Every pharma commercial function is racing to deploy agentic AI - but the gap between the pilot and the production build is where most programmes stall. This keynote goes beyond the hype to confront the uncomfortable truth that the technology isn’t the easy part.
- Move from pilot to production by learning how to size human-in-the-loop correctly across a multi-agent architecture before it becomes your adoption bottleneck.
- Close the expectation gap by benchmarking your governance and data-quality readiness against organisations already running agentic AI at scale.Â
- As pricing pressure shifts cost and risk toward patients, what does that mean for patient engagement—and for access itself? Explore how engagement becomes as critical as reimbursement in determining uptake and outcomes
- With European launches becoming harder to make the math work, can today's go-to-market models keep up? Examine whether traditional launch models can scale sustainably amid tighter budgets and rising complexity
- With innovation globalizing and geopolitics growing less stable, what role will Europe play in the decade ahead? Assess how China's emergence as a biopharma innovation powerhouse challenges assumptions about where innovation originates and scales
Every commercial AI programme promises better customer engagement - and needs to prove ROI to keep its funding. Leaders closest to live rep, marketing and omnichannel AI deployments separate genuine commercial impact from vendor noise, and confront the trust gap that's quietly capping adoption in the field.
Â
Close the field-trust gap before it kills adoption to determine whether reps and customer-facing teams actually use an AI tool, or quietly route around it.
Interrogate the impact before you believe it - the specific, evidence-based questions that separate a real engagement improvement from an inflated case study.
Hosted by a Reuters journalist in accordance with the Reuters Trust Principles.Â
- Drive cross-functional excellence by dismantling silos through cross-functional insights and unified platforms that enable empowered teams to act faster
- Outpace competitors by strategically investing in AI tools at critical inflection points to automate workflows and accelerate time-to-market for innovation
- Patients and physicians are already asking frontier models before they ask a doctor - and if your evidence isn't visible to the models, it doesn't exist in that conversation. This panel tackles pharma's next commercial AI frontier: Generative Engine Optimisation.
- Get ahead of the next AI arms race by understanding what it takes to make your clinical and product evidence visible and accurate inside the LLMs your customers already trust.
- Learn from the first real case studies separating genuine GEO capability from agency hype, so you know exactly what to ask for before you buy.Â
- Redesign the operating model around AI, industrialising what today sits in scattered pilots (roles, workflow and governance rebuilt, not tools bolted on)
- Move past efficiency to new capability, using agentic workflows and a shared content-and-data engine to do what the old model couldn't
- Build organisational alignment and shared principles so the new operating model becomes institutional practice, not just a pilot success story that teams work around "
- Identify market access, reimbursement and stakeholder risks before launch by pressure-testing evidence plans, value propositions and engagement strategies early in development
- Improve commercial outcomes by aligning pre-launch marketing, payer expectations and evidence generation activities around the factors most likely to influence adoption and uptake
Cecilia von Ahn Scheel, Chief Transformation and Launch Excellence Officer, Ferrer
- Separate genuine AI value from adoption vanity metrics with credible measures such as accelerated cost reductions and improved commercial performance
- Demonstrate where expected returns failed to materialise and pinpoint what separated high-value AI deployments from those that failed to deliver
Elliot Antrobus-Holder, Global Head of Digital, Innovation & AI, Gilead Sciences
- Create the commercial environment where field and marketing pilots compound rather than compete for platform and IT resources - a shared customer data layer, orchestrated rep, digital and event channels, and AI across field, content, targeting and engagement, with unit economics for rep productivity and customer engagement that hold at production volume
- Develop a standardised commercial playbook for graduating pilots into enterprise scale - clear criteria for rep adoption and customer impact readiness, repeatable field and marketing on-ramp processes, predictable timelines so your sales enablement and marketing ops teams know the path before they startÂ
- Optimise market access, pricing and launch strategies with AI-powered models that evaluate multiple regulatory, competitive and market scenarios
- Anticipate disruption, strengthen portfolio decisions and allocate resources more effectively through predictive scenario planning and faster strategic adaptation
- Expose why most pharma AI ROI claims collapse under scrutiny: companies measure adoption (pilots completed, users trained) instead of business impact, because they don't have clean data to measure from in the first place
- Learn what separates the pharma AI programmes that deliver measurable outcomes from the 80% that stall after pilots - the answer is always data readiness, not AI sophistication
- Design AI as the rep's own tool, not a manager's dashboard, so the field adopts it rather than works around it
- Clear the MLR and approval bottleneck that strands working pilots, with the risk-tiered review path that unblocked it
- Roll out in stages by team readiness (Rome wasn't built in a day), running old and new side by side so no one works without a net
- Strengthen launch readiness by bringing commercial, medical and market access teams together early enough to influence product positioning, stakeholder engagement and adoption strategies
- Translate early cross-functional alignment into practical tools with launch-readiness data, payer-evidence, and market-research capabilities s in place 24 to 36 months pre-launch
- Pinpoint the uptake and reimbursement barriers most likely to erode product value, and see how teams are using real-world evidence and payer insight to test them pre launch
- Set success criteria, implement in controlled settings and choose projects with reasonable scope and clear ROI potential to provide clear performance metrics for leadership evaluation
- Challenge where pharma should place its biggest bets across medical, commercial, and enterprise operations as well as where investment should stop.
