Introduction

SPEAKERS:

·       Gavin Outteridge, General Manager, Arysana

·       Aditi Tuteja, Global Head of Market Access Innovation, Specialty Care, Sanofi

KEY TAKEAWAYS:

·       HEOR and economic value narratives are often gated and unindexed, making them structurally invisible to LLMs

·       Sanofi teams report 50% less time on manual evidence work, redirected to strategic cross-functional orchestration

·       Integrated Evidence Generation Planning (IEGP) platforms are pharma's unrecognized architecture for AI-era information authority

·       At least one top 25 pharma has deployed unified evidence tooling enterprise-wide across all commercial and medical functions

·       Payer objection simulation before HTA submission is emerging as AI's highest-value downstream market access application

The Evidence AI Cannot See

At Reuters Events: Pharma USA, Gavin Outteridge framed the session's premise as a launch execution challenge, specifically the problem of dynamically linking messaging, evidence, and TPPs with AI enablement to accelerate how teams use the science they generate. That is how the session was positioned. The most consequential insight landed a leap further on.

That came from Aditi Tuteja, and it doubles down on the investment case for Integrated Evidence Generation Planning (IEGP) platforms. "The biggest barrier is our HEOR and economic value narratives are not even indexed today," Tuteja said. "They're behind the gate, so LLMs don't even see your structured data. You may have a great structured format, but LLMs are not even getting to it just yet."

The content pharma invests the most to produce, validated with the most scientific rigor, relied on most heavily for HTA submissions and payer negotiations, is precisely the content that AI systems mediating healthcare information cannot access. The governance practices that make HEOR evidence trustworthy are the same practices that render it invisible. This runs in both directions: the more carefully a company controls access to its economic value narratives, the more completely those narratives are excluded from the AI systems increasingly mediating how physicians, payers, and patients evaluate treatments.

Rigorous Evidence Loses to Freely Crawlable Content

Pharma's evidence governance was designed for a world where information moved through human intermediaries. MSLs carried dossiers. Account teams briefed payer committees. Peer-reviewed journals gated clinical claims. In that world, controlling access protected quality. In an AI-mediated information ecosystem, those same controls create structural exclusion. Less rigorous, freely available content fills the void in LLM training data and retrieval, shaping AI responses about drugs whose most authoritative science is locked behind institutional walls.

Tuteja identified the specific mechanism: "Having a consistent message across the organization, having the right data linkage, gives us the citable authority you need for even LLMs to process this information. So having consistency across the organization's value messages that go out, those authoritative linkages from evidence and the other resources that are relevant to represent our data in appropriate light, are so critical to actually compete in the LLM space." The LLM visibility problem cannot be delegated to a digital marketing team optimizing metadata. It requires the upstream evidence architecture that market access has been building for entirely different reasons.

Outteridge extended this to what patients actually experience. When someone searches for drug information through ChatGPT or Google Gemini, the response "should link back to the source evidence, the published, peer-reviewed evidence that underpins any information that comes out, whether it's the patient labels or PI and so on." That aspiration currently fails at the first step. LLMs cannot retrieve what they cannot index.

The failure compounds in a specific and dangerous direction. "Generative AI can still make stuff up, and we don't want patients getting made-up information," Outteridge said. When pharma's structured evidence is invisible and unstructured web content is not, hallucination risk concentrates exactly where it is most dangerous: in the therapeutic areas where manufacturers have produced the most rigorous science but failed to make it discoverable. Outteridge argued that solving schema, indexability, and authoritative linkage determines "whether what any AI-enabled tool is producing is trustworthy, it can be relied on for healthcare decision-making." The organizations that solve this first will not simply improve their AI search visibility. They will determine whether the AI-mediated information ecosystem for their therapeutic area is built on peer-reviewed science or on whatever happens to be freely crawlable.

The Internal Infrastructure That Holds the External Fix

The LLM visibility crisis cannot be addressed externally until the internal evidence architecture is unified. LLMs reward authoritative, consistently messaged, well-structured content. Siloed organizations cannot produce it.

Tuteja's diagnosis is direct: "Traditionally, we've functioned in our functional silos of HEOR, medical, and market access, often working in parallel but not on one living roadmap. And that has created a structured gap of no single source of truth." That gap is both an internal efficiency problem and the root cause of the external discoverability failure. Inconsistent messaging across functions means even evidence that is publicly available lacks the coherent signal that LLM ranking algorithms treat as authoritative.

