SPEAKERS:
- Erica Smith, VP, Value & Market Access, Chiesi
- Dicla Veliz Salce, IO Women's Cancer Lead, AstraZeneca
- Dilesh Doshi, VP, Global Health Economics and Outcomes Research, Acadia Pharmaceuticals
- Moderator: Michele Petruccelli, Partner, Wavestone
KEY TAKEAWAYS:
• Cross-functional insight sharing outweighs data sophistication as the real launch readiness differentiator
• Real-world evidence makes clinical trial outcomes prescriptively actionable, not just statistically significant
• Pre-defined pivot points prevent reactive drift without abandoning a sound core strategy
• AI is reshaping HCP access faster than most launch teams are building responses to it
• Narrative control now requires active social and AI surveillance, not just a scientific communication plan
Most launch teams believe their biggest problem is data quality. The panel at Pharma USA 2026 argued it's something harder to fix: data behavior. The question isn't what signals are available before day one - geomapping, claims platforms, natural history registries, social determinants overlays - it's whether the teams holding those signals are actually in the same room. "Fragmentation mutes impact in every way, every scenario," said Erica Smith, VP of Value and Market Access at Chiesi. That's the diagnosis most launch post-mortems eventually reach, usually eighteen months too late.
Leaders came together from rare disease, oncology, and specialty markets who have collectively launched products across the full spectrum of outcomes, from category-defining successes to what Dicla Veliz Salce called learning experiences she wouldn't trade. The panel, led by Michele Petruccelli, who leads Life Sciences initiatives at Wavestone, focused on patient‑centric commercialization, data‑driven launch strategy, and measurable outcomes. What emerged wasn't a framework. It was a reckoning with a sequential launch model that was already straining before AI accelerated its obsolescence.
Data silos are the launch failure nobody owns
The standard launch readiness narrative treats data as the asset. Build the evidence package, align the value story, deploy the field. The panelists here argued that sequence is where launches quietly fail. Evidence generation happens in one team. Claims analysis lives in another. Patient advocacy insights may sit with medical affairs. None of it moves fast enough, or together enough, to change what the rep says on Monday.
Dilesh Doshi, VP of Global HEOR at Acadia Pharmaceuticals, described a rare disease mandate that makes the discipline unusually concrete: "We're using data to not only make sure that we identify where the relatively smaller group of patient exists in this area, but also making sure that the providers and centers of excellence that are responsible for taking care of these patients have the right set of information on day one." In rare disease, patient populations are small enough that geomapping zip codes to treatment centers is operationally feasible given the right data sets. In broader indications, the same discipline is harder to enforce and rarer in practice.
Veliz Salce described a shift she has observed in oncology: commercial teams are now engaging Flatiron and similar claims platforms to inform launch planning, where historically that data lived with medical and access functions. The shift indicates progress. The destination is still integration, not just access. Understanding the treatment journey and current outcomes only helps, she noted, when the resulting insights cross team boundaries before the launch plan is locked.
Smith's framing cuts to the structural problem. It's not the data type that determines launch readiness. It's whether different teams are interpreting the same data together. She has seen launches where HEOR, commercial, payer, and equity teams each had their own data huddle and their own conclusions, then showed up to market with a fractured story. The fix isn't a better dashboard. Getting in the room, looking at each other's data, and sharing different perspectives - that's what she credits with producing strategies that hold.
Clinical data alone will not move a physician
The panel's sharpest consensus was on a failure mode that remains underappreciated: strong clinical trial results that don't change prescribing behavior. This isn't a messaging problem. It's an evidence translation problem.
Veliz Salce has lived this directly. Working in early breast cancer, she launched into a curative setting where hormonal therapies had been the standard of care for two decades. Physicians were comfortable. Patients were comfortable. A compelling hazard ratio wasn't enough to disrupt that inertia. "The moment when they see that this recurrence risk is real in real-world evidence, this is when they are willing to add a new therapy to their arsenal," she said. Clinical trial data defines the value proposition. Real-world evidence makes it credible to the person who has to prescribe it.
Doshi takes this further in rare disease, where patient populations are heterogeneous and clinical endpoints often abstract. His teams have embedded patient exit interviews and caregiver exit interviews into clinical trials, capturing the experiential layer above the efficacy scores. "Your loved one or your child might be able to participate in school activities some more," he said, describing how those interviews translate into the language physicians need for productive conversations with families. That texture doesn't come from a claims database. It comes from structured listening, early, before commercial pressures narrow the conversation to endpoints.
Smith adds a dimension most launch frameworks don't account for: the gap between what patients experience and what they tell their physicians. She described a pattern familiar to anyone with a family member managing a chronic condition - patients who withhold symptoms to avoid burdening their doctor. "If we can help physicians understand what their patients are thinking that they're not saying - how do we do that?" The answer she's piloting involves social determinants of health data, environmental factors, and community-level insights layered onto the clinical story. It points toward a physician conversation that's more complete than the one clinical data alone supports.