- Apply a scorecard to real use cases across medical, commercial and enterprise operations, and leave with a reusable template that links pilot metrics to budget decisions
- Set the commercial requirements for clean customer data and partner with data-tech to fix it at source, rather than inheriting whatever the analytics layer serves up
- Make the foundations-first case in commercial terms, tying clean data to a targeting or revenue outcome leadership already wants
- Monitor data quality as a commercial metric, not just an IT scorecard, so leadership sees it as enabler of growth "
- Identify, prioritise and engage the stakeholders that shape product perception, reimbursement and uptake to strengthen support by developing structured engagement strategies across policymakers, payers, healthcare professionals and patient groups
- Align evidence, value messaging and advocacy efforts around the groups that have the greatest influence on funding and adoption decisionsÂ
- Show how centralised data governance, single customer record, and enterprise AI platforms enabled your commercial team to target smarter, personalise faster, and measure ROI accurately as they have access to the full customer picture
- Expose what broke when data stayed siloed between field systems, marketing automation, and medical affairs and what changed when the enterprise data layer worked correctly
- Act on one unified HCP record across medical and marketing, coordinating sequence and suppression so channels reinforce instead of collide
- Sequence the build for usable value in the first months, ahead of the multi-year single-view programme
- Establish clear stewardship and governance so the unified record stays current and trusted as medical and marketing strategies evolve, preventing drift back into silos
- Learn the value of when commercial teams have access to unified customer intelligence, clean prescriber data, and real-time engagement metrics versus fragmented data and delayed insights
- Show the infrastructure (customer data platforms, real-time analytics, orchestration engines) that turns enterprise data investments into immediate commercial capability
- Settle whether marketing still owns omnichannel or the mandate has moved to data and AI (who holds the budget and the roadmap)
- Draw where the customer passes compliantly between medical and commercial hands
- Assign the orchestration that keeps the journey connected when no single function owns it end to end
- Refine evidence strategies and cross-functional planning to navigate JCA challenges and protect launch successÂ
- Close the evidence gaps, silos and assumptions most associated with unsuccessful assessments by aligning medical and commercial teams, and the RWE capabilities needed to address them before launch
- Define the evidence, comparator and outcome measures needed to strengthen future JCA submissions, and build them into development and launch plans ahead of submission
- Connect consumer signals, predictive insight and next-best actions to the choices made across commercial and marketing teams
- Redesign commercial decision making around where AI should recommend, automate or act versus where teams can still offer differentiated value
- Discover which next-best actions are driving measurable commercial impact today, the KPIs they influence most, and the strategic decisions teams continue to keep human-ledÂ
- Show what a developed field delivers that automation can't: trusted access, local judgement, the relationships that move share
- Build the rep-centric transformation as a deliberate play on patient-centric and HCP-centric, with the capability plan behind it
- Separate real resistance from noise, co-designing change so tools get used, not routed around
- Identify which products, markets and patient populations should be prioritised when international reference pricing and reimbursement uncertainty threaten traditional launch sequencing models
- Compare the strategic trade-offs between delayed launches, restricted indications, alternative access pathways and market selection decisions to protect both revenue and patient access
- Put next-best-action and account insight in the rep's hands before every call, so prep becomes a briefed conversation
- Strengthen thin European data with enrichment so account plans hold, instead of waiting for a perfect dataset
- Move from siloed rep tools to a unified intelligence layer that compounds rep productivity, not adds tool complexityÂ
- Define which real-world endpoints and data sources payers will accept as the basis for outcomes-linked payment, and how to secure access to that data
- Build a patient outcomes measurement framework by identifying the patient-reported outcomes that can serve as payment triggers, and the evidence collection strategies needed to make them credible to payers
- Prove access is working in practice, not just on paper: what post-approval data shows real uptake and removes restrictions, and how that evidence feeds back into the next agreement
- Replace fragmented AI pilots with an enterprise approach through established platforms that enables AI to scale across all business functions rather than each building out their own model
- Build guardrails that enable rather than block scale through the recognition that every use case does not require the same level of risk, compliance and security controls.