The strategic reframe Tuteja offered rejects the narrow productivity argument entirely. "AI in HEOR is not just about faster literature reviews or faster analysis," she said. "It is about integrated evidence generation planning. That's really where the opportunity lies." Integrated evidence planning produces the cross-functional messaging consistency that AI systems can recognize as a credible signal. The infrastructure required to compete in AI-mediated search is the same infrastructure required to run a coherent launch.

Tuteja described this as a cultural transition: from organizations that collect data to organizations that orchestrate insights, enabled by AI systems that standardize evidence ecosystems across functions. The 50% metric she cited captures the efficiency dimension: "The teams are spending 50% less time on the manual evidence effort, but they are doing more strategic orchestration across their cross-functional partners." The headline number matters less than what it signals. Teams moving from execution to orchestration are building precisely the cross-functional alignment that unified evidence architecture demands.

Outteridge provided the scale validation that moves this from theory to operational reality. "We now have one top-25 pharma enterprise-wide, all functions, commercial strategy, MPP, medical, HEOR, market access, working Arysana’s ATLAS – an example of a commercially-available, pharma-specific Integrated Evidence Generation Planning (IEGP) tool - for one consistent set of messaging and evidence generation planning." Enterprise-wide deployment across all commercial and medical functions is the organizational condition that makes authoritative, consistently messaged external content possible. The internal platform story and the external LLM authority story are, architecturally, the same story.

Citability as Infrastructure

Citability is an engineering requirement, not a documentation habit. Outteridge described Arysana's approach: every AI-generated suggestion in the ATLAS IEGP tool is designed to be "citable, referenced from the publications that it's drawing on." The company is operationalizing this through formal academic validation, with a poster published at ISPOR Europe in November and a second submitted to ISPOR Global in May. The healthcare-specific standard Outteridge articulated is unambiguous: "In healthcare in particular, you have to do the extra work to go the extra mile to make sure that what any AI-enabled tool is producing is trustworthy, it can be relied on for healthcare decision-making."

That trust infrastructure produces measurable competitive advantage downstream. Tuteja noted that Sanofi teams have become "more prepared for taking on early dialogue with HTA bodies. The more we have payer packages, the value proposition story ready to interrogate, we are able to simulate the payer objections and get those answers faster before the submission." Payer objection simulation before submission generates structured, evidence-linked content of exactly the type that external AI systems would need to surface accurate drug information to payers now conducting their own AI-assisted research. The output of internal HTA preparation is, latently, external AI-ready content. Almost no organization has begun to treat it that way.

The Dual-Use Architecture Pharma Hasn't Named Yet

Tuteja offered the benchmark statement of where Sanofi currently stands: "We've moved from AI as an experiment towards AI as a default infrastructure." That transition, from pilot to embedded operating model, is what separates organizations building durable capability from those running proof-of-concept exercises indefinitely.

Outteridge anchored the efficiency investment to its ultimate accountability metric: "Speeding and improving how quickly you can get this product and the evidence in support of it to the patients that need it." Every efficiency gain, every HTA preparation improvement, every reduction in manual evidence effort is meaningful only insofar as it compresses the distance between clinical science and the patients for whom it was generated.

The original insight this session surfaces, is that pharma's Integrated Evidence Generation Planning (IEGP) platforms are being built to solve a launch efficiency problem, but they also contain the architectural foundation for a second and arguably more significant challenge: establishing AI-era information authority in an ecosystem where LLMs are already mediating how physicians, payers, and patients form judgments about drugs. The two use cases share the same requirements: unified messaging, authoritative source linkage, cross-functional consistency, and validated citability. Building for one builds for both.

The organizations investing in evidence governance infrastructure today are inadvertently making the right bet for an AI-mediated future, provided they eventually recognize what they have built and act on it before competitors do. The gating problem will require deliberate resolution: structured data standards, schema design for indexability, and decisions about what evidence can be made discoverable without compromising competitive position or regulatory compliance. None of that is simple. But the prerequisite, a unified, consistently messaged, citable internal evidence architecture, is already being assembled at the most operationally advanced companies in the industry.