Discover more on this topic at Pharma Customer Engagement USA 2026 (October 27-28, Philadelphia) - where commercial, marketing, medical, data and AI pioneers converge. Explore the agenda here.
Strategy stability is not the same as rigidity
Petruccelli's question on strategy-versus-pivot discipline exposed one of the most operationally useful tensions of the session. Every launch team talks about agility. Very few have thought through the mechanics of when to use it.
Veliz Salce offered the clearest mental model: "I see a strategy as the destination, but just like Waze sometimes tells me you need to take a different path because there is a roadblock here." Route changes are not destination changes. The discipline is in distinguishing between signals that warrant a course correction and noise that triggers premature abandonment of a sound strategy.
Doshi pushed back on the instinct to pivot too fast, particularly at the regional level. The strategy set before launch typically reflects sound national-level evidence. What it often misses is execution variance at the state or health system level - a Medicaid prior authorization policy that's ambiguous, a field team undertrained on medical necessity pathways for rare disease products that may not receive formulary access for a longer period of time. "Having that cross-functional team at the regional level and giving them a little bit of autonomy to make that little course correction" is how one can manage this. Local teams with real-time prescription data and the authority to act on it, without triggering a national level strategic overhaul.
Smith's contribution is structural: pre-defined pivot points. Build the inspection moments into the launch design before going to market. Decide in advance that at month three you will evaluate payer readiness against original assumptions. This prevents the two failure modes that plague launches - reactive pivoting based on early noise, and stubborn course-holding when the data is actually telling you something real. Smith has experienced both. "We took it out to the market and found out that they were not anywhere close to the curve," she said of one access strategy. The pivot was necessary. What would have helped was designing the trip wire earlier.
AI is moving faster than the launch model it's replacing
The panel's AI discussion surfaced a more uncomfortable truth than most AI-at-launch conversations produce. The teams using AI to improve launch execution are largely doing productivity work - faster insight synthesis, virtual HCP training, real-time field intelligence. That's real. But the more significant AI dynamic may be happening on the other side of the physician relationship.
Smith named it directly: "We were spending so much time thinking about how we were going to use AI that we weren't thinking enough about how our customers were using AI." Payers are using AI to accelerate prior authorization reviews. Younger HCPs are using AI to answer clinical questions they would previously have asked an MSL. The traditional access model - rep, then MSL, then medical information - is being compressed at the front end by a generation of physicians who want a tailored answer in real time and won't wait twenty-four hours. Doshi's team is piloting portal-based AI responses to HCP inquiries, built on the recognition that physicians "want the response early and they want the response tailored to their question."
Veliz Salce has deployed AI as a virtual physician for field force training, feeding the model with clinical evidence, treatment guidelines, and segment-specific market research so reps can practice against realistic objections before they're in front of a real prescriber. The infrastructure investment is modest. The intellectual investment - deciding what the AI needs to know to simulate the right physician - is where the work is. "It's more the thinking behind to ensure that I feed the AI with the right information," she said.
The harder problem is narrative control. Doshi's argument here is the one launch leaders should carry back to their teams: "If you are not dynamic in real-time action, the narrative is going to be controlled by other people through social media, through AI, through the access to all of the information." Scientific communication plans were built for a world where the manufacturer controlled the pace of information flow. That world is gone. A competitor's off-label data, a caregiver forum's adverse event thread, an AI chatbot trained on unapproved sources - these now shape prescribing perception faster than a field force can respond. Veliz Salce pointed to the partnership implication: AI platforms currently serving HCPs lack access to approved, current product information, often by more than a year. Companies that move on strategic AI partnerships now will have a material advantage in the next launch cycle.
The questions that determine whether day one actually works
What the panel collectively described is the obsolescence of the sequential launch model. The old architecture ran in phases: evidence generation, then value story, then payer strategy, then field deployment. Each handoff lost fidelity. By the time insights reached the rep, they were months old and stripped of nuance.
The model companies are building toward is continuous and parallel. HEOR embedded in clinical development to capture patient voice before endpoints are locked. Regional cross-functional teams empowered to interpret real-time data and correct local execution. AI surveillance monitoring narrative formation in channels the field can't reach. Social determinants layered onto clinical data to fill the gap between what patients experience and what physicians hear.
Veliz Salce's challenge applies at the organizational level as much as the individual one: "We live in a world that is data rich but sometimes there are no actionable insights." The teams that close that gap - not by acquiring more data, but by asking sharper questions of the data they already have - are the ones whose day one actually becomes day one. Smith's provocation is the right place to start: "Ask this question: Who have we not listened to?" In pharma launches, the answer to that question almost always identifies the signal the launch plan is missing - and the patient population still waiting for access to a therapy that already exists.
To get you highlights of Pharma USA 2026 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.
Discover more on this topic at Pharma Customer Engagement USA 2026 (October 27-28, Philadelphia) - where commercial, marketing, medical, data and AI pioneers converge. Explore the agenda here.