- Balance innovation and oversight through risk-based governance frameworks that accelerate AI adoption without compromising compliance
- Get your evidence into the AI layer the customer reaches first, so the answer they trust carries your story
- Close the gap where paywalled evidence never reaches the model, the reason your best data is invisible to the tool your customer uses
- Build an open-access evidence content strategy, not a crisis response to LLM hallucinations
- Anticipate shifting reimbursement expectations by understanding whether emerging HTA frameworks are beginning to challenge traditional benchmarks such as NICE in shaping evidence and value requirements
- Future-proof evidence strategies by adapting health economic models, trial endpoints and value frameworks to meet the evolving demands of diverse global markets without increasing complexity or duplication
- Discover what separates successful platform implementations from failed ones - the infrastructure, governance, and organisational changes that drive engagement improvement
- Learn how to integrate qualitative patient insights with quantitative data, navigate compliance ambiguity that stops teams from sharing, and build data-driven capability in a function built on empathy, not statistics
- Get cited by influencing the wider source graph (third-party, reference and clinical sources), not on-site SEO
- Monitor and correct what the models say about your brand, within the rules, with a correction route that holds
- Build citation governance at scale: systematic influence of source graphs rather than one-off corrections
- Increase evidence credibility with structured prospective studies that convert fragmented patient data into robust, decision-ready insights
- Discover how a global pharma organisation strengthened regulatory confidence, improved evidence consistency, and secured stronger support for access and value discussions through a protocol-led approach
Build once, engage everywhere: The CDP and orchestration architecture that works across AI and human touchpoints
- Show the unified engagement architecture that requires unified customer data (patient + HCP), real-time journey insight, cross-channel personalisation, and governance that prevents message conflicts
- Expose what breaks when you try to stitch together point solutions versus building on a unified platform from the start
- Become the source the model cites, so the patient and HCP who trust the machine inherit that trust in your evidence
- Give every AI answer a named owner across marketing, medical and patient teams, so accuracy holds as models update
-Catch and correct a wrong answer before it spreads, with a route that moves at model speed, not a quarterly review
- Evaluate the benefits and limitations of country-specific versus regional evidence approaches to determine optimal strategies for different therapeutic areas and markets
- Scale insights and eliminate duplicated efforts by establishing frameworks that leverage regional collaboration while preserving local market relevance
-Get data that travels with data platforms, evidence repositories and interoperable data standards let RWE studies, PROs, registry data and HTA dossier components be reused across marketsÂ
- Evaluate how pharma companies are digitally enabling KOL/investigator engagement, emphasising seamless collaboration and 360-degree transparency across all engagements to avoid losing out to competitors through poor experience
- Expose what fragmentation costs: HCPs are receiving conflicting information, brand credibility undermined, engagement failing to convert
- Show how a single approved-content and interaction record lets commercial, medical and field teams give HCPs consistent answers across every channel, including AI-generated ones
- Build the global claim core once, then re-evidence it for each market's HTA and payer bar, so adaptation is deliberate not discovered at rejection
- Give local the payer-language latitude where credibility is decided, holding the core intact everywhere else
- Design the global-local decision framework upfront, not market-by-market during claim development
- Engineer content as modular components with a tagging taxonomy and DAM structure any market assembles without rebuilding
- Close the reuse loop by measuring which components each market uses, turning blind output into managed supply
- Reduce go-to-market timelines for new markets or indications by deploying pre-vetted, pre-compliant component libraries instead of rebuilding from scratch
- Accelerate HTA submissions, reimbursement dossiers and evidence reviews by automating time-intensive research, drafting and analysis tasks, allowing teams to deliver more with existing resources
- Shift expert capacity from administrative activities to strategic decision-making, improving market access readiness while maintaining the quality and rigour required by regulators and payersÂ
- Judge content on relevance to the HCP, not volume shipped, now AI can produce it without limit
- Steer production by real usage signal: shift investment toward the content HCPs use, and stop making what lands nowhere
- Rebalance who decides and who produces, now both global and local can generate infinitely
- Move beyond measuring MSL interactions and scientific exchanges by tracking their influence on prescribing decisions, treatment pathways and clinical practice.
- Create a unified medical impact framework that connects MSL insights, scientific exchange and real-world evidence, enabling field teams to track and communicate the indicators that matter most to patients, healthcare systems and leadership
-Prioritise the initiatives that matter most by linking medical activities to measurable changes in physician behaviour, patient outcomes and product adoption.Â
- Name the real blockers to connecting commercial, medical and market access data: who owns it, who funds it, and where architecture still fails
- Decide which silos to break, which to protect and where shared data can unlock cross-functional decisions and enterprise AI value.
