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Two Years of Generative AI in Life Sciences: Lessons Learned and the Path Forward

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Two Years of Generative AI in Life Sciences: Lessons Learned and the Path Forward

02 Apr 2025

Executive Summary

Over the past two years, the potential of Generative AI (GenAI) has caused huge excitement across the life sciences industry. What began as experiments automating basic tasks like document search and summarization has evolved into a belief that GenAI can be a powerful catalyst driving strategic outcomes like speed to market, improved compliance, personalised customer engagement, deep insights, improved patient-centricity and lower costs across drug discovery, clinical development, regulatory, safety, medical affairs, and commercialization.

For C-suite executives, the message is unequivocal: GenAI is not merely a buzzword, but a transformative force empowering organizations to achieve unprecedented levels of operational efficiency, accelerated innovation, and elevated customer engagement through advanced capabilities such as content generation, insights discovery, and personalized medicine. This whitepaper highlights a practitioner’s view through key lessons learned, critical success factors, and actionable insights to guide pharma organizations in navigating the path forward with GenAI. The time to move from experimentation to enterprise-scale adoption has arrived.

Introduction: The Evolution of GenAI— From Experimentation to Strategic Advantage

The introduction of ChatGPT and similar GenAI solutions in 2022 marked a pivotal moment for AI in pharma. Initial experiments focused on automating rudimentary tasks but the efforts rapidly evolved into enterprise-wide AI solutions streamlining workflows and unveiling new opportunities across R&D, medical writing, clinical trials, marketing, and customer engagement.

A. The First Wave: Lessons from Early GenAI Adoption in life sciences

Early GenAI adoption in the pharmaceutical industry centered on proof-of-concept (PoC) projects aimed at automating routine tasks such as simple document drafting, summarization, and literature reviews. While early successes highlighted the significant potential for efficiency gains, they also exposed critical gaps, particularly in addressing the specialized needs of the life sciences industry.

For CXOs, a key takeaway is that GenAI’s success hinges on domain-specific solutions. Pharma companies leveraging AI for content generation—such as creating promotional materials or medically rich reports — observed productivity gains but grappled with challenges like brand consistency, regulatory compliance, accuracy and alignment with approved claims libraries. This underscores the need for highly contextualized, compliant GenAI models that seamlessly integrate with existing workflows and systems.

Another crucial lesson from the first wave of GenAI adoption is the paramount importance of data privacy and compliance. As regulatory standards such as GDPR, HIPAA, and the EU AI Act gain prominence, the need for robust security and Responsible AI (RAI) frameworks emerge as a key factor to consider for scaling GenAI solutions.

B. The Current State: Pilot to Production

Today, most leading life sciences organizations are at a stage when pilots or PoCs have been completed across most areas like medical writing, Standard Response Document (SRD) creation, MLR, content generation, literature review, avatar creation etc. More often than not these pilots have shown promising results and pharma companies are now exploring scaling pilot projects to deploying scalable AI solutions into production. Early successes highlight its transformative potential across functions:

Clinical development

AI platforms are now optimizing clinical trial protocols, reducing protocol amendments by 40% and increasing patient enrolment by 25%. AI also assists in patient stratification, analyzing vast datasets to predict patient responses and improve trial outcomes.

Regulatory Affairs

GenAI is being used effectively to handle Health Authority (HA) queries and draft regulatory documents such as COs, CSRs. An innovator pharma company has successfully tested a GenAI solution that searches internal and external sources of data to determine whether similar HA queries were received and how they were answered in the past with a very high accuracy and at an 80% reduction in response time.

Medical Affairs

GenAI has revolutionized content review during the MLR process. A top three generics company and a mid-pharma specialty company have successfully used GenAI to fact validate promotional material and enable accurate source traceability on citations. A top five innovator company and a European pharma organisation have been successful in creating de novo SRDs, cutting the time taken by more than 60% by the Medical Information team.

Pharmacovigilance

Early efforts at a top five pharma company have shown promising results in generating a first draft of Periodic Safety Update Reports (PSURs). It is anticipated to reduce the time to submission by >20 days.

