Introduction

SPEAKERS: 
Karishma Parikh, Head of Marketing and Strategic Engagement, Uro-Oncology, Ferring Pharmaceuticals 
Maya Schlessinger, Global Marketing Launch Lead, Pfizer 
Betsy Thorne, Senior Director, Market Access & Patient Support, Syneos Health (moderator) 

KEY TAKEAWAYS: 
• Patient-centric go-to-market strategy starts with a solid cross-functional understanding of the therapeutic area and treatment landscape. 
• Real-world data is now essential infrastructure for sustaining clinical relevance once competitors narrow a product's differentiation. 
• Co-creation with advocacy groups and patients should begin during Phase III trial design — not after launch. 
• Community oncology is where the access gap, and the opportunity for scientifically elevated field engagement, is greatest. 
• AI accelerates execution, but human judgment still determines whether that speed serves patients or just increases throughput. 
 
Many people living with cancer are older and treated in community settings, often far from academic centers and clinical trial sites so access to the latest innovations can be uneven, and real-world treatment patterns can lag the evolving standard of care. That reality framed a discussion between launch leaders from Ferring's uro-oncology franchise and Pfizer's global commercial oncology team at Pharma USA 2026. Their central point: the gap between a company's stated commitment to patients and what patients ultimately receive is rarely a values problem — it's a structural one, and it starts before a field representative ever enters a physician's office. “You can’t be a great external partner until you solidify that internally and ensure the right people are at the table from the start,” said Karishma Parikh, Head of Marketing and Strategic Engagement for Uro-Oncology at Ferring Pharmaceuticals. “That sequencing, internal before external, is the organizing logic of everything that follows.” 

Alignment Isn't Optional — It's Launch Velocity 
Pharma's biggest commercial execution challenge isn't a lack of patient insight, but systems that keep insight from being shared. “There’s this firewall usually between medical and commercial, which needs to have some flexibility and partnership,” Parikh said. The cost is not just cultural friction; it's launch velocity. When commercial teams are excluded from early strategy conversations, they inherit a positioning they didn't help build and market access constraints they were not prepared for. In an oncology category where two or three new entrants can appear in a single year, playing catch-up is not a recoverable position. 

Maya Schlessinger, Launch Lead at Pfizer Oncology, described her team's answer: cross-functional alignment that starts with shared questions rather than siloed deliverables. Her team consulted clinical experts before building content, so the materials answered physicians’ questions rather than ones the team assumed they had. “My theme is co-creation, not just internally with functions, but also externally, to bring in the experts and patient advocates too,” she said. 

The implication: if the materials physicians receive were built without their input, “patient-first” is a value statement layered onto a commercial system built on other priorities. Alignment across headquarters medical, market access, and commercial teams on strategic priorities is what determines whether patient-centricity is executable or decorative. 

Real-World Data as Competitive Infrastructure 
Clinical trial data has a shorter shelf life than most launch teams assume. A Phase III readout that defines a product's differentiation at launch can be largely outdated 18 months later, once a competitor publishes new data or real-world use reveals tolerability patterns the trial was not designed to capture. “A year later a competitor comes — is your pivotal clinical trial still relevant?” Schlessinger asked. Her team worked with community physicians early to understand what strong real-world study design looks like, so that by the time real-world data is released, the audience already has the vocabulary to evaluate it. 

Parikh added a regulatory dimension: the FDA's evolving approach to real-world evidence, combined with how NCCN guidelines interact with payer decision-making, is reshaping oncology's evidentiary hierarchy. Data that would have been inadmissible in a value conversation a few years ago is now informing formulary discussions and teams that built real-world programs early, with payer questions in mind, are starting from a structural advantage. 

There is a patient dimension too. Quality-of-life data, gathered from patients’ years into therapy, communicates something progression-free survival data can't: what it feels like to live on a long-term treatment. “A lot of these medicines, patients can be on for several years,” Schlessinger said. “Understanding the challenges, how patients are doing on this therapy versus those who are not and getting that information to physicians is really critical.” 

Community Access Is Where the Gap Lives 
Centers of excellence anchor most launch strategies, but they treat only a fraction of the patients who need these medicines. The community oncologist managing a dozen tumor types a day, often for rural patients who cannot travel to an academic center, is the physician whose prescribing behavior determines whether a therapy reaches its real patient population. 

Schlessinger described how her team responded to a community prescribing gap: partnering with local advocacy groups to understand what happens between diagnosis and treatment initiation, then building multilingual materials patients could review before their appointment. That reflected commitment made visible through operational investment, not a new promotional push. 

Parikh's argument for the field force was structural, not tactical. Community physicians are information-saturated and time-compressed, and the answer isn't simpler messaging — it's scientifically elevated field personnel. “These physicians are seeing sometimes dozens of different cancers at any given point,” she said. “If you’re able to deliver a succinct, impactful message, you keep that patient.” That means training reps to hold clinical conversations, not just deliver a sales pitch. 

AI Accelerates Execution — the Direction Is Still Human 
Pharma conversations about AI tend to swing between excitement and caution. This one was more grounded: AI as a multiplier of human activity, not a replacement for it. Schlessinger described using AI to prepare for customer calls, for reviewing prior conversation history and anticipating unresolved physician concerns. “Make AI work for you,” she said. “Keep making the prompts better.” 