- Develop an AI-ready operating model that connects launch, evidence and HCP insights to support faster, more informed decision-making
- Tell the story of how one organisation mapped roles, skills and team design (which roles to grow, which to wind down, which are new) and what transitions are underway now
- Show the work of building a fully AI-native commercial org: how you're focusing the team on work that moves the needle and handing the rest to AI
- Expose what's changing beyond org design: partner models, channel structure, go-to-market shifts that need to start this year to position for 2030
- Move beyond static segmentation by using behavioural, engagement and clinical insights to identify which stakeholders require attention, what information they need and when engagement will have the greatest impact
- Improve field force productivity by replacing broad engagement strategies with tailored interactions that strengthen relationships, increase relevance and support better decision-making
- Show how fragmented data sources, poor data quality, and inconsistent governance force you to delay or redesign your RWE programme
- Expose the gap: you had the money for RWE, the protocol was solid, but your data infrastructure couldn't deliver the consistency, traceability, and governance that regulators demanded for regulatory-grade evidence
- Build the data spine first: one customer record and the consent and identity plumbing everything else needs
- Layer AI on top: the models, guardrails and human-in-the-loop that make it usable in a regulated setting
- Sequence investment so each layer compounds rather than resets, maturing the stack toward 2030Â
- Expose where commercial and medical affairs each miss what AI assistants tell HCPs and patients about therapeutic value, and agree who monitors, who responds and where the compliance line sits.
- Share field and MSL insight, AI-answer monitoring and approved scientific evidence across the firewall without breaching it, so both functions correct the same record.Â
- Show what data readiness means for regulatory-grade RWE data quality standards, governance frameworks, integration requirements, traceability, and validation that most pharma companies underestimate until they commit to an RWE programme
- Expose the hidden costs and delays when data infrastructure isn't built for RWE: retrofitting data quality, auditing lineage, adding governance controls, integrating legacy systems
- Map the shift in customer behaviour starting today - understand where your AI-first customer is already moving, so engagement strategy and value positioning aren't built for yesterday's model
- Plan for the customer who arrives already briefed, redesigning engagement for a conversation that starts after the research is done
- Prepare to differentiate in a category with 10 or 12 competitors, building the value story that holds when the molecule alone no longer separates you
- Start investing now in engagement infrastructure (content, evidence, messaging) that will matter in an AI-mediated customer relationship
- Use KOL insight and influence to prioritise launch messaging, field focus and external engagement, and agree which commercial decisions it can inform
- Leverage KOL relationships to generate actionable insights that strengthen evidence generation, shape educational programmes and drive smarter commercial decisions throughout the product lifecycle.
- Track each KOL insight from capture, to evidence or education change, to commercial decision
- Learn how to combine RWE, HTA requirement data, and competitive pricing intelligence into models that predict which markets will reimburse rather than relying on organisational optimism
- Discover the data infrastructure required to support reimbursement planning at scale such as data governance, analytics platforms, and integration across RWE, pricing, and payer intelligence
- Leave with a reimbursement probability framework that helps identify market access risks early, prioritise evidence investments and optimise launch sequencing across markets.
The Joint Clinical Assessment (JCA) is reshaping how the clinical value of new medicines is assessed across the EU. As the first assessments emerge, questions are arising around evidence expectations, the management of uncertainty, and how the framework accommodates areas where traditional clinical trial data may be limited.
This panel will examine what the first wave of assessments reveals about the JCA process, its implications for health technology developers, and what may need to evolve to support timely and equitable patient access across Europe.
Â
- Examine what the first JCA reports reveal about evidence expectations, areas of uncertainty, and the strategic implications for health technology developers, patient access and European competitiveness.
- Explore where greater flexibility in evidence requirements may be needed to reflect the realities of developing medicines in complex therapeutic areas, including rare diseasesÂ
- Compound commercial performance over time by transforming CRM from a basic interface into an intelligence system that captures and leverages organisational memory as a strategic asset
- Drive context-aware engagement and automate commercial workflows at scale through AI agents that leverage collective data and intelligence
Most-favoured-nation pricing has become the single biggest variable in European launch planning. With a third of companies already reshuffling their global launch order, this panel unpacks how commercial leaders are prioritising portfolios, resourcing launches, and messaging to HCPs and payers when the sequencing itself is in flux.
- Build your brand-prioritisation case by learning how peer commercial teams are deciding which assets get early European investment and which get deferred, when launch order is no longer a given.
- Protect your forecasting and resourcing plans by understanding how leading marketing and commercial teams are adapting go-to-market timelines, field deployment, and HCP engagement strategy to a launch sequence that can shift mid-cycle.Â
Hosted by a Reuters journalist in accordance with the Reuters Trust Principles.Â
- Scientific innovation is accelerating — so why does it not reach patients reliably and in time? Exploring what's holding progress back and what it takes to remove those barriers.
- Rewire the way medicines are developed and delivered — by disease, by design, and across the health ecosystem — to turn structural change into smarter decisions, faster approvals, and accelerated impact for patients.
Real-world evidence built for one European market rarely travels cleanly to the next. As Joint Clinical Assessment pushes toward a harmonised evidence standard, this panel confronts the practical barrier underneath it: uneven data quality, access, and infrastructure across member states
Â
- Get ahead of Joint Clinical Assessment by understanding where your evidence base is strongest - and weakest - across different member states before submission.
