BCG-WEF Project: Digital Health Care Transformation

Strategic Choices in the Age of AI: Shaping the Future of Life Sciences

AI and increasingly connected data ecosystems are reconfiguring the life sciences value chain, with implications for how innovation is generated, translated, and delivered to patients. While digital technologies have already raised the industry’s performance baseline, AI is shifting the focus from optimizing the current value chain to redefining its structure, thereby expanding the range of viable strategic positions and forcing more explicit choices about where and how companies compete.

Competitive advantage is no longer anchored in incremental improvements but in how organizations position themselves within a fundamentally evolving system.

Analysis by the World Economic Forum and BCG identifies two fundamental shifts. First, in R&D, AI removes scarcity in research by dramatically increasing the volume and speed of hypothesis generation. As a result, the bottleneck shifts downstream, from discovery to development, translation, and evidence generation. Competitive advantage increasingly depends on the ability to convert a larger share of ideas into approved, adopted, and scalable interventions, supported by continuous learning systems that integrate clinical and real-world data.

Second, in market access and distribution, AI and digital interfaces reshape how demand is formed, how decisions are influenced, and how products reach patients. Multiple access pathways are emerging in parallel (provider-led, direct-to-patient, and new intermediary-driven models), each requiring distinct capabilities, operating models, and economic logic.

Across these shifts, three strategic approaches emerge in both R&D and commercialization. In R&D, companies may position themselves as AI-supported science champions, asset integrators, or development engines. In access and distribution, they may reinforce provider-led pathways, build direct patient relationships, or challenge traditional distribution models. These approaches are not mutually exclusive, but pursuing all simultaneously risks strategic dilution. Leaders must therefore make deliberate choices and align capabilities, organization and investment accordingly.

The WEF-BCG analysis has two immediate implications. First, organizations must move beyond isolated digital initiatives and build integrated, life cycle–spanning capabilities, supported by interoperable data and embedded across core processes. Second, leaders must make explicit choices about where and how to compete within a reconfigured value chain. Incremental adaptation is no longer sufficient; sustained advantage requires aligning capabilities, operating models, and investment around a clear strategic position.

Looking ahead, the pace and direction of transformation will depend on alignment between industry and the public sector. Regulatory frameworks, reimbursement models, and data governance will shape which approaches can scale and where capabilities concentrate.

As AI reshapes both the creation and delivery of innovation, life sciences companies must transition from optimizing existing models to building fundamentally new ones, defining how value is created, captured, and sustained in the decades ahead.

Earning Trust for AI in Health: A Collaborative Path Forward

Health care systems globally face growing pressures: rising costs, workforce shortages, and persistent inefficiencies. In this context, AI offers transformative opportunities to enhance patient outcomes and optimize system performance. But realizing AI’s benefits in health care requires responsible development, rigorous evaluation, and a deliberate focus on building trust among stakeholders.

Today’s regulatory frameworks, designed primarily for pharmaceuticals and medical devices, are not fully suited to manage the probabilistic, dynamic nature of AI technologies. Traditional evaluation methods, which emphasize pre-market validation, struggle to accommodate AI systems that evolve post-deployment. As AI adoption accelerates, regulatory models must evolve accordingly.

New research from the World Economic Forum and BCG identifies three urgent priorities to earn trust for AI in health:

Address fragmentation and build technical capacity. Current AI ecosystems are fragmented, and many health leaders lack a deep understanding of AI technologies. Health systems must build technical literacy among decision makers so that they can critically assess and responsibly integrate AI solutions.

Adapt evaluation and regulatory frameworks. New approaches, such as regulatory sandboxes, post-market surveillance, and life-cycle monitoring are essential. Guidelines must complement legislation to enable innovation while maintaining high standards of safety and effectiveness. Independent quality assurance resources and real-world testing environments, such as those being developed under initiatives like the Testing and Experimentation Facility for Health AI and Robotics (TEF-Health), can support more dynamic development.

Promote public–private collaboration. Public–private partnerships should move beyond consultation to active codevelopment of evaluation standards and monitoring frameworks. Such collaboration is vital to ensure that regulatory practices keep pace with AI innovation while safeguarding patient trust and public health objectives.

The WEF-BCG research also emphasizes the importance of global coordination. Divergences in AI regulatory approaches across regions—especially between the Global North and Global South—risk creating barriers to the scalable deployment of AI in health care. Capacity-building efforts, particularly in underresourced health systems, are crucial to ensure equitable benefits from AI advances.

Ultimately, the future of AI in health care must be grounded in adaptability, transparency, and shared responsibility. By strengthening evaluation processes, building technical capacity, and fostering structured public–private collaboration, health systems can unlock the transformative potential of AI while upholding patient safety and trust and ensuring broader access to innovation.

The path forward demands continuous innovation not only in technology but also in regulation and system design. The time to act is now, to ensure that AI fulfils its promise of delivering better health outcomes for all.

The Future of AI-Enabled Health: Leading the Way

As artificial intelligence and digital technologies fundamentally change the way that industries worldwide do business, health care leaders have a choice to make: Embrace transformative change that profoundly alters how health care is accessed and delivered, or proceed with incremental changes and tools that improve the practice of health care at the margins?

Artificial intelligence has the potential to drive tremendous improvements in health outcomes worldwide and reduce inequities in care. Still, to date the health care sector has been relatively slow to adopt AI at scale.

The World Economic Forum and BCG interviewed more than 75 experts in the public and private health care sectors to better understand the issues hindering progress. Three central but surmountable challenges emerged:

• AI lacks appeal for policymakers and industry leaders because there is no compelling strategy that aligns AI efforts with broader health goals and political priorities.
• Strategic health care goals aren’t integrated with technology choices, leading to missed opportunities to use AI for systemic improvement in health care delivery and outcomes.
• In a fragmented regulatory landscape, a lack of transparency and accountability around AI undermines public trust.

To scale AI and achieve transformative change, health care leaders must take several steps:

• Deliver short-term AI benefits that demonstrate returns and encourage long-term investments.
• Align public and private sector objectives and priorities around AI and agree on how to best share the value it creates.
• Prioritize shared infrastructure investments, such as digital public infrastructures that, where feasible, align private sector services with public good solutions.
• Train leaders at all levels in the technical aspects of health care to provide the skills necessary to make strategic decisions about AI’s potential, limits, and risks rather than deferring technology decisions to others.
• Build trust in AI by improving post-market surveillance to identify AI-related risks, and consider establishing AI ethical committees and principles.
• Advocate for locally controlled data that is globally connected and patient centered to drive innovation and ensure patient safety and privacy.

AI can be a revolutionary force in health care. For that to happen, however, public and private sector leaders will need to act in a new spirit of cooperation focused on creating a sustainable AI health ecosystem. This WEF-BCG report describes the major challenges health care leaders face and the pivotal steps they can and must take to achieve transformative change.