The question for every HEOR, market access, and commercial leader reading this is whether their organization will recognize the dual-use potential of what they are already building, or whether they will continue treating evidence governance as a launch efficiency project and discover too late that they built the right foundation and failed to use it. The organizations that extend their internal evidence architecture toward external discoverability before the next product cycle will set the terms for how LLMs represent their therapeutic areas. That window is open now. It will not stay open indefinitely.

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.

"There are now levers to pull at the clinical trial design phase to incorporate endpoints that matter, potentially external comparators that matter to payers, even if they're not going to be submitted for regulatory approval. Bring that perspective forward."

Manufacturers waiting for institutional alignment between regulatory and reimbursement frameworks will wait indefinitely. The bridge must be built from the manufacturer's side, and it must begin in trial design.

Evidence Architecture Is Now a Pre-Launch Competitive Moat

The evidence package that clears regulatory review and the evidence package that secures appropriate payer access have different authors, different timelines, and increasingly different contents. Treating trial design as a regulatory exercise produces evidence that arrives at the payer's desk already insufficient.

Adam Hathorn, Head, National Accounts, US Market Access, Sanofi, put the commercial timeline in concrete terms: "Our customers look at products that are coming to market really early on. It could be 12 months, could be 18 months, sometimes even before if it's a disease state that will be a newer disease state to them." That window means payer evidence needs are functionally a Phase 2 design input. The unmet need narrative, the demographic alignment to specific payer populations, and the shape of the forthcoming value story must be legible to payers before pivotal data exists—not assembled from it afterward.

This is where the concept of a living evidence ecosystem becomes operationally meaningful rather than aspirational. Lott argued that "the evidence should be dynamic and it should be evergreen and it should be responsive to the changes in market demands or access constraints that are popping up in real time... payer narratives can be dynamically developed over time and things that were once discrete in time become really living things." The static value dossier—assembled at launch, updated annually, submitted as a finished document—is the wrong unit of analysis. In a market where the IRA, PBM structural reform, and competitive biosimilar entry can shift a payer's formulary calculus within a single quarter, evidence that cannot adapt is evidence that decays.

Wright reinforced what specificity actually requires in practice: the data package lands better when it uses numbers the payer already uses, reflecting their own population rather than a generalized trial cohort. That reframes evidence from a broadcast document into a co-created instrument—one built with the payer's own denominators and defensible in their own internal review processes.

Hathorn cited simultaneous active variables that make static planning untenable: PBM reform shifting from a rebate model to a net sales model by 2028, IRA impacts shaping payer forecasting through 2027 and 2028, and Medicaid channel exposure from pending legislation. These are not future scenarios to monitor. They are current inputs to evidence strategy. Organizations that can generate payer-population-specific analyses inside the pre-launch window are structurally advantaged over those presenting generic value dossiers, and the distinction compounds over time.

Cell and Gene Therapy for Common Diseases Breaks the Model

The industry's CGT pipeline is no longer confined to rare diseases with small, well-defined patient populations. Common conditions are now in scope, and that shift breaks the evidence model that has supported drug access for decades.

Lott stated the methodological challenge without softening it: "We're not talking about rare diseases now. We're moving into an era of CGT for common diseases... we have to move beyond the average treatment effect that we're estimating as part of a routine clinical trial to these individual treatment effects.  That's the holy grail." This is a category shift, not an incremental refinement. The average treatment effect estimated from a controlled trial cannot answer the question a payer must now ask: which specific patient, in my specific population, will benefit enough from a one-time, high-cost therapy to justify this expenditure—particularly if they may switch plans before long-term benefit materializes? Actuarial caution in the absence of individual-level evidence is not irrational. It is the predictable consequence of asking population-level methods to support individual-level coverage decisions.

Likewise, generalizing clinical trial results to payer-relevant populations is another emerging opportunity. Lott described a capability that would have been implausible three years ago: "You would never come to a payer and be like, hey guess what, I got an external control on this that's really reflective of your patients. Or by the way, that's actually using your data, right? And these are your patients and this is how it's stacking up. And it's not perfect, but it's a step further than what we were used to." The "not perfect" acknowledgment matters. The question is whether it is directionally accurate enough to support coverage decisions, and whether the relationship infrastructure exists to have that conversation honestly.

Hathorn confirmed that payer-data partnerships create value extending beyond the manufacturer-payer relationship:

"When you have a partnership with a payer and you are using their data, it is super powerful... it also helps them to justify when they're talking to their customers."

