Agenda
AI & Digital Innovations
Driving Smarter Science, Faster Trials and Stronger Value Through AI‐Enabled Innovation
We are delighted to welcome you to our upcoming AI & DIGITAL INNOVATIONS World Congress 2026 Europe, hosted by FACILITATE LIVE. The Congress focuses on “Driving Smarter Science, Faster Trials and Stronger Value Through AI‐Enabled Innovation”.
The biopharmaceutical industry is entering a defining moment, one where scientific ambition, operational pressure and commercial expectations converge, and where artificial intelligence is rapidly becoming the most transformative force across the entire R&D and clinical value chain. What began as isolated pilots and exploratory proofs of concept has evolved into a new operating paradigm: AI as a strategic engine powering smarter science, faster development and stronger evidence. Organisations that embrace this shift are already seeing measurable gains in productivity, decision quality and competitive differentiation. Those that hesitate risk falling behind in an increasingly data‐driven, insight‐accelerated landscape.
The AI & DIGITAL INNOVATIONS CONGRESS convenes the leaders shaping that future. Senior executives, scientific innovators, clinical strategists, digital architects and commercial decision makers gather here to explore how intelligent technologies are redefining what is possible, from the earliest spark of discovery to the moment a therapy reaches patients and markets. The programme is designed not simply to showcase tools or trends, but to illuminate the structural changes underway across modern R&D and the strategic capabilities required to compete in an AI‐enabled world.
At the heart of this transformation is the rise of multimodal foundation models, generative design systems and predictive analytics that unlock deeper biological, chemical and clinical insight. These technologies are enabling organisations to interrogate complex data at unprecedented scale, design novel molecules and modalities with greater precision, and make high conviction decisions earlier in the development process. The implications are profound: accelerated timelines, reduced attrition, more resilient portfolios and assets with clearer commercial potential.
Equally significant is the operational revolution unfolding across clinical development. AI is reshaping trial design, feasibility modelling, patient stratification and site performance, while digital endpoints, sensors and remote monitoring are redefining how evidence is captured and validated. These innovations are not only improving scientific robustness—they are strengthening regulatory submissions, enhancing payer narratives and enabling more inclusive, patient‐centric trial experiences. The result is a clinical ecosystem that is faster, leaner and more capable of generating the high‐quality evidence required for successful launch and sustained market access.
Beyond science and operations, AI is also transforming how organisations think about strategy, investment and lifecycle value. Intelligent forecasting, continuous evidence generation and real‐time safety monitoring are creating new opportunities to optimise resources, reduce risk and extend the commercial impact of therapies long after approval. The convergence of scientific intelligence, operational excellence and evidence advantage is redefining what it means to lead in biopharma.
The AI & DIGITAL INNOVATIONS CONGRESS serves as a platform to understand these shifts, challenge assumptions and accelerate adoption. It is a meeting for candid discussion, cross‐functional collaboration and strategic alignment, bringing together the people and ideas that will shape the next decade of innovation. As AI continues to evolve, the organisations that harness its full potential will not only deliver better science and stronger evidence; they will define the future of therapeutic development and the competitive landscape of global healthcare.
We look forward to your participation!
Sincerely yours,
Jocelyn Raguindin
Conference Director
Paradigm Global Events / Facilitate Live
GAIN LATEST INSIGHTS ON:
- AI‐powered scientific strategy and how intelligent models are reshaping early‐stage decision‐making, investment confidence and portfolio advantage.
- The rise of multimodal foundation models as engines of analytical intelligence across discovery, translational science and strategic R&D planning.
- Generative design technologies that accelerate molecular creativity, expand chemical space and create commercially differentiated assets.
- Predictive ADMET, in‐silico toxicology and early risk reduction frameworks that strengthen translational confidence and portfolio resilience.
- Autonomous labs, robotics and high velocity experimentation systems that dramatically increase discovery throughput and operational efficiency.
- Modern AI‐native data infrastructures that unify scientific, clinical and operational intelligence to support scalable innovation.
- AI‐enabled target validation, biomarker strategy and multiomics integration for deeper mechanistic insight and more reliable early‐stage decisions.
- High‐conviction portfolio prioritisation models that improve resource allocation, accelerate go/no‐go decisions and enhance commercial outcomes.
- AI‐driven clinical development strategies that modernise trial design, strengthen feasibility modelling and improve operational predictability.
- Precision trials powered by biomarker intelligence and patient stratification to increase success probability and strengthen regulatory narratives.
- Digital trials, sensors and AI‐derived endpoints that transform patient engagement, evidence capture and trial inclusivity.