- Turn fragmentation into strategy by learning which collaboration and data-collection models are already bridging the gap between Europe's most and least data-mature markets.Â
Every pharma commercial function is racing to deploy agentic AI - but the gap between the pilot and the production build is where most programmes stall. This keynote goes beyond the hype to confront the uncomfortable truth that the technology isn’t the easy part.
- Move from pilot to production by learning how to size human-in-the-loop correctly across a multi-agent architecture before it becomes your adoption bottleneck.
- Close the expectation gap by benchmarking your governance and data-quality readiness against organisations already running agentic AI at scale.Â
- As pricing pressure shifts cost and risk toward patients, what does that mean for patient engagement—and for access itself? Explore how engagement becomes as critical as reimbursement in determining uptake and outcomes
- With European launches becoming harder to make the math work, can today's go-to-market models keep up? Examine whether traditional launch models can scale sustainably amid tighter budgets and rising complexity
- With innovation globalizing and geopolitics growing less stable, what role will Europe play in the decade ahead? Assess how China's emergence as a biopharma innovation powerhouse challenges assumptions about where innovation originates and scales
Every commercial AI programme promises better customer engagement - and needs to prove ROI to keep its funding. Leaders closest to live rep, marketing and omnichannel AI deployments separate genuine commercial impact from vendor noise, and confront the trust gap that's quietly capping adoption in the field.
Â
Close the field-trust gap before it kills adoption to determine whether reps and customer-facing teams actually use an AI tool, or quietly route around it.
Interrogate the impact before you believe it - the specific, evidence-based questions that separate a real engagement improvement from an inflated case study.
Hosted by a Reuters journalist in accordance with the Reuters Trust Principles.Â
- Drive cross-functional excellence by dismantling silos through cross-functional insights and unified platforms that enable empowered teams to act faster
- Outpace competitors by strategically investing in AI tools at critical inflection points to automate workflows and accelerate time-to-market for innovation
- Patients and physicians are already asking frontier models before they ask a doctor - and if your evidence isn't visible to the models, it doesn't exist in that conversation. This panel tackles pharma's next commercial AI frontier: Generative Engine Optimisation.
- Get ahead of the next AI arms race by understanding what it takes to make your clinical and product evidence visible and accurate inside the LLMs your customers already trust.
- Learn from the first real case studies separating genuine GEO capability from agency hype, so you know exactly what to ask for before you buy.Â
The Joint Clinical Assessment (JCA) is reshaping how the clinical value of new medicines is assessed across the EU. As the first assessments emerge, questions are arising around evidence expectations, the management of uncertainty, and how the framework accommodates areas where traditional clinical trial data may be limited.
This panel will examine what the first wave of assessments reveals about the JCA process, its implications for health technology developers, and what may need to evolve to support timely and equitable patient access across Europe.
Â
- Examine what the first JCA reports reveal about evidence expectations, areas of uncertainty, and the strategic implications for health technology developers, patient access and European competitiveness.
- Explore where greater flexibility in evidence requirements may be needed to reflect the realities of developing medicines in complex therapeutic areas, including rare diseasesÂ
- Compound commercial performance over time by transforming CRM from a basic interface into an intelligence system that captures and leverages organisational memory as a strategic asset
- Drive context-aware engagement and automate commercial workflows at scale through AI agents that leverage collective data and intelligence
Most-favoured-nation pricing has become the single biggest variable in European launch planning. With a third of companies already reshuffling their global launch order, this panel unpacks how commercial leaders are prioritising portfolios, resourcing launches, and messaging to HCPs and payers when the sequencing itself is in flux.
- Build your brand-prioritisation case by learning how peer commercial teams are deciding which assets get early European investment and which get deferred, when launch order is no longer a given.
- Protect your forecasting and resourcing plans by understanding how leading marketing and commercial teams are adapting go-to-market timelines, field deployment, and HCP engagement strategy to a launch sequence that can shift mid-cycle.Â
Hosted by a Reuters journalist in accordance with the Reuters Trust Principles.Â
- Scientific innovation is accelerating — so why does it not reach patients reliably and in time? Exploring what's holding progress back and what it takes to remove those barriers.
- Rewire the way medicines are developed and delivered — by disease, by design, and across the health ecosystem — to turn structural change into smarter decisions, faster approvals, and accelerated impact for patients.
Real-world evidence built for one European market rarely travels cleanly to the next. As Joint Clinical Assessment pushes toward a harmonised evidence standard, this panel confronts the practical barrier underneath it: uneven data quality, access, and infrastructure across member states
Â
- Get ahead of Joint Clinical Assessment by understanding where your evidence base is strongest - and weakest - across different member states before submission.
