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Pharma's commercial leaders are at a crossroads, blending AI's potential with applications as they tackle implementation challenges.
In brief:
- Digitization moves from bonus to backbone, dictating pharma's ability to evolve and thrive.
- Complexity in AI implementation emerges as a key focus for pharma.
- Case studies showcase strategies for navigating AI's evolving landscape.
Artificial intelligence (AI) proficiency is undergoing a strategic expansion, with pharmaceutical industry leaders acknowledging that its advanced applications, which focus on improving efficiency and outcomes, are key to gaining a competitive advantage.
The pharmaceutical industry is swiftly adopting AI to enhance everything from R&D to patient care. Yet, unlocking AI's full value goes beyond isolated trials; it demands integrating technology and developing talent across the organization. As the sector evolves from a few large AI use cases to a future dominated by numerous AI microservices, companies must prepare their tech and data infrastructures for easy integration of these innovations.
With thousands of AI solutions in development, the shift is inevitable. Pharma giants need to ensure that their current AI initiatives lay the groundwork for a flexible, future-ready ecosystem. This means aligning technology upgrades and workforce skills with business goals, setting the stage for seamless adoption of AI's transformative power.
Potential data and AI use cases for pharma
In the quest to stay at the forefront of innovation and efficiency, the pharmaceutical industry stands on the brink of a thrilling transformation. The adoption of AI and data-driven solutions is revolutionizing not just how we approach health care challenges but also reinventing the strategies that drive market success. Let's look at how leveraging cutting-edge technology and analytics can unveil new horizons in pharmaceutical strategy, segmentation, health care systems and patient engagement.
The visual representation validates the testament to the power of AI and data in revolutionizing the pharmaceutical industry. It serves as a guide to the potential use cases that can drive innovation, efficiency and patient-centric solutions. As you consider the implications of these advancements, remember that each icon and segment is a gateway to deeper insights and strategic foresight. We encourage you to reflect on how these elements can be integrated into your organization's fabric to leverage the full potential of AI.
Pharma's struggle with seamless AI implementation
- Misaligned strategies: As the pharmaceutical industry transitions from a traditional sales-driven approach to a model focused on partnership with health systems, AI use cases must evolve in tandem. It's crucial for pharma companies to align their strategies with this shift, ensuring that they not only enhance the efficiency of their sales teams but also prioritize the needs and experiences of their stakeholders. This means moving beyond the “next best action” for field force to developing a cohesive ecosystem that dynamically responds to customer requirements.
- Fragmented approach: To maximize the impact of AI, pharma companies must weave these technologies into their existing tech infrastructure. Ongoing refinement of their systems and data capabilities is essential to evolve from pilot projects to a dynamic environment ready for AI microservices.
- Cultural shift: Pharma companies aiming to capitalize on AI must prioritize reshaping their business processes and nurturing an environment conducive to change. This requires equipping staff with the skills to leverage AI tools, manage data with care and embrace new operational methods. The commitment to democratizing AI paves the way for its transformative benefits throughout the organization.
- Disorganized and guarded data: When companies obtain data of poor quality or at a high cost, the effectiveness of AI use cases diminishes. This lowers their adoption rates and also slows down the progress of a data-driven strategy within the company. AI solutions are often not fully integrated with a company's main IT system and data models. This, coupled with scattered digital skills, makes it tough for these solutions to scale up and gain acceptance across different markets and functions.
- Concentrating solely on execution: Pharma companies often prioritize the deployment of AI systems without ensuring their effective adoption and use. A successful AI transformation extends beyond implementation; it requires a change in both processes and people's behaviors. This includes managing the transition, fostering data literacy, soliciting actionable feedback and continuously refining the solutions.
- Conflicting deployment trade-offs: Pharma companies must decide between building their own AI solutions or partnering externally, with the choice depending on strategic value and integration potential. To ensure success, it's crucial to have a tech and data setup that supports easy integration of AI, maximizing the benefits of any partnership or in-house development.
- Internal development hurdles: Pharma companies navigating internal AI development must strike a balance between business-led use case definition and IT-driven execution. Business units should drive the prioritization of AI initiatives, while IT ensures these initiatives align technically and integrate smoothly with the broader infrastructure.
Synthesizing solutions to these challenges is not merely about survival — it's about setting a course for sustainable growth and leadership in an industry where change is the only constant. The adept use of democratized AI is a beacon of innovation, lighting the path to modernized, responsive and patient-centric pharmaceutical practices.
Exceptional AI case studies spearheading pharma evolution
Leveraging advanced technology is crucial for maintaining a competitive edge. Below are three case studies that illustrate how strategic deployment of GenAI and AI-driven analytics platforms can drive innovation, operational efficiency and personalized health care solutions within the industry.
Each case underscores the importance of synchronizing technology with business strategy to achieve tangible results and pave the way for future advancements.
- Case Study 1
- Case Study 2
- Case Study 3
AI strategy and roadmap development for a leading pharma company
Context: A tailor-made GenAI strategy and implementation roadmap were established, addressing the unique business conditions of international regions, achieving congruence with the broad digital and IT frameworks for enhanced scalability at a global pharmaceutical enterprise.
Situation: Diverse levels of maturity and autonomy across various regional offices and disparities in understanding between management and regional teams presented a complex scenario. This called for a nuanced GenAI approach that would integrate into global digital and IT strategy initiatives smoothly.