Commercial Operations

AI is enabling generate dynamic marketing content tailored to local markets. Pharma companies have reduced video production costs by 40% while improving speed-to-market by 2x .

Sidebar

Case Study: 2X faster content production leveraging GenAI

A global pharmaceutical leader set out to redefine stakeholder engagement through personalized, video-based multilingual content across 13+ markets. However, the organization was confronted with significant challenges: high content localization costs and effort to ensure medical content accuracy during localization.

Solution

In collaboration with Indegene, the organization deployed an advanced GenAI-powered “Avatar” solution with human-in-the-loop approach, effectively meshing technology, people, and processes. The solution’s core capabilities included:

60+ hyper-realistic Avatars:

Designed with lifelike facial expressions and dynamic gestures

Advanced speech and lip-sync technology:

Leveraging AI-driven phoneme detection and frame-by-frame analysis to ensure seamless voice cloning and precise articulation in multiple languages

Cultural and emotional customization:

Tailoring content to reflect diverse ethnicities, age groups, and emotional tones—ranging from calmness to excitement— to ensure resonance with diverse target audiences

Strong data security protocols:

Ensuring compliance through stringent security guardrails across processes and data

Outcomes

The solution empowered the organization to overcome traditional localization challenges by rapidly creating 200+ personalized videos across seven languages. These videos delivered content that felt authentic and culturally attuned for key markets such as Germany, Spain, France, and Portugal—achieving a 40% reduction in production costs compared to traditional models while enabling 2X faster production and localization.

200+

personalized videos across seven languages

40%

reduction in production costs

2x

faster production and localization

While the pilots have been successful in many areas, organisations are also going through internal deliberations on how regulatory authorities will react to these interventions. Many authorities are working on guidelines, and we are seeing some draft guidelines emerge from the EMA, FDA etc., but there is still uncertainty on this and that is likely to reduce the pace of adoption of GenAI.

Despite these early successes, the rapid evolution of GenAI technologies presents challenges in expediting decision-making and driving organization-wide adoption. To fully unlock GenAI’s potential, life sciences organizations must embrace a holistic, integrated value-chain approach rather than the current mushrooming of fragmented initiatives. This entails a COE-driven strategy, adopting multimodal solutions, and aligning GenAI investments with existing data and technology stacks, enabling AI-powered tools to work seamlessly across functions.

Critical Success Factors for Generative AI Adoption in Life Sciences

The successful adoption of GenAI in life sciences depends on a sophisticated orchestration of domain expertise, technical capabilities, organizational readiness, and execution excellence—all within the industry’s unique constraints.

Designing GenAI-Ready Organizations: Blueprint of a New Operating model

Many leading organizations are establishing AI Centers of Excellence (CoEs) that unify medical, regulatory, commercial, and technical expertise under cohesive leadership. A best practice is emerging: a hybrid operating model that combines a centralized CoE driving innovation and setting standards with decentralized AI capabilities embedded within business units. The success of this model rests on three foundational pillars:

Strategic alignment of functional expertise and GenAI operations

Striking the right balance between responsible AI deployment and domain-specific innovation is essential to maintaining operational integrity while fostering creative experimentation.

Executive sponsorship

Leadership must provide clear direction, measurable goals, and accountability frameworks to ensure GenAI initiatives align with broader organizational objectives and deliver tangible value.

Centralized governance councils

Balancing innovation with compliance requires transparent policies and rigorous oversight to maintain regulatory adherence while encouraging innovation.

Moreover, talent development plays a pivotal role in scaling AI capabilities. Enterprises must invest in programs that promote

AI fluency

across the workforce, equipping business stakeholders with the skills and tools needed to thrive in AI-driven environments. Initiatives like workbench tools for hands-on experimentation and curated skill-building programs underscore the importance of cultivating expertise at scale.