- Turn fragmentation into strategy by learning which collaboration and data-collection models are already bridging the gap between Europe's most and least data-mature markets.Â
- Redesign the operating model around AI, industrialising what today sits in scattered pilots (roles, workflow and governance rebuilt, not tools bolted on)
- Move past efficiency to new capability, using agentic workflows and a shared content-and-data engine to do what the old model couldn't
- Build organisational alignment and shared principles so the new operating model becomes institutional practice, not just a pilot success story that teams work around "
- Create the commercial environment where field and marketing pilots compound rather than compete for platform and IT resources - a shared customer data layer, orchestrated rep, digital and event channels, and AI across field, content, targeting and engagement, with unit economics for rep productivity and customer engagement that hold at production volume
- Develop a standardised commercial playbook for graduating pilots into enterprise scale - clear criteria for rep adoption and customer impact readiness, repeatable field and marketing on-ramp processes, predictable timelines so your sales enablement and marketing ops teams know the path before they startÂ
- Design AI as the rep's own tool, not a manager's dashboard, so the field adopts it rather than works around it
- Clear the MLR and approval bottleneck that strands working pilots, with the risk-tiered review path that unblocked it
- Roll out in stages by team readiness (Rome wasn't built in a day), running old and new side by side so no one works without a net
- Set the commercial requirements for clean customer data and partner with data-tech to fix it at source, rather than inheriting whatever the analytics layer serves up
- Make the foundations-first case in commercial terms, tying clean data to a targeting or revenue outcome leadership already wants
- Monitor data quality as a commercial metric, not just an IT scorecard, so leadership sees it as enabler of growth "
- Act on one unified HCP record across medical and marketing, coordinating sequence and suppression so channels reinforce instead of collide
- Sequence the build for usable value in the first months, ahead of the multi-year single-view programme
- Establish clear stewardship and governance so the unified record stays current and trusted as medical and marketing strategies evolve, preventing drift back into silos
- Settle whether marketing still owns omnichannel or the mandate has moved to data and AI (who holds the budget and the roadmap)
- Draw where the customer passes compliantly between medical and commercial hands
- Assign the orchestration that keeps the journey connected when no single function owns it end to end
- Show what a developed field delivers that automation can't: trusted access, local judgement, the relationships that move share
- Build the rep-centric transformation as a deliberate play on patient-centric and HCP-centric, with the capability plan behind it
- Separate real resistance from noise, co-designing change so tools get used, not routed around
- Put next-best-action and account insight in the rep's hands before every call, so prep becomes a briefed conversation
- Strengthen thin European data with enrichment so account plans hold, instead of waiting for a perfect dataset
- Move from siloed rep tools to a unified intelligence layer that compounds rep productivity, not adds tool complexityÂ
- Get your evidence into the AI layer the customer reaches first, so the answer they trust carries your story
- Close the gap where paywalled evidence never reaches the model, the reason your best data is invisible to the tool your customer uses
- Build an open-access evidence content strategy, not a crisis response to LLM hallucinations
- Get cited by influencing the wider source graph (third-party, reference and clinical sources), not on-site SEO
- Monitor and correct what the models say about your brand, within the rules, with a correction route that holds
- Build citation governance at scale: systematic influence of source graphs rather than one-off corrections
- Become the source the model cites, so the patient and HCP who trust the machine inherit that trust in your evidence
- Give every AI answer a named owner across marketing, medical and patient teams, so accuracy holds as models update
-Catch and correct a wrong answer before it spreads, with a route that moves at model speed, not a quarterly review
- Build the global claim core once, then re-evidence it for each market's HTA and payer bar, so adaptation is deliberate not discovered at rejection
- Give local the payer-language latitude where credibility is decided, holding the core intact everywhere else
- Design the global-local decision framework upfront, not market-by-market during claim development
- Engineer content as modular components with a tagging taxonomy and DAM structure any market assembles without rebuilding
- Close the reuse loop by measuring which components each market uses, turning blind output into managed supply
- Reduce go-to-market timelines for new markets or indications by deploying pre-vetted, pre-compliant component libraries instead of rebuilding from scratch
- Judge content on relevance to the HCP, not volume shipped, now AI can produce it without limit
- Steer production by real usage signal: shift investment toward the content HCPs use, and stop making what lands nowhere
- Rebalance who decides and who produces, now both global and local can generate infinitely
- Tell the story of how one organisation mapped roles, skills and team design (which roles to grow, which to wind down, which are new) and what transitions are underway now
- Show the work of building a fully AI-native commercial org: how you're focusing the team on work that moves the needle and handing the rest to AI
- Expose what's changing beyond org design: partner models, channel structure, go-to-market shifts that need to start this year to position for 2030
- Build the data spine first: one customer record and the consent and identity plumbing everything else needs
- Layer AI on top: the models, guardrails and human-in-the-loop that make it usable in a regulated setting
- Sequence investment so each layer compounds rather than resets, maturing the stack toward 2030Â
- Map the shift in customer behaviour starting today - understand where your AI-first customer is already moving, so engagement strategy and value positioning aren't built for yesterday's model
- Plan for the customer who arrives already briefed, redesigning engagement for a conversation that starts after the research is done