Payers face downstream accountability to employers, unions, and plan members for every formulary decision. Evidence built on their own population data gives them a defensible rationale for their own customers, creating a mutual interest alignment that rebate negotiations alone cannot produce.

That alignment depends on a foundation Hathorn described in operational terms: "Not all your data is going to be perfect. And so you have to acknowledge that and be upfront about that... the more trust you have and the more understanding that you're both trying to achieve the same end goal, then that can really smooth out a lot of the friction." Transparency about data limitations is not a concession. It is the mechanism by which imperfect but directionally valuable evidence earns enough credibility to influence decisions. In a domain where individual treatment effect models carry irreducible uncertainty, the relationship equity to be believed despite that uncertainty is itself a form of competitive infrastructure.

The Internal Prerequisite Most Organizations Haven't Met

The external evidence challenge has an internal prerequisite that most large pharma organizations have not cleared: their own commercial analytics and HEOR teams are producing divergent answers from the same data. Before the industry can build living evidence ecosystems for payer customers, it must first achieve internal coherence.

Lott named the organizational failure precisely: "At a lot of pharmaceutical companies, your commercial data science and analytics team is separate from your HEOR or your real-world evidence team... I've seen agentic AI act as a single source of truth and a harmonization mechanism that really democratizes how we derive insights from data so that really everyone's on the same page inside of an organization." The internal silo is not primarily a technology problem—the databases often overlap. It is a query and governance problem. Agentic AI that normalizes how questions are asked and answered across functions creates the organizational foundation on which all external evidence communication depends. Without it, manufacturers risk presenting payer customers with analyses that contradict their own internal modeling.

Wright surfaced a second internal constraint operating simultaneously: portfolio triage at the field level. National account teams carrying multiple brands must allocate limited customer-facing time against competing internal priorities, and the evidence asset that matters most to a regional payer may be irrelevant to a PBM managing national formulary decisions. Hathorn was direct about the consequence: "You have to be able to say back to the brand, like, you know, I'm sorry, this is not that big of a priority for my customer. We'll do what we can do to establish the access that you need, but we have other priorities that we need to work on."

Lott also flagged a policy variable that belongs inside scenario planning but is frequently absent from it. CMS Coverage with Evidence Development decisions—historically applied to devices—are emerging as an anticipated mechanism for high-cost therapeutics, potentially generating post-approval evidence requirements resembling clinical trial protocols in scope and burden. Market access teams modeling only regulatory approval and commercial formulary access are missing a third evidence obligation that is not hypothetical.

From Deliverable to Infrastructure

The panelists described, without explicitly naming it, a category shift in what "evidence" means for market access. Evidence is moving from a deliverable—the static value dossier assembled at approval—to an infrastructure: a continuously operating system that links trial data to real-world data, generates payer-population-specific analyses on demand, and updates as policy and competitive conditions shift.

Lott pointed to a research model that illustrates where this is heading. At Stanford's Center for Digital Health, a team published work in Nature using networks of agentic AI to run drug development processes end-to-end, from modeling through candidate identification. The implication for market access is not that AI replaces account teams. It is that hybrid teams—human account directors paired with AI agents handling data synthesis, scenario modeling, and population-level analysis—are already technically feasible and will become operationally standard. "I want to see hybrid teams of humans and agents as the years progress," Lott said. "Some people are already doing it."

Hathorn offered a commercially candid recalibration of where real-world evidence actually sits in payer decisions: "I don't know that it would outweigh it. I think it complements it... you could be talking to a PBM or a GPO of a PBM and, you know, it's pretty much financial. But you could be talking to a regional payer and it's really impactful." The assumption that superior evidence translates uniformly into superior access is wrong. Evidence infrastructure investments must be calibrated to the account mix—weighted toward payer types where population-level data and trust relationships can move formulary decisions, and paired with financial contracting where they cannot.

The diagnostic question for market access leadership is not whether the organization has a value dossier. It is whether the organization can link pivotal trial data to real-world claims at the patient level after trial exit, generate a payer-population-specific external control analysis within the pre-launch planning cycle using the payer's own data denominators, and produce the same answer from HEOR and commercial analytics when querying the same underlying database. For organizations approaching cell and gene therapy in common disease markets, a no on any of these is not a capability gap to close post-launch. It is the access risk that is already accumulating.