Return on Investment (ROI) Measurement of GenAI Initiatives and Prioritization Frameworks

Measuring the value generated by GenAI solutions is a critical topic in life sciences, especially given the industry's focus on tangible outcomes. To measure the ROI effectively, organizations can adopt pragmatic approaches such as time-to-market acceleration metrics, compliance cost reductions, improve in quality and process efficiency gains. For example, tracking the reduction in manual review time for pharmacovigilance case processing or the enhanced accuracy in regulatory submission filings can directly showcase cost and time savings. Furthermore, leveraging metrics such as customer engagement levels in medical affairs, adherence to compliance standards, or even insights derived from real-world evidence can help demonstrate both qualitative and quantitative value. Establishing clear value-tracking frameworks early in the implementation phase will ensure that stakeholders can see the direct impact of these initiatives, reinforcing the strategic importance of scaling GenAI solutions.

To effectively harness the potential of Generative AI, pharma organizations can implement a use case prioritization framework that evaluates applications based on key criteria. Such as framework helps identify which AI use cases in life sciences deliver the most significant value and impact, guiding strategic decision-making. By integrating this approach with a robust value framework, organizations can monitor and quantify drivers such as business value, strategic relevance, risk mitigation, efficiency, and scalability. Together, these frameworks ensure that leaders in life sciences are grounded in the reality of their GenAI investments, minimizing surprises, validating the investment thesis, and maximizing returns as the technology continues to evolve.

Example of a GenAI use case prioritization framework for life sciences

Value Lever	Value Driver	Value Description	Impact Value			Impact Level
			1 Minor	3 Moderate	5 Significant	Enter Score
Business Value	Cost Savings	Target average savings per year; only realized cost savings count, FTE savings to be put in reduction of process resources	≤$50k	≤$500k	>$500k	1
Strategic Relevance	Alignment with company goals/vision		Indirect impact on company goals/vision	Direct impact on company goals/vision	Direct impact on multiple company goals/vision	5
	Time to clinical trial impact	Target reduction of time to clinical trial initiation or completion compared to status quo	≤10%	10-30%	>30%	1
	Competitive advantage	Improvement in benchmark metrics for this project in comparison to industry standards	Negligible improvement of performance	Consistent improvement of performance	Substantial improvement of performance	1
Risk reduction	Compliance	Improvement in adherence to existing regulations and process	Negligible or no impact on compliance	Measurable increase in compliance	Increase in compliance across areas	3
	Quality	Improvement in quality of deliverables	Negligible or no impact	Measurable quality improvement	Significant improvement in quality	1
	Variability	Improvement in Consistency of deliverables	Negligible or no impact	Measurable reduction	Significant reduction in variability	5
Efficiency improvement	Increase in throughput	Increase in throughput per process execution	≤10%	10-30%	>30%	3
	Reduction of process resources	Reduction in FTEs, materials, time etc., per process execution	≤10%	10-30%	>30%	5
	Increase in robustness (intelligence)	Improvement in overall product process, e.g. workflow/ elimination of administrative tasks	Negligible improvement in overall process effectiveness	Measurable improvement in overall process effectiveness	Substantial improvement in overall process effectiveness	5
Scalability	Transferability of the solution across other use cases in the organization		Isolated use case	2-5 use cases	>5 use cases	5

Engineering GenAI for Life Sciences: Cracking the Code

Success in life sciences requires GenAI solutions tailored to the industry’s unique challenges. Generic approaches often fail to address the complexities of disease mechanisms, patient engagement, and stringent regulatory requirements. For example, the misinterpretation of acronyms by Generative AI underscores the need for domain-specific contextualization and robust guardrails. In a medical context, “RA” might mean Rheumatoid Arthritis but could be erroneously interpreted as Risk Assessment if the AI lacks specialized training.

Navigating regulatory nuances is equally crucial. For instance, in the Nordics, patients depicted in promotional or educational materials must not appear in visible pain, reflecting cultural norms of stoicism and a preference for understated healthcare narratives. These insights should inform the development of guardrails and agentic Retrieval-Augmented Generation (RAG) frameworks to ensure contextualized and compliant outputs.

Additionally, creating an enterprise taxonomy for GenAI models and training is critical. Advanced techniques like Knowledge Distillation and the development of comprehensive ontologies and knowledge graphs help organize clinical and business data, ensuring that AI models retain contextual relevance across the entire value chain.

