SPEAKERS:
Cedric Grand-Pierre, Head of Customer Engagement Excellence, Astellas
Saket Malhotra, Global Head of Commercial Digital, BioMarin
Alanna Peet, Managing Director, Commercial Consulting, Accenture
KEY TAKEAWAYS:
• Pharma companies are dismantling brand-centric planning for AI-powered real-time customer engagement
• Mindset transformation outweighs technical skills as the primary barrier to customer-focused operations
• Initial AI model accuracy ranges from 88-94%, requiring realistic expectations and iterative improvement
• Organizational silos remain the biggest obstacle, necessitating cross-functional teams spanning market access
• AI is reducing agency dependency and accelerating content creation while enabling continuous evolution
The pharma industry's customer engagement challenge isn't primarily technical—it's structural. Despite years of digital transformation investments, organizational silos continue fragmenting the customer experience across market access, medical affairs, and commercial functions. "Customers don't appreciate the silos. They don't appreciate the boundaries to be able to have the right data to be able to navigate apps using AI or GenAI to be able to access the new capabilities entirely," Alanna Peet explained at Pharma Customer Engagement USA.
The solution requires more than new technology platforms. "It's really about how do you deliver that integration and how do you engage with the customers based on everything that's happening around them," Cedric Grand-Pierre said.
From Brand Plans to Customer Journeys
Both Astellas and BioMarin are fundamentally restructuring how they approach commercial strategy, moving away from traditional annual brand planning toward continuous, data-driven customer engagement. This shift reflects broader industry recognition that field-based, campaign-oriented approaches no longer match how healthcare stakeholders want to interact with pharma companies.
"We are moving models from data maps supported by data...moving away from this defined brand plan, bringing common center innovation happening from moving away from almost universal approaches,"
Saket Malhotra said. The transformation extends beyond marketing tactics to fundamental operating models.
"A few years ago we were very much a very field-driven type of organization...the biggest changes are coming with the realization that pharma is evolving and how do we want to win in these new significant channels," Grand-Pierre noted.
Where pharma companies once developed annual brand plans executed through quarterly campaigns, leading organizations now deploy AI-powered systems that enable real-time optimization. "Nowadays when you're defining your target or when you do your segmentation, you don't need to use basic ways of targeting. You should use AI as part of your target window," Malhotra explained. This requires different data strategies, new measurement frameworks, and cross-functional collaboration that breaks down traditional departmental boundaries.
The shift has profound implications for how commercial teams operate. Rather than executing predetermined campaigns, teams must now respond dynamically to customer behavior and intent signals. This demands agility that traditional pharma organizational structures weren't designed to support. Both companies emphasized the importance of creating integrated teams that bring together market access, medical affairs, and commercial functions to deliver cohesive customer experiences.
AI Implementation Reality Check
While AI dominates industry conversations, successful implementation requires managing expectations and focusing on practical use cases rather than transformational promises. Both executives emphasized pilot-based approaches that deliver measurable business impact while building organizational capability.
"Day one your models are not going to be 100% accurate. Setting a very realistic expectation with your business partners will be very important—maybe 88% to 94% accuracy as you run these initial models,"
Malhotra said. This pragmatic approach contrasts sharply with vendor promises of immediate transformation. "I think the exploration process is critical—what you see, how quickly are you learning what the impact is, how that helps you create that intuitive experience," Grand-Pierre added.
The applications span content generation, dynamic targeting, customer journey orchestration, and sales enablement. Content creation workflows exemplify the transformation potential. "We used to work with creative agencies and now AI can create something through integration of the content. And then you can change the whole cycle of customer impact," Malhotra noted.
Tasks that previously required external creative agencies and weeks of production time can now be accelerated through AI-assisted generation, though compliance review requirements remain unchanged.
The technology enables continuous experimentation across analytics, customer-facing applications, and internal productivity tools. Organizations are testing AI for call report automation, marketing simulation, real-time campaign optimization, and patient support hub personalization. The focus remains on practical implementations that demonstrate value quickly rather than enterprise-wide transformation programs that promise revolutionary change but struggle to deliver measurable impact.
Both executives emphasized the importance of measuring business outcomes rather than productivity metrics alone. AI can accelerate many internal processes, but that efficiency doesn't automatically translate to better customer experiences or improved patient outcomes. Organizations must maintain discipline in distinguishing between internal operational improvements and genuine customer value creation.
The Talent Transformation Imperative
The most significant barrier to customer-centric transformation isn't technology platforms or data infrastructure—it's organizational mindset. Both pharma leaders identified cultural orientation as the primary challenge, noting that while technical skills exist within the industry, customer-focused thinking and cross-functional collaboration remain underdeveloped.
"The biggest change...is that mindset shift. I think that's number one—there's talent in industry, there's a lot, but not necessarily enough with the right mindset," Grand-Pierre said. This creates a dual talent challenge: recruiting external expertise from consumer and technology sectors while simultaneously developing existing pharma professionals.
The required capabilities extend beyond traditional brand management and therapeutic expertise. "You cannot have siloed digital vertical capabilities for data analysis. You really need to put your customer at the center and determine what type of skills you need," Malhotra explained.
Organizations need professionals who understand customer journey design, behavioral data interpretation, and agile methodologies—skills that weren't priorities in traditional pharma commercial organizations.
Both companies are establishing internal academies, webinars, and champion networks to accelerate capability development. The approach involves identifying champions from brand marketing teams, field teams, market access, and medical affairs who can advocate for new approaches and help bring broader teams along. This recognizes that transformation requires more than training programs—it demands cultural change that happens through demonstration and peer influence.
The talent challenge extends to partnership decisions. Organizations must evaluate technology vendors not just on platform capabilities but on their understanding of pharma regulatory requirements and commercial realities. The most sophisticated AI platform delivers little value if the vendor doesn't understand the compliance constraints that govern pharma customer engagement.
Continuous Evolution as Operating Model
The future of pharma customer engagement isn't a destination but a continuous evolution model. As AI capabilities improve through machine learning and organizational experience deepens, commercial systems will require ongoing enhancement rather than periodic upgrades.
"This is a bit like an operating system being upgraded all the time. Your brand is going to get new versions, your capabilities are going to be able to utilize different data and information,"
Peet said. This demands fundamental shifts in how pharma companies structure investments, measure success, and develop talent.
Traditional project-based funding models and discrete campaign lifecycles give way to platform thinking and perpetual optimization.
The measurement challenge becomes particularly acute. "Productivity may not have impact on customer outcomes," Peet noted. Distinguishing between internal productivity gains and actual customer outcome improvements requires discipline, especially when efficiency metrics are easier to quantify than engagement quality or patient experience.
Both executives emphasized the importance of starting small and learning fast. Rather than attempting comprehensive transformation programs, organizations should pursue focused pilots that demonstrate value and build organizational capability. This approach enables faster learning cycles and reduces the risk of large-scale implementations that fail to deliver expected returns.
The organizations that will win aren't necessarily those with the most advanced AI technology or largest data sets. Instead, competitive advantage will accrue to companies that successfully integrate cross-functional capabilities, maintain learning velocity, and keep customer needs rather than internal processes at the center of decision-making.
This requires leadership commitment to breaking down organizational silos and creating the agile operating models that customer-centric engagement demands.
Success also requires realistic expectations about implementation timelines and model accuracy. Organizations must resist the temptation to delay deployment until AI models achieve perfection. The learning that comes from real-world implementation—even with 88-94% accuracy—generates more value than extended development cycles that pursue theoretical perfection. This pragmatism over perfection mindset represents a significant cultural shift for an industry accustomed to extensive validation before launch.
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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.