This content reflects the personal views of the speakers and is not intended to represent the official position of their respective companies.

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.

"Trust is built over time," Schlessinger said. "One small slip can make it vanish."

That asymmetry — slow to build, fast to lose — has a structural implication that the session's full argument demands: brand teams cannot manage trust. They can only create the conditions in which trust becomes possible. Those conditions are internal alignment, field force capability, real-world evidence infrastructure, and operational programs that remove friction between prescription and patient access.

The organizations that understand this are not building trust campaigns. They are building systems. In oncology, where a community physician managing 30 tumor types needs a succinct, scientifically credible answer in under five minutes, and where a rural patient over 65 may never reach a center of excellence, the difference between a campaign and a system is the difference between a patient who gets the standard of care and one who doesn't. That outcome is determined by decisions made in cross-functional strategy meetings, field training programs, and logistics contracts — not in the brand messaging document.

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.

"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.

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Strategic Focus Starts with Choice

The prescription Riordan and Stamm offer — limit focus to one to three critical launch drivers — sounds simple in a conference presentation and is operationally brutal in practice. Not because identifying the top drivers is analytically difficult. Because maintaining that focus through a twelve-to-eighteen-month planning cycle, against continuous internal pressure to re-expand scope, requires a form of organizational discipline that most pharma companies have not built and many have not attempted.

"The organizations that win launches," Riordan argued, "are the ones that pick the one or two or three things that are truly critical and resist the pressure to treat everything as equally important." The resistance is the hard part. Analytical prioritization is a workshop exercise. Organizational prioritization is a sustained political act.

What the survey of pharma executives finding about HCP gravitational pull actually reveals is that most organizations are not prioritizing at all. They are listing. A launch plan that identifies fifteen strategic priorities has not made strategic choices. It has documented everything the organization intends to do and labeled it strategy. The discipline Riordan and Stamm are prescribing is the discipline to look at that list and remove twelve items from it, knowing that the functions responsible for those items will push back.

What really matters the most is permission. Someone with sufficient authority must be willing to say that a given activity is not happening, and must be willing to hold that position when someone asks again. That kind of protected prioritization is culturally foreign in large pharma organizations, where cross-functional consensus is both a regulatory necessity and a deeply embedded operating norm. Stamm observed that the launches in Inizio's benchmark that most consistently outperformed were distinguished not by superior resources or more sophisticated analytics, but by clearer decision rights. Someone was actually empowered to hold the line on scope.

Governance That Makes Saying No Stick

Prioritization without governance has a half-life measured in weeks. The governance structures Riordan and Stamm advocate are not oversight mechanisms. They are decision-forcing mechanisms. The distinction matters. Oversight governance asks whether the launch is on track. Decision-forcing governance asks what should stop, given what the team now knows. The second question is structurally harder, because stopping an activity that has already been resourced and socialized requires overriding prior decisions, which in most organizations requires consensus that rarely forms fast enough to be useful.

Stamm's implementation framework incorporates agile rhythms not as a software methodology import but as a structural solution to this problem. Regular, time-bounded review cycles require teams to make explicit choices about what continues and what does not. The agile cadence creates a forcing function: instead of activities persisting by default until someone makes the case to stop them, they require active renewal. The burden of proof inverts. That inversion is modest in description and significant in practice. It is the organizational mechanism that keeps prioritization from decaying back into comprehensiveness.

What deserves naming directly, is why organizations resist these structures. Decision-forcing governance redistributes power. Comprehensive planning environments favor consensus-builders: people who can align large cross-functional groups, accommodate diverse stakeholder interests, and produce plans that reflect everyone's input. Prioritized planning environments favor decision-makers: people willing to hold a position under pressure and absorb the organizational friction of saying no. Those are different skills, rewarded differently in different cultures. The shift from one operating mode to the other is a cultural change that governance changes can support but cannot substitute for.

The practical diagnostic is simple enough that any launch team can apply it immediately. Count the stated priorities in your current launch plan. If the number exceeds three, you have not prioritized. You have listed. The operational difference between a list and a strategy is what you have chosen to sacrifice. Most launch plans contain the former and describe it as the latter.

Every organization that has ever underperformed a launch had a strategy. Most of them had too many. Importantly, these findings are based on proprietary benchmark analysis, and should be interpreted as directional insights rather than definitive conclusions.

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.