- Operational forecasting, site performance analytics and cost efficiency models that reduce trial waste and improve timeline reliability.
- AI‐powered safety monitoring and pharmacovigilance systems that enable real‐time signal detection and stronger lifecycle risk management.
- Real‐world data and AI‐driven evidence strategies that enhance submissions, payer value stories and market access success.
- Continuous evidence generation across development and post‐market phases to support sustained commercial growth and competitive differentiation.
WHO SHOULD ATTEND?
This congress is designed for senior scientific, digital, clinical and strategic stakeholders across pharma, biotech, and research organisations who are shaping the next era of AI‐enabled therapeutic innovation.
Network with Presidents, Heads/Chiefs, VPs, Directors, and Leaders in the area of:
R&D, Discovery & Preclinical Leadership
- Chief Scientific Officers
- Heads of Research & Early Development
- Directors of Drug Discovery, Target Identification & Validation
- Computational Biology, Bioinformatics & Translational Science Leaders
- Preclinical Development & Mechanistic Modelling Leads
AI, Data Science & Digital Innovation Leaders
- Chief Digital Officers & Chief Data Officers
- Heads of AI/ML, Advanced Analytics & Scientific Computing
- Directors of Digital R&D, Automation & Robotics
- Data Engineering, Informatics & Platform Architecture Leads
Clinical Development, Operations & Evidence Generation
- Chief Medical Officers
- Clinical Development & Clinical Operations Directors
- Heads of Digital Trials, DCTs & Clinical Innovation
- Biomarker Strategy, Precision Medicine & Patient Stratification Leads
- Real‐World Evidence, Real‐World Data & HEOR Directors
Regulatory, Safety & Compliance Stakeholders
- Regulatory Affairs Leaders (AI, Digital Health, Evidence Submissions)
- Pharmacovigilance & Drug Safety Directors
- Quality, Compliance & Digital Endpoint Validation Specialists
Commercial, Market Access & Strategic Innovation
- Chief Innovation Officer
- Market Access & HEOR Leaders
- Portfolio Strategy & R&D Investment Directors
- Strategic Partnerships, Innovation & Business Development Lead
Technology, Platform & Digital Health Innovators
- AI Platform Providers & Scientific Product Leaders
- Digital Health, Sensor & Remote Monitoring Innovators
- Clinical Technology Vendors (CTMS, eCOA, eConsent, DCT platforms)
- Data Infrastructure, Cloud & Automation Specialists
Investors, Advisors & Innovation Scouts
- Life Sciences Investors & Venture Partners
- Strategic Advisors & Innovation Consultants
- Corporate Development & Emerging Technology
- Day 1 11/11//2026
- Day 2 12/11//2026
AI‐Enabled Discovery, Early Development & Strategic R&D Acceleration
- How AI‐enhanced insight is reshaping scientific strategy and early‐stage prioritisation
- The commercial implications of faster, more confident go/no‐go decisions
- Balancing scientific ambition with investment discipline in an AI‐native environment
- Competitive differentiation through intelligent discovery and early development
- Leadership behaviours required to scale AI across scientific and business functions
Moderator:
Panellists:
- Multimodal models enabling deeper biological, chemical and translational insight
- How unified data intelligence strengthens scientific confidence and commercial viability
- New capabilities for target, biomarker and modality exploration at scale
- Strategic implications for pipeline competitiveness and R&D productivity
- Building organisational readiness for foundation model adoption
- AI‐driven molecular creativity expanding chemical space and design possibilities
- Accelerating hit‐to‐lead cycles and reducing cost of failure in early development
- Commercial value unlocked through differentiated, AI‐designed assets
- Integrating generative tools into discovery workflows and decision‐making
- Case examples demonstrating accelerated timelines and improved asset quality
- How automation reduces bottlenecks and increases experimental throughput
- Improving reproducibility, data quality and operational efficiency
- Commercial benefits of automated discovery environments
- Integrating robotics into scientific workflows without disruption
- Customer case studies demonstrating measurable productivity gains
- Creating unified, high‐performance data environments for cross‐functional teams
- Reducing infrastructure complexity while enabling advanced analytics
- Supporting AI‐native discovery, modelling and simulation at scale
- Commercial ROI demonstrated through faster cycles and reduced operational cost
- Real‐world examples of platform‐driven scientific acceleration
- Strengthening early triage and reducing downstream attrition
- Improving translational confidence through predictive modelling
- Commercial impact of early risk mitigation on portfolio stability
- Integrating ADMET intelligence into scientific and strategic decision‐making
- Case examples of accelerated early development through predictive tools
- Improving confidence in target selection through multimodal AI
- Biomarker intelligence strengthening translational alignment
- Commercial implications of more reliable early‐stage decisions
- Integrating AI‐driven validation into scientific workflows
- Case studies demonstrating improved early‐stage success rates
