SPEAKERS:
- Florent Hassen, Global Product Owner – AI & Omnichannel Advanced Analytics, Roche
- Phoebos Stergiou, Head of Product Management, Omnichannel Customer Engagement, Digital Innovation, Senior Director, Gilead Sciences
- Sameer Singla, Principal, Data & AI, Axtria
- Jeppe Guildford Manuel, Data Science External Affairs & Intelligence Director, Novo Nordisk (Moderator)
KEY TAKEAWAYS:
- Precision engagement failures are structural and organizational, not caused by insufficient technology
- Data latency limits the ability to predict in time before commercial teams can act on them
- Governance through decision systems is the durable cross-functional alignment mechanism
- Product owner accountability models separate tech and business decisions while creating a functional bridge between them
- HCP digital fatigue signals that volume-based engagement is being displaced by irreplaceable concierge-level value
Sameer Singla arrived at Pharma 2026 prepared to say something his clients rarely hear; " I think the industry currently is early to mid-maturity, and that's not a technical failure, that's not a fault line because of technology. It's a very structural limitation." What followed was a convergence that executives should find uncomfortable, as three organizations with radically different positions in the value chain; a global pharma product owner, a commercial engagement lead, and a consulting principal, independently arrived at the same diagnosis, then prescribed different fixes. The convergence in diagnosis paired with divergence in prescription is what makes the discussion strategically useful.
Florent Hassen, Global Product Owner for AI and Omnichannel Advanced Analytics at Roche, established the standard against which every subsequent observation was implicitly measured. Precision engagement, he argued, is "the total opposite" of the generic LinkedIn outreach everyone recognizes. "It is to make sure that every time you interact with someone you do it at scale and it's perceived by your customer as this interaction was unique." That definition is deceptively demanding. Scale and perceived uniqueness are in direct tension, resolving it requires organizational capability.
Governance Architecture Outlasts Steering Committees
The gap between a precision engagement pilot and a precision engagement capability is almost always an organizational design failure dressed up as a technology gap. Jeppe Guildford Manuel pressed the question directly: "It's easy to advocate for in theory, but it's a little bit harder to sustain. What is it that actually works and how do we ensure that that alignment continues over time, so it's not just a pilot?"
Phoebos Stergiou, Senior Director of Omnichannel Customer Engagement at Gilead Sciences, offered the sharpest structural answer. "I think we need to move away from trying to manage governance through meetings and trying to manage governance through decision systems, the repeatable ways in which companies make the decisions that matter." Steering committees create the appearance of alignment; decision systems encode alignment into how work actually gets done. Phoebos specified three co-dependent components: clear decision rights at every organizational level, shared definitions of what a good outcome looks like across departments, and executive sponsorship operating simultaneously at senior and execution levels. Executive sponsorship sits at the apex not as ceremonial endorsement, but because without it shared goals dissolve the moment departmental priorities diverge under quarterly pressure.
Florent described a different structural solution operating at the role level. At Roche, the product owner model creates a bridge function, someone with dual fluency who can translate business requirements into technical backlog and vice versa. "You need to make sure that indeed tech decisions are made by tech people, but business decisions are made by business people. And you need to at some point of time to have a convergence between both, but you need to have a clear accountability." When tech money flows directly to marketing or medical affairs without that accountability structure, Florent cautioned. The first instinct is vendor partnership and a new data silo, because no one is accountable for interoperability.
Sameer reinforced the architectural dimension from the enterprise level. "Don't look at your data & AI strategy as a commercial data strategy or R&D data strategy. First build an organizational data & AI strategy which is aligned with your organizational business goals, not IT goals. Then percolate it down. You cannot create it bottoms up. It has to be an executive top-down strategy." Three prescriptions of decision systems, product owner accountability and enterprise-first data architecture, operating at strategy, process, and role level respectively. The implication is that all three may be co-requisite rather than interchangeable.
Predictions That Arrive Too Late to Matter
Even when organizational design is sound, a second structural problem undermines the value of precision engagement investment: the predictions are accurate, but they are chronologically irrelevant. Sameer quantified the paradox precisely. "If I have to predict the outcome that this patient will withdraw from the therapy, the best I can do is look 90 days hence and say within 90 days what is the probability this patient will withdraw. But I'm using data which is already three months old to make that prediction. So, by the time I make that prediction, that clinical outcome has already happened in the past." The analytical models work but the intervention window has already closed. No amount of algorithmic refinement resolves a data latency problem structurally embedded in how claims and patient records move from healthcare systems to data providers.
Sameer identified a compounding failure: the most patient-centric asset organizations build is abandoned precisely when it becomes commercially actionable. "We build amazingly detailed patient journeys during pre-launch phase, we have a very good understanding of who our patient is, how do they come through the entire journey, but we nearly forget about them by the time we launch the product, and we never use them, or we hardly use them after launch. But that is the cornerstone of why we are in this market in the first place."