Strategic initiatives: Comprehensive assessments of existing GenAI resources were conducted, along with a capture of cross-regional needs and aspirations within the international operational spectrum. This resulted in a prioritization of the most vital use cases, a detailed look into GenAI maturity and an alignment with external partnership potentials.
Strategic outcomes: Following the board of directors' approval and allocation of substantial funds for transformation, a three- to four-year GenAI strategic vision was crafted. This vision identifies actionable GenAI use cases that offer clear commercial benefits and aligns them with the company's technical capabilities and market readiness. To support this vision, a detailed 12- to –18-month GenAI strategic plan was developed, outlining the necessary steps for a comprehensive transformation. This plan encompasses the technical upgrades required for AI integration, the development of data governance structures and the upskilling of the workforce to ensure seamless integration of AI technologies.
Real- world evidence data platform advancement for a pharma leader
Context: An AI-driven real- world evidence (RWE) data platform was developed to refine and personalize the engagement between health care professionals and a leading global pharmaceutical company.
Situation: With the goal of improving health outcomes, the company needed an RWE data analytics platform to equip medical science liaisons (MSLs) with deep insights for personalized health care professional (HCP) engagement across various therapeutic areas.
Strategic initiatives: Identification of key instances that would strengthen the field medical teams' engagements and designing a user experience focused on maximizing the data platform's utility for outcome-driven interactions.
Strategic outcomes: A global RWE data platform was successfully conceptualized and deployed, pooling diverse data sets for real-time analytics and insights. This platform became central to the transformation of the company's medical affairs function into an insight-driven operation. An agile pod deployment model was introduced to accommodate expansion and adoption across multiple countries, aiming at significant revenue growth within a projected period.
Enabling data-led optimization in a leading pharma firm
Context: A leading pharmaceutical company embarked on developing an enterprise data management and governance architecture to fully leverage the potential of AI and analytics.
Situation: The journey toward becoming a data-centric organization was met with challenges: data siloed and scattered across multiple analytical platforms, decentralized business teams and an absence of company-wide coherent data practices.
Strategic initiatives: A deep dive analysis into the existing data setup was performed to identify gaps. Progress was made toward a mixed data and AI operational framework and the creation of a centralized data and AI center of excellence. These measures were enacted to orchestrate and streamline the data-related activities across the enterprise.
Strategic outcomes: The creation of a bespoke enterprise-wide data management and governance protocol yielded a centralized data and analytics office. An innovative data architecture was established, encompassing key concepts like data-driven insights and self-service analytics. The organization has now successfully embedded over a dozen business data governance initiatives, ensuring consistent data integration and adherence to regulatory and corporate standards.
Patrick Grünewald's vision for sustained transformation
EY Partner Patrick Grünewald, shares insights on driving sustainable impact by energizing teams with the right use cases and transforming tech and behaviors.
Why EY professionals are your go-to collaborator for AI leadership in pharma
From strategy inception through to technology implementation and beyond — covering user experience, workforce, operations and regulatory facets — we're here to propel you into AI leadership. Working with EY professionals means securing a collaboration that empowers your pharma company with the resources and guidance required to navigate and dominate the AI domain.
Here are some more compelling reasons why collaborating with EY professionals will position your pharma company at the pinnacle of AI leadership:
Commercial acumen: Recognized as a trusted advisory in pharmaceutical consulting, the EY organization brings extensive commercial strategy experience and operational knowledge.Leveraging top-tier commercial understanding and therapeutic area insights, the EY team ensures that AI strategies are intricately tailored to meet both market conditions and your distinct business objectives, surpassing the offerings of general technology providers.
Profound technological expertise: The global EY organization’s deep-seated proficiency in digital frameworks, technology innovation and AI functions is complemented by a comprehensive history of successful implementations — a contrast to strategy-only establishments.
Impartial strategic insights: EY professionals maintain an objective stance in its consultancy, prioritizing your company's ambitions above all and providing unbiased recommendations that aim to boost your pursuit of AI excellence in the pharma landscape.
US$1.4 billion vote of confidence in AI: The global EY organization’s strategic commitment to AI is marked by a US$1.4 billion investment, affirming its position as a leader in AI-driven solutions. This investment fuels the development of in-house frameworks and models, leveraging the expertise of the EY organization's 34,000-strong global team of data, analytics and AI professionals.
EY.ai, a unifying platform, brings together multidisciplinary experience with our leading-edge AI capabilities and a holistic ecosystem to help you drive AI-enabled business transformations with confidence.
EY professionals provide consultancy that traverses the full AI implementation spectrum — from strategy formation to the meticulous crafting of technology, enhancing user experiences, aligning teams, improving operations and ensuring robust compliance with regulatory and legal standards.
EY teams’ experience ensures that your AI strategy is not only envisioned but also meticulously executed. For a collaboration that embodies the pinnacle of professionalism and forward thinking, we invite you to start a conversation with us.
Summary
The pharmaceutical industry is embracing a shift that brings artificial intelligence (AI) from the hands of a few experts to the core of its business operations. This shift necessitates a deep, foundational change, embedding AI into the decision-making process and daily workflows to drive productivity and growth. This article highlights the critical steps for this transformation, including comprehensive digitization, adapting go-to-market strategies and integrating advanced technologies. By doing so, pharma companies can secure a competitive edge and pave the way for a resilient, patient-focused future that fully capitalizes on AI's capabilities.