- AI integrating transcriptomics, proteomics and metabolomics at scale
- Identifying mechanistic drivers and translational biomarkers
- Strengthening scientific rationale for early‐stage investments
- Commercial value of improved biological understanding
- Real‐world examples of multi‐omics‐driven breakthroughs
- Closed‐loop optimisation enabling continuous experimentation
- Robotics improving throughput, reproducibility and cost efficiency
- Commercial impact of high‐velocity discovery cycles
- Integrating autonomous systems into existing R&D workflows
- Case studies demonstrating accelerated discovery timelines
- Designing modern data environments that support AI‐driven science
- Reducing fragmentation and enabling cross‐functional collaboration
- Commercial benefits of unified, high‐performance infrastructure
- Governance and security considerations for AI‐native R&D
- Examples of scalable architectures delivering measurable impact
- AI‐driven modelling improving portfolio decision‐making
- Strengthening investment confidence through predictive insight
- Commercial implications of faster, more accurate prioritisation
- Integrating AI into strategic planning and resource allocation
- Case examples of portfolio uplift through intelligent triage
- How AI accelerates scientific and commercial value creation
- R&D productivity gains and cost efficiency through intelligent workflows
- Competitive differentiation through AI‐enabled pipelines
- Leadership behaviours required for sustained innovation
- The future of AI‐powered early development and portfolio strategy
Moderator:
Panellists:
AI‐Driven Clinical Development, Operational Excellence & Evidence Strategy
- Modernising trial design through predictive modelling and digital innovation
- Improving recruitment, retention and site performance with AI‐driven insight
- Operational forecasting enabling leaner, more predictable trial execution
- Digital endpoints and sensor‐based data strengthening evidence quality
- Commercial impact of faster, smarter and more inclusive trials
Moderator:
Panellists:
- Increasing trial success probability through precision cohort design
- Reducing variability and improving signal detection
- Strengthening regulatory and payer narratives with biomarker‐driven evidence
- Integrating stratification intelligence into clinical strategy
- Commercial implications for competitive positioning and launch readiness
- Continuous, real‐world patient insights improving trial robustness
- Validating digital endpoints for regulatory and commercial acceptance
- Enhancing inclusivity and patient experience through remote monitoring
- Operational efficiency gains from digital trial models
- Evidence advantages supporting launch, market access and lifecycle value
- Predictive analytics improving operational planning and risk management
- Site selection and performance optimisation through AI‐driven insight
- Reducing operational waste and improving cost efficiency
- Strengthening timelines through proactive decision‐making
- Customer case studies demonstrating operational uplift
- Sensor‐based data capture enabling continuous patient insight
- Technologies improving engagement, adherence and retention
- Real‐time monitoring strengthening safety and efficacy signals
- Evidence generation advantages for regulatory and commercial success
- Real‐world examples of digitally enhanced trials
- Real‐time adverse event detection improving patient safety
- Strengthening signal intelligence for regulatory confidence
- Operational efficiency gains in safety workflows
- Commercial implications for lifecycle management and risk mitigation
- Case examples demonstrating accelerated safety insight
- Predictive modelling improving protocol design and complexity reduction
- Feasibility insights strengthening operational readiness
- Commercial impact of fewer amendments and faster timelines
- Integrating optimisation tools into clinical planning
- Case studies demonstrating improved protocol performance
- AI improving forecasting accuracy across timelines and resources
- Identifying operational risks earlier and more reliably
- Commercial benefits of leaner, more predictable trial execution
- Integrating forecasting tools into operational workflows
- Real‐world examples of cost and time savings
- Synthesising RWD/RWE to strengthen submissions and payer narratives
- Improving evidence quality through multimodal AI
- Commercial implications for launch, access and lifecycle value
- Integrating RWE intelligence into clinical and commercial strategy
- Case studies demonstrating accelerated approvals and stronger value stories
- AI enabling perpetual insight across clinical and commercial phases
- Strengthening lifecycle management through ongoing evidence creation
- Commercial value of continuous data for differentiation and competitiveness
- Integrating continuous evidence into organisational strategy
- Real‐world examples of post‐market intelligence acceleration
- Regulatory confidence through AI‐enhanced evidence generation
- Market access differentiation through stronger value stories
- Post‐market insight acceleration improving lifecycle performance
- Commercial value unlocked through continuous evidence
- The future of evidence‐driven competitiveness in an AI‐enabled industry