Pre-launch patient journey work is treated as a regulatory and medical exercise, handed off and filed. Post-launch commercial teams rebuild from scratch using narrower commercial data, never connecting to the clinical understanding that preceded them.
Phoebos's measurement framework addresses a related failure mode. "One of the things that often goes wrong is that we treat measurement as reporting, we just want to report what happened. Whereas the reality is that if we want to actually drive improvement, we want to measure not only what's happening, but really diagnose why it's happening." Her three-tier model covers experience metrics (relevance, respect, cadence), behavioral metrics (friction reduction versus volume), and organizational improvement metrics (execution effectiveness). The practical consequence for data strategy is clarifying: rather than accumulating data comprehensively, Gilead focuses on optimizing latency and quality for the specific data sets that will drive the decisions that matter. Measurement architecture precedes data architecture.
Discover more on this topic at Pharma Commercial Data & Tech Europe 2026 (4-5 November, London) Europe’s collaborative home for data and tech pioneers. Visit the website here.
HCP Fatigue Redefines the Value Proposition
The consumerization trend Jeppe described, where patients arriving at physician appointments with LLM-generated treatment preferences, prepared to seek another opinion if rebuffed, is reconfiguring who pharma needs to engage and what that engagement must deliver.
"If your company can bring some value to your customer beyond just an email campaign, then you are going to counterbalance the consumerization. But if your company, your tactics consist only to send 10 emails per month, then yes, you will be replaced," said Florent. The examples he cited are instructive: supporting an HCP navigating drug access for an asylum seeker, enabling a physician to organize community upskilling sessions for regional colleagues. Neither is a digital engagement play. Both are high-touch interventions that no AI-mediated channel can replicate, and both generate reciprocal loyalty that insulates a company from being disintermediated by Medscape, ChatGPT, or a competitor's next campaign.
Sameer extended the stakeholder map to capture the structural shift underneath the consumerization trend. Patients increasingly arrive at pharmacies before they go to HCPs, self-medicating or seeking first-line OTC alternatives informed by LLM queries. Engagement strategy now requires pharmacy-level touchpoints at the awareness stage, HCP coaching on handling patient-initiated objections, and patient-facing content early enough in the journey to shape the conversation before it reaches the clinic. Most pharma commercial models have not fully operationalized any of these three. Sameer noted the social dimension bluntly: in markets with weak prescription controls, LLM-driven self-medication is not merely a commercial challenge but a public health one, and the industry's engagement needs to be matched by policy intervention.
Florent surfaced a finding from Roche's real-time HCP feedback capture that reframes the competitive risk. "There is a digital fatigue from the HCPs. After COVID we have been doing a lot of webinars, a lot of remote meetings. They are bombarded with email campaigns and they just don't want to receive 50 emails per week. They just want sometimes to talk to a human." Engagement volume, measured as a proxy for engagement quality, is producing measurable deterioration in the customer relationship it is designed to strengthen.
The Structural Fix Is Organizational
The three prescriptions offered across the session, enterprise data governance, decision system architecture, product owner accountability, look like alternatives. They are not. Each addresses a different failure mode in the same organizational system.
Phoebos was direct about the sequencing: "A lot of the time we think that we fix the technology problem and then automatically we will improve the way we do precision engagement. But actually, in my opinion, it's more about people, process and technology, and in that right order of importance." Technology scales effective organizational design. It does not substitute for it.
Jeppe's closing provocation sharpened the boundary condition: are there barriers between R&D and commercial that need to be maintained? Florent's answer was instructive. At Roche, data domain lakes enforce access by organizational role, with workflow-governed permissions for cross-domain requests. The architecture does not eliminate boundaries; it makes them precise and navigable rather than blunt and impenetrable. Medical inquiry content stays within medical. Sales pricing data stays within its domain. But insights that can legitimately cross domains, clinical patient journey work, for instance, have a governed pathway to do so. Sameer's partial defense of silos acknowledged the CFO logic that created them: "The cost of a breach will overwhelm any additional cost of maintaining those silos." The problem is not that the logic was wrong. The problem is that it was applied without precision, producing blanket fragmentation where selective permeability would have served better.
Florent described a production-deployed pipeline at Roche that illustrates what the organizational architecture enables. HCP master data records are linked to public social media accounts via fuzzy matching. Real-time posting activity feeds into next best action content recommendations through natural language processing. If an HCP is publicly posting about a specific mechanism or molecule, the system surfaces relevant content for the next interaction. That capability exists because Roche built the data domain architecture, the product owner accountability model, and the cross-functional governance that makes it operational. The pipeline is the output of the organizational design.
Companies still treating precision engagement as a technology procurement question are solving for the wrong constraint. The question worth asking before the next planning cycle is not which AI platform to invest in, but whether the organizational architecture exists to make any prediction actionable before the clinical moment it was designed to address has already passed. For most organizations, that answer is currently no, and no model update will change it.
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Discover more on this topic at Pharma Commercial Data & Tech Europe 2026 (4-5 November, London) Europe’s collaborative home for data and tech pioneers. Visit the website here.