- Prepare to differentiate in a category with 10 or 12 competitors, building the value story that holds when the molecule alone no longer separates you
- Start investing now in engagement infrastructure (content, evidence, messaging) that will matter in an AI-mediated customer relationship
- Identify market access, reimbursement and stakeholder risks before launch by pressure-testing evidence plans, value propositions and engagement strategies early in development
- Improve commercial outcomes by aligning pre-launch marketing, payer expectations and evidence generation activities around the factors most likely to influence adoption and uptake
Cecilia von Ahn Scheel, Chief Transformation and Launch Excellence Officer, Ferrer
- Optimise market access, pricing and launch strategies with AI-powered models that evaluate multiple regulatory, competitive and market scenarios
- Anticipate disruption, strengthen portfolio decisions and allocate resources more effectively through predictive scenario planning and faster strategic adaptation
- Strengthen launch readiness by bringing commercial, medical and market access teams together early enough to influence product positioning, stakeholder engagement and adoption strategies
- Translate early cross-functional alignment into practical tools with launch-readiness data, payer-evidence, and market-research capabilities s in place 24 to 36 months pre-launch
- Pinpoint the uptake and reimbursement barriers most likely to erode product value, and see how teams are using real-world evidence and payer insight to test them pre launch
- Identify, prioritise and engage the stakeholders that shape product perception, reimbursement and uptake to strengthen support by developing structured engagement strategies across policymakers, payers, healthcare professionals and patient groups
- Align evidence, value messaging and advocacy efforts around the groups that have the greatest influence on funding and adoption decisionsÂ
- Refine evidence strategies and cross-functional planning to navigate JCA challenges and protect launch successÂ
- Close the evidence gaps, silos and assumptions most associated with unsuccessful assessments by aligning medical and commercial teams, and the RWE capabilities needed to address them before launch
- Define the evidence, comparator and outcome measures needed to strengthen future JCA submissions, and build them into development and launch plans ahead of submission
- Identify which products, markets and patient populations should be prioritised when international reference pricing and reimbursement uncertainty threaten traditional launch sequencing models
- Compare the strategic trade-offs between delayed launches, restricted indications, alternative access pathways and market selection decisions to protect both revenue and patient access
- Define which real-world endpoints and data sources payers will accept as the basis for outcomes-linked payment, and how to secure access to that data
- Build a patient outcomes measurement framework by identifying the patient-reported outcomes that can serve as payment triggers, and the evidence collection strategies needed to make them credible to payers
- Prove access is working in practice, not just on paper: what post-approval data shows real uptake and removes restrictions, and how that evidence feeds back into the next agreement
- Anticipate shifting reimbursement expectations by understanding whether emerging HTA frameworks are beginning to challenge traditional benchmarks such as NICE in shaping evidence and value requirements
- Future-proof evidence strategies by adapting health economic models, trial endpoints and value frameworks to meet the evolving demands of diverse global markets without increasing complexity or duplication
- Increase evidence credibility with structured prospective studies that convert fragmented patient data into robust, decision-ready insights
- Discover how a global pharma organisation strengthened regulatory confidence, improved evidence consistency, and secured stronger support for access and value discussions through a protocol-led approach
- Evaluate the benefits and limitations of country-specific versus regional evidence approaches to determine optimal strategies for different therapeutic areas and markets
- Scale insights and eliminate duplicated efforts by establishing frameworks that leverage regional collaboration while preserving local market relevance
-Get data that travels with data platforms, evidence repositories and interoperable data standards let RWE studies, PROs, registry data and HTA dossier components be reused across marketsÂ
- Accelerate HTA submissions, reimbursement dossiers and evidence reviews by automating time-intensive research, drafting and analysis tasks, allowing teams to deliver more with existing resources
- Shift expert capacity from administrative activities to strategic decision-making, improving market access readiness while maintaining the quality and rigour required by regulators and payersÂ
- Move beyond measuring MSL interactions and scientific exchanges by tracking their influence on prescribing decisions, treatment pathways and clinical practice.
- Create a unified medical impact framework that connects MSL insights, scientific exchange and real-world evidence, enabling field teams to track and communicate the indicators that matter most to patients, healthcare systems and leadership
-Prioritise the initiatives that matter most by linking medical activities to measurable changes in physician behaviour, patient outcomes and product adoption.Â
- Move beyond static segmentation by using behavioural, engagement and clinical insights to identify which stakeholders require attention, what information they need and when engagement will have the greatest impact
- Improve field force productivity by replacing broad engagement strategies with tailored interactions that strengthen relationships, increase relevance and support better decision-making
- Expose where commercial and medical affairs each miss what AI assistants tell HCPs and patients about therapeutic value, and agree who monitors, who responds and where the compliance line sits.
- Share field and MSL insight, AI-answer monitoring and approved scientific evidence across the firewall without breaching it, so both functions correct the same record.Â
- Use KOL insight and influence to prioritise launch messaging, field focus and external engagement, and agree which commercial decisions it can inform
- Leverage KOL relationships to generate actionable insights that strengthen evidence generation, shape educational programmes and drive smarter commercial decisions throughout the product lifecycle.