Maximizing the GenAI Impact: The Power of Multimodal Approaches

Traditional foundation models face significant limitations—they are often restricted to single-modal outputs, lack the ability to integrate context across media formats, struggle with scaling personalization, and require additional effort to ensure compliance in regulated industries like pharma and life sciences. These constraints limit their effectiveness for complex, multi-format scenarios.

Integrating multimodal approaches is critical for maximizing GenAI's potential in life sciences. By utilizing systems capable of processing text, images, and video, organizations can overcome these challenges, generating compelling, personalized content at scale across various formats while maintaining strict regulatory compliance. This integration empowers marketing teams to streamline every aspect of marketing campaigns—planning, creation, distribution, activation, and performance measurement—at an unprecedented scale and speed.

For example, a marketer planning an awareness campaign for a brand can utilize multimodal AI to optimize the entire process. By analyzing the campaign brief—which defines the target persona, objectives, and messaging strategy—multimodal AI generates tailored concepts, including key visuals such as infographics, video storyboards, and platform-specific copy designed for channels like LinkedIn (targeting professionals) and Instagram (engaging broader audiences).

Similarly, in Clinical, multimodal AI can streamline protocol design by synthesizing inputs from scientific literature, patient demographics, and investigator feedback into a cohesive document. For Regulatory teams, it can dynamically generate submission-ready documents by integrating data across clinical trials, compliance requirements, and regional standards, reducing manual effort while ensuring alignment with guidelines like EMA, FDA, and ICH.

In Pharmacovigilance, multimodal AI has the potential to revolutionize safety workflows by automating case processing, enabling faster intake, triage, and data entry with improved accuracy. It can streamline aggregate report preparation by extracting and summarizing key insights from large volumes of data, ensuring compliance with global regulatory standards. Additionally, multimodal AI can enhance signal detection and management by analyzing real-world data, literature, and adverse event reports, identifying potential safety concerns with greater precision. This allows for proactive risk assessment, timely decision-making, and more effective communication with regulators, ultimately supporting patient safety.

For Medical Affairs, this technology can empower teams to develop personalized HCP engagement strategies, combining deep insights from publications, clinical trial data, and real-world evidence into educational materials tailored for specific therapeutic areas.

Thus, unlike foundational models, which are constrained to single-modal inputs and outputs and demand extensive manual effort for integration, multimodal AI seamlessly combines creative development, compliance assurance, and operational automation.

Seamless Integration of GenAI with Enterprise Data and Technology Stacks: The Role of Contextualization Engines

The integration of GenAI with existing enterprise data and technology ecosystems is a transformative advancement for life sciences organizations. This synergy enables compliant, efficient, and impactful AI adoption. Companies that successfully deploy GenAI prioritize its seamless integration with their data and technology stacks, securing a significant competitive advantage.

A pivotal enabler in this ecosystem is a contextualization engine—a connective layer that facilitates communication between AI systems and enterprise data and technology platforms.

For example, integrating a GenAI application with a Digital Asset Management (DAM) system can revolutionize the creation of compliant marketing materials. With the right contextualization engine in place, the system interprets the context of the query, translates it for the DAM, cross-references approved claims, and generates accurate campaign content.

This approach not only drives operational efficiency but also ensures that GenAI outputs are aligned with regulatory and organizational standards, making it an indispensable component for scalable AI adoption in life sciences.

When designing the contextualization layer, companies must also consider scalability, data governance, and domain-specific needs. A common horizontal layer for the enterprise could drive consistency and reduce duplication of efforts across multiple verticals, enabling shared data insights and compliance frameworks. However, in specialized domains such as regulatory, pharmacovigilance, clinical development, or medical affairs, separate contextualization layers tailored to each business vertical may be necessary to address unique workflows, data structures, and compliance requirements. These layers must be designed to integrate seamlessly with existing systems such as Regulatory Information Management (RIM), safety databases or clinical trial management systems (CTMS), ensuring that GenAI applications enhance productivity while meeting industryspecific standards. Balancing these approaches requires organizations to assess their enterprise architecture, operational goals, and long-term AI strategies to maximize ROI and adoption success.