Parikh raised a point most AI-in-pharma discussions miss, physicians and patients are using the same tools commercial teams are. A patient asking an AI tool about her treatment options is getting an answer, one pharma companies have limited visibility into and almost no influence over. It's a near-term marketing challenge with no established playbook, and companies that start thinking about it now, rather than treating AI purely as an internal efficiency tool, will have a structural advantage.  

Trust Is Earned Sequentially, Not Announced 
The session's throughline: trust in pharma is not a communications outcome, it's an operational one. Physicians trust companies that show up with real data, answer hard questions, and build materials with, not just for the people who'll use them. “Trust is built over time,” Schlessinger said. “One small slip can make it vanish.” 

Companies can't announce trust into existence; they can only build the conditions that make it possible: internal alignment, field force capability, real-world evidence infrastructure, and operational programs that remove friction between prescription and access. In oncology, where a community physician needs a credible answer in under five minutes and a rural patient over 65 may never reach a center of excellence, that's the difference between a campaign and a system — and between a patient who gets the standard of care and one who doesn't. 

To get you highlights of Pharma USA 2026 faster, we are using generative AI technology to summarize the transcripts of the sessions. If you have any feedback about the summary, please contact lucy.fisher@thomsonreuters.com.  

"Making sure that you tailor your governance to the appropriate risk level — just like every other business decision or tech investment you would make — is critical to ensuring that you don't get caught up in a morass of doing the same process for something that's super risky as for something that everyone would understand is not as risky."

A rep copilot surfacing pre-call planning information inside an existing Veeva workflow is not the same governance problem as an AI system synthesizing clinical trial data into brand strategy recommendations. Treating them identically wastes compliance resources and builds organizational resistance to the governance function itself.

Joshi formalized this into a three-tier framework at Takeda. Tier one covers low-risk, high-return tools — rep copilots, omnichannel copilots, market research assistants — where experimentation within existing ecosystems carries limited regulatory exposure. Tier two covers marketing automation and MLR processes, where legal and regulatory involvement becomes necessary. Tier three is the category every pharma company is racing toward: an AI system capable of synthesizing clinical trial data, conference presentations, market research, and transactional records into disease area strategy. "This is the most high, high risk area," Joshi said. "We don't have a real answer over there." The framework is useful precisely because it makes that uncertainty explicit rather than treating all three tiers as equivalent unknowns.

BMS has operationalized the time dimension of this calibration. "Gone are the days when you had unlimited funding to run pilots you wanted to run," Tantsyura said. "We condensed the timelines — before it used to be unlimited time; now you have to show the results within the three-month time period.

Leadership now is expecting that before we even start working on anything, we have very clearly defined value — not just slides, but demos — showing every two weeks what progress has been made." A hard pilot window with biweekly accountability replaces open-ended exploration. The three-month constraint is not primarily a cost control mechanism. It is a forcing function that surfaces adoption problems and compliance friction early enough to address them without sunk-cost pressure.

One governance actor most technology conversations still overlook is procurement. "Procurement is also having a seat at the table," Joshi said. "Procurement along with legal has been really driving conversation much earlier on in the journey of AI, so that you don't have to do expensive retrofitting of all this from a regulatory standpoint. That has been the big learning." Procurement's earlier entry into AI investment decisions is preventing downstream regulatory costs — but it also introduces a new friction point at the front of the pipeline that organizations haven't fully mapped into their governance timelines.

The Capability No One Named But Everyone Described

Dr. Salinas posed the question that crystallizes the regulatory specificity of the pharma context: pharma operates in a very different space, with strict regulatory oversight — how are organizations responsibly deploying AI while ensuring compliance and maintaining audit visibility? The session produced multiple frameworks in response. What it did not produce — because no panelist named it explicitly — is the capability that connects all of them.

The measurement dimension points toward it. "It's no longer about Gen AI insights," Liu said. "We need to measure also the activities that are taken upon those insights and then what's the outcome of that. That's the next level we're trying to get into." The people dimension confirms it. "Do we have the leadership alignment? Do we have the team operationally scalable? This is about your deployment, driving the change management and thinking through the end-to-end pull-through," Liu said. Both observations describe organizations that must hold multiple operational modes simultaneously, not transition from one to the next.

The capability is governance ambidexterity: the organizational capacity to run parallel governance tracks at different speeds and scrutiny levels for different risk tiers, without collapsing them into a single process. Tantsyura confirmed BMS is already doing this — federated governance for traditional AI, centralized cross-functional review for agentic AI, both live at the same time. Joshi described the "ambidextrous organization" in terms of operations versus experimentation. The evidence suggests the same structural demand applies to governance itself.

Liu's measurement evolution — tracking not just AI outputs but actions taken and outcomes produced — requires governance infrastructure that spans from content generation through field execution through business impact, across multiple compliance regimes simultaneously. That is a different organizational design problem than the one pharma solved when it first built centralized AI review boards.

The organizations advancing fastest are not the ones with the most mature governance model. They are the ones that can operate multiple governance models in parallel — applying proportionate scrutiny through risk-tiering, maintaining federated guardrails for known-risk categories, and restarting centralized review for emerging ones — without treating each new technology generation as a crisis that suspends everything else. The diagnostic question for any leadership team is not whether your governance is mature. It is whether your governance architecture can absorb the next generation before this one has finished scaling — and whether you have already started building the answer.

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.

Text

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.