- Track each KOL insight from capture, to evidence or education change, to commercial decision
- Separate genuine AI value from adoption vanity metrics with credible measures such as accelerated cost reductions and improved commercial performance
- Demonstrate where expected returns failed to materialise and pinpoint what separated high-value AI deployments from those that failed to deliver
Elliot Antrobus-Holder, Global Head of Digital, Innovation & AI, Gilead Sciences
- Expose why most pharma AI ROI claims collapse under scrutiny: companies measure adoption (pilots completed, users trained) instead of business impact, because they don't have clean data to measure from in the first place
- Learn what separates the pharma AI programmes that deliver measurable outcomes from the 80% that stall after pilots - the answer is always data readiness, not AI sophistication
- Set success criteria, implement in controlled settings and choose projects with reasonable scope and clear ROI potential to provide clear performance metrics for leadership evaluation
- Challenge where pharma should place its biggest bets across medical, commercial, and enterprise operations as well as where investment should stop.
- Apply a scorecard to real use cases across medical, commercial and enterprise operations, and leave with a reusable template that links pilot metrics to budget decisions
- Show how centralised data governance, single customer record, and enterprise AI platforms enabled your commercial team to target smarter, personalise faster, and measure ROI accurately as they have access to the full customer picture
- Expose what broke when data stayed siloed between field systems, marketing automation, and medical affairs and what changed when the enterprise data layer worked correctly
- Learn the value of when commercial teams have access to unified customer intelligence, clean prescriber data, and real-time engagement metrics versus fragmented data and delayed insights
- Show the infrastructure (customer data platforms, real-time analytics, orchestration engines) that turns enterprise data investments into immediate commercial capability
- Connect consumer signals, predictive insight and next-best actions to the choices made across commercial and marketing teams
- Redesign commercial decision making around where AI should recommend, automate or act versus where teams can still offer differentiated value
- Discover which next-best actions are driving measurable commercial impact today, the KPIs they influence most, and the strategic decisions teams continue to keep human-ledÂ
- Replace fragmented AI pilots with an enterprise approach through established platforms that enables AI to scale across all business functions rather than each building out their own model
- Build guardrails that enable rather than block scale through the recognition that every use case does not require the same level of risk, compliance and security controls.
- Balance innovation and oversight through risk-based governance frameworks that accelerate AI adoption without compromising compliance
- Discover what separates successful platform implementations from failed ones - the infrastructure, governance, and organisational changes that drive engagement improvement
- Learn how to integrate qualitative patient insights with quantitative data, navigate compliance ambiguity that stops teams from sharing, and build data-driven capability in a function built on empathy, not statistics
Build once, engage everywhere: The CDP and orchestration architecture that works across AI and human touchpoints
- Show the unified engagement architecture that requires unified customer data (patient + HCP), real-time journey insight, cross-channel personalisation, and governance that prevents message conflicts
- Expose what breaks when you try to stitch together point solutions versus building on a unified platform from the start
- Evaluate how pharma companies are digitally enabling KOL/investigator engagement, emphasising seamless collaboration and 360-degree transparency across all engagements to avoid losing out to competitors through poor experience
- Expose what fragmentation costs: HCPs are receiving conflicting information, brand credibility undermined, engagement failing to convert
- Show how a single approved-content and interaction record lets commercial, medical and field teams give HCPs consistent answers across every channel, including AI-generated ones
- Name the real blockers to connecting commercial, medical and market access data: who owns it, who funds it, and where architecture still fails
- Decide which silos to break, which to protect and where shared data can unlock cross-functional decisions and enterprise AI value.
- Develop an AI-ready operating model that connects launch, evidence and HCP insights to support faster, more informed decision-making
- Show how fragmented data sources, poor data quality, and inconsistent governance force you to delay or redesign your RWE programme
- Expose the gap: you had the money for RWE, the protocol was solid, but your data infrastructure couldn't deliver the consistency, traceability, and governance that regulators demanded for regulatory-grade evidence
- Show what data readiness means for regulatory-grade RWE data quality standards, governance frameworks, integration requirements, traceability, and validation that most pharma companies underestimate until they commit to an RWE programme
- Expose the hidden costs and delays when data infrastructure isn't built for RWE: retrofitting data quality, auditing lineage, adding governance controls, integrating legacy systems
- Learn how to combine RWE, HTA requirement data, and competitive pricing intelligence into models that predict which markets will reimburse rather than relying on organisational optimism
- Discover the data infrastructure required to support reimbursement planning at scale such as data governance, analytics platforms, and integration across RWE, pricing, and payer intelligence
- Leave with a reimbursement probability framework that helps identify market access risks early, prioritise evidence investments and optimise launch sequencing across markets.