AI Security and Compliance: Safeguarding Privacy and Legal Risks

As AI is increasingly integrated into life sciences operations, privacy and compliance issues become even more critical. Life sciences organizations must address concerns around data security, regulatory compliance, and ethics by implementing strong encryption protocols, guardrails framework, access controls, and continuous monitoring.

AI models, particularly in sensitive areas such as clinical trials and patient data analysis, must comply with regulatory standards like EU AI Act, GDPR and HIPAA. In addition, Intellectual Property rights around AIgenerated content (including images, videos, and promotional materials) also need to be explicitly defined, ensuring that AI output does not infringe on copyright or raise legal challenges in any markets.

Upskilling and Change Management Practices: A key differentiator

Leading organizations are galvanizing GenAI adoption through robust change management practices that drive innovation and enable enterprise-wide collaboration. The following examples showcase effective best practices.

GenAI councils and cross-functional teams

One global enterprise established a dedicated GenAI Council, uniting experts from business, technology, compliance, and operations. This council functions alongside specialized use case identification group, ensuring that innovative ideas are rigorously vetted and aligned with strategic objectives across the organization.

Targeted upskilling and enablement tools

A mid-sized organization accelerated its GenAI adoption by launching company-wide AI literacy bootcamps and handson workshops. These initiatives, coupled with custom and compliant GenAI enablement tools, empowered employees to experiment, develop new use cases, and track learning outcomes through clear performance metrics.

Integrated communication and feedback mechanisms

Another industry leader prioritized continuous engagement by establishing robust communication channels. Regular feedback loops and performance reviews ensured that AI initiatives not only met operational goals but also adapted in response to emerging challenges and opportunities.

Future Outlook for Generative AI in Pharma

The Rise of Agents: From Automation to a Powerful Autonomous Force

AI agents are poised to revolutionize the future of business, ushering in a new era of autonomy, intelligence, and collaboration. Unlike traditional Generative AI applications that focus on singular tasks, these autonomous systems will leverage Large Language Models (LLMs) and other cutting-edge AI technologies to reason, plan, and interact with data, people, and systems in increasingly complex ways.

In the future, AI agents won’t just execute predefined actions—they will create workflows, optimize processes, and tackle complex problem-solving traditionally managed by humans. These agents will be capable of continuously learning and adapting, allowing them to develop new strategies and solve problems without human intervention. Additionally, AI agents will collaborate in multi-agent systems, where they communicate, coordinate, and work together to address challenges that require multiple levels of reasoning. Agent-to-agent collaboration will amplify efficiency, scale operations, and enable businesses to react to dynamic shifts with agility.

These agents will no longer work in isolation but will seamlessly integrate across business functions as well as departments like campaign strategy, content operations, data & analytics, medical, and commercial operations— empowering businesses to solve challenges holistically.

Consider a future where specialized agents manage key business functions:

Campaign strategist agent

Automatically identifies target audiences and dynamically recommends the best channels based on real-time data and analytics.

Content agent

Automates content creation and management across domains, ensuring compliance, accuracy, and adaptation to specific use cases.

Recruitment agent

Dynamically identifies and matches eligible patients for clinical trials by scanning real-world data (RWD) sources and electronic health records (EHRs).

Site feasibility agent

Evaluates site performance metrics and recommends high-performing sites based on therapeutic area, patient demographics, and geographic factors.

Regulatory intelligence agent

Tracks changes in regulatory policies across regions, alerting teams to updates that impact ongoing or planned submissions.

MLR agent

Validates content against regulatory guidelines in real time, accelerating approval processes and reducing delays.

Campaign analytics agent

Monitors campaign performance, adjusting strategies dynamically based on live engagement metrics and outcomes.

Literature agent

Search and screen literature articles based on the end use such as SRD creation, ICSR review, signal detection, RWE etc.

HCP engagement agent

Analyzes healthcare professional (HCP) preferences and suggests tailored engagement plans for medical education or product awareness campaigns.

Submission readiness agent

Prepares and validates regulatory submissions (e.g. IND, NDA, BLA) by ensuring compliance with regional guidelines such as FDA, EMA, and ICH.

