, contained scope, and alignment with existing processes. Organizations spreading resources across dozens of disconnected pilots sacrifice the focused effort required to achieve production quality. The companies successfully scaling AI share common characteristics: executive sponsorship ensuring cross-functional coordination, dedicated technical teams building reusable components, and ruthless prioritization concentrating resources on initiatives with enterprise-wide applicability rather than localized optimization.
Proven Applications Delivering Measurable Impact
Beyond the pilots, some organizations have achieved tangible results by focusing on contained use cases with clear ROI metrics. ViiV Healthcare deployed AI-powered coaching assistants that analyze recorded physician calls, providing field teams immediate feedback on question quality, personalization, and conversation effectiveness. "We use recording during calls and we use that as a coaching assistant," Kate Francis described. "And it's real time because the individual doesn't have to have someone with them assessing how they do." The system eliminates weeks-long reporting delays and removes the need for physical accompaniment during calls, though obtaining physician consent for recording requires different approaches than traditional field rides.
The impact extends beyond efficiency. AI's capability to process vast information volumes is accelerating medical education and enabling more sophisticated shared decision-making between physicians and patients. "You can go through 200,000 scientific publications in a matter of hours, things that used to take months," Francis noted. This acceleration proves particularly valuable in rapidly evolving therapeutic areas where evidence bases expand faster than human review capacity.
GSK embedded machine learning tools into commercial investment workflows across five markets, enabling finance, commercial, and insights teams to make real-time tactical adjustments. The system functions as decision support rather than autonomous decision-making, maintaining human oversight while dramatically improving analysis speed. This human-in-the-loop design addresses both compliance requirements and user acceptance challenges, allowing teams to build confidence in AI recommendations through transparent logic and override capabilities when business judgment suggests alternative approaches.
The transformational potential is particularly evident in HIV and infectious disease, where patient advocates play active roles in treatment decisions and healthcare equity gaps persist.
"If what we do is improve the optimization of the message, improve the efficiency of how that communication happens, that it is tailored and personalized to the customer, not just the HCP, but the customer we're dealing with, then we're going to improve overall healthcare,"
Francis emphasized. AI applications extending beyond physician engagement to patient education, adherence support, and community connection could address systematic barriers preventing marginalized populations from accessing care.
Human Barriers Constrain Technical Possibilities
Despite AI's technical sophistication, the binding constraint on pharma ROI is not algorithms, data, or compliance but change management. Organizations underestimate the human barriers including works council concerns, job displacement anxiety, discomfort with changing physician relationships, preference for perfection over iterative solutions, and resistance to new working methods. "I think the biggest bottleneck obviously is compliance tools, data mindset," Francis acknowledged. "I think people's mindset, if they're close to it, they're worried about it getting through works councils, just getting people to embrace something that's completely different and new."
Even technically successful deployments fail without addressing training needs, demonstrating short-term wins, and phasing implementation to avoid overwhelming teams with simultaneous initiatives. The anxiety around AI displacement, while understandable, may be premature. "This idea that AI is going to take away a job, we're going to disappear, then a bot or an agent, which is for me still a glorified bot, can take over our jobs is not a scenario that's going to happen in the next two, three, four years," Calliano assessed.
The hard work required to make AI functional and accurate ensures human expertise remains essential. Organizations transparently communicating AI's role as augmentation rather than replacement, involving users in co-development, and demonstrating productivity gains rather than headcount reduction will accelerate adoption and capture value faster than those treating AI as cost reduction mechanism.
Sandoz identified medical inquiry management as a high-impact AI entry point. With nearly a billion patients treated globally, the company faces massive inquiry volumes from patients and healthcare professionals across markets like the US, Germany, and Japan. "Today there is a big budget," Pagano noted, describing the current manual process requiring medical affairs personnel to answer within strict timeframes. AI triage could perform initial question categorization and solution matching, routing inquiries to appropriate medical personnel rather than forcing 48-hour waits through multiple channels.
"There is still a human element because the final decision is taken by a human," Pagano emphasized. "But AI can help a lot."
The session concluded with a provocative challenge cutting to the heart of AI's paradox in pharma engagement. "If everyone is creating more content faster, but the HCP's time is capped, are we all at risk of spamming doctors?" Walton questioned. The answer may lie in using AI for restraint as much as generation, determining when not to engage rather than maximizing touchpoint frequency. Organizations that combine AI personalization capabilities with content discipline and attention economics will likely achieve better outcomes than those simply accelerating production.
The path forward requires balancing competing imperatives. Organizations must experiment to discover high-value applications while maintaining discipline to scale proven use cases. They must leverage AI's speed while respecting regulatory processes ensuring patient safety. They must pursue efficiency gains while investing in change management ensuring adoption. The companies successfully navigating these tensions will separate themselves not through superior algorithms but through superior organizational capabilities translating technical possibilities into business results.
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Discover more on this topic at Pharma 2026 (22-24 April, Barcelona) - the global collaborative network for leading pharma innovators. Visit the website here.