Below is an illustration of a multi-agent workflow for marketing.

Multi-agent AI workflow for pharma marketing

These agents will form a network that continuously evolves, refining their capabilities through feedback from human collaborators and other agents. As each agent becomes more specialized and intelligent, the collective ecosystem will drive unparalleled efficiency and agility. The synergy between agents will lead to a constantly evolving business intelligence system, capable of transforming operations, innovating strategies, and fueling growth.

The future of business will be built on interconnected, autonomous AI agents that learn, adapt, and optimize in real-time. These agents will enhance every facet of business, driving innovation, scalability, and operational excellence, while allowing organizations to stay ahead of the curve in an ever-evolving market landscape.

Generative UI: A New Digital Interface for All Customer Interactions

Next big breakthrough will be Generative UI which will revolutionize how companies design and interact with digital systems. Rather than relying on static, predefined user interfaces. Generative UI will leverage agentic AI systems to design and generate user interfaces that are contextual and customized to the individual’s needs. Whether it’s for internal teams or external stakeholders, the interfaces will dynamically evolve based on the user’s role, regulatory requirements, or task at hand.

Unlike traditional static interfaces, Generative UI will enable content, such as clinical data, medical guidelines, drug information, and training materials, to be displayed in ways that best support the HCP’s clinical practice, specialty, location, and regulatory environment. For example, an HCP specializing in oncology may see the latest treatment protocols, case studies, and drug updates that specifically relate to their field, while an HCP in cardiology will see different content based on their area of expertise.

Generative UI will also play a transformative role in clinical, pharmacovigilance, regulatory, and medical affairs by dynamically generating interfaces that streamline case processing workflows, automate aggregate report generation, and provide tailored dashboards for signal detection and risk management. In regulatory affairs, it can offer dynamically updated submission templates, track region-specific regulatory requirements, and present compliance checklists tailored to the product’s lifecycle stage. In clinical, it can present trial data and patient recruitment insights customized for study teams, while medical affairs teams can access real-time, compliant content for scientific exchange and personalized HCP engagement.

Immersive and Interactive Experiences Across Channels 24/7

Conversational AI is evolving beyond simple text-based interactions and voice-enabled chatbots into multimodal systems that integrate a diverse range of communication formats, including text, voice, visuals, gestures, and human like avatars. This next phase of AI evolution will enable businesses to create immersive, interactive experiences that are intuitive, dynamic, and seamlessly available across multiple channels, 24/7. With this shift, conversational AI will no longer just be reactive—responding to customer inquiries—it will create deeper, more meaningful customer relationships that foster engagement, trust, and brand loyalty.

For instance, as part of the broader move toward digital-first engagement, avatar-based virtual sales representatives will become a central tool in the sales toolkit. These AI-powered avatars will be designed to handle routine interactions with HCPs, facilitating real-time product inquiries, answering frequent questions, and assisting with order placements, all while offering a human-like interaction experience. These avatars will be capable of delivering personalized, context-aware responses tailored to the HCPs’ needs, specialty, and interests. With advanced AI capabilities, the avatars will be able to simulate a face-to-face conversation, providing a more engaging and empathetic experience than traditional chatbots.

In clinical trials, conversational AI could streamline patient recruitment by engaging with potential participants, answering eligibility questions, and automating consent processes while providing tailored support throughout the study. In regulatory affairs, AI-powered avatars could assist in navigating complex submission processes, ensuring accurate documentation and compliance with guidelines. For pharmacovigilance, they could interact with reporters to gather comprehensive adverse event details or guide them through structured reporting forms. In medical affairs, conversational AI systems could facilitate virtual HCP education sessions, provide insights into ongoing trials, or clarify post-market safety findings in an engaging manner.

Promise of Hyper-Personalization Would Be Real

The shift from generic, impersonal experiences to the democratization of hyper-personalized engagements will fundamentally reshape how pharma brands interact with HCPs, patients, and policymakers. GenAI will be at the forefront of this transformation, enabling companies to move from traditional, segment-based messaging to highly customized campaigns that reflect the unique needs and preferences of each individual stakeholder.

Unlike current segmentation models which group HCPs by broad categories like prescribers, non-prescribers, or advocates, GenAI will empower marketers to develop dynamic, personalized HCP personas that adapt at journey stage and interaction level, tailoring communication style and content based on specific interactions, and interests. Another interesting dimension to watch out will be personification of brands with GenAI. By aligning AI with the brand or company’s core values, voice, and tone, pharma marketers can establish stronger emotional connections with HCPs and other stakeholders, ultimately fostering brand loyalty and trust.

Also, in pharmacovigilance, GenAI can personalize safety communications based on patient or provider profiles. For medical affairs, it tailors clinical data and resources to HCP’s needs, enhancing engagement. In clinical trials, AI can adapt content and outreach to patient preferences and trial eligibility, improving recruitment. Regulatory affairs can benefit by customizing communications with agencies to streamline submissions and increase success rates.

Synthetic Data Will Become Critical for Novel Therapeutic Areas

Synthetic data generation is emerging as a game-changing capability in life sciences, particularly for rare diseases and novel therapeutic areas where real-world data is limited. This technology creates statistically valid datasets that mirror the complexity of actual patient data while maintaining privacy compliance. Early adopters report 30-40% acceleration in research timelines and significant cost reductions in early-stage development. For executives, synthetic data capabilities offer a clear competitive advantage: faster research cycles, reduced development costs, and the ability to confidently explore new therapeutic areas where data scarcity has traditionally hindered progress (working out regulatory debates).

A blended approach combining technical skills with domain expertise is vital for developing AI-driven solutions that meet the complex needs of life sciences. Companies that successfully navigate this change invest in building internal capabilities while also leveraging external partnerships to stay at the forefront of AI innovation.

Conclusion and Implications: Next Wave of AI Innovation in Pharma

Generative AI has evolved from an experimental tool into a transformative force, reshaping the pharmaceutical industry at every level. From drug discovery to commercialization, AI is revolutionizing how life sciences organizations operate, innovate, and engage with stakeholders. Its potential is no longer aspirational—it is the driving force behind pharma’s future.

Key Implications:

Drug launch cycles will accelerate like never before,
cutting through delays and getting innovations to market faster. Speed will become the new superpower.

Seamless, anywhere-anytime experiences will set a new standard,
delivering hyperpersonalized engagement for HCPs, patients, and stakeholders across every touchpoint. AI will ensure every moment counts.

Agents will rise as the ultimate brand champions,
autonomously managing campaigns, compliance, and customer interactions. They will tirelessly amplify your brand's presence and impact.

AI will break silos and build bridges,
unifying systems and teams under a single, datadriven vision. Collaboration will transform from aspiration to reality.

AI learning will be embedded into the organization’s DNA,
fostering a culture of continuous growth where employees and AI work handin- hand, learning, adapting, and winning—together.

Trustworthy AI will define success,
ensuring ethical, compliant, and secure innovations that build confidence while meeting regulatory and societal expectations.

Generative AI is not just an enabler—it is the engine of a smarter, faster, and more connected pharmaceutical ecosystem. Organizations that embrace AI-powered strategies today will lead the industry's evolution, setting benchmarks for innovation, agility, and impact.

Success with AI in pharma organizations will be determined by setting a few bold and measurable business performance goals. These goals should be ambitious—achievements that would be impossible without leveraging advanced digital innovations and AI. They should also push teams to rethink and transform traditional ways of working.

The question is no longer whether to adopt Generative AI—it's how quickly you can scale it to lead this transformation. The future of pharma is here. The time to act is now. Are you ready?

Acknowledgements:

We thank the members of the GenAI Global Council for their active contributions towards this paper:

Matteo di Tommaso

SVP, Global Drug Discovery IT

Bristol Myers Squibb

Edwin Erckens

Chief Digital Technology Officer

UCB

Clapton Dias

SVP, Global R&D Strategy, Portfolio, and Hubs

EMD Serono, Inc. (Merck Group)

Suhail Allam

Former Head Data Analytics and Insights US & Head Data and Analytics International

Novartis

Ashish Sharma

EDO & Vice President, Strategic Data Products