Our thinking

New frontiers: How AI is transforming the life sciences industry

Taking the pulse of AI in the life sciences sector and exploring how organizations can maximize opportunities from this rapidly evolving technology to build healthier futures

Embracing change

The convergence of artificial intelligence (AI) and life sciences is no longer a distant promise. Companies operating in the sector are actively embracing the technology and are already achieving measurable results. In the following exclusive report from White & Case, in association with Mergermarket, this new reality is explored in depth. Drawing on a proprietary survey of senior executives spanning human pharma and biotech, healthcare provision, medical devices and animal health, the report provides a comprehensive overview of where the sector stands and where it may be heading.

Recent market data demonstrates the scale and urgency of this shift. AI in the pharma market alone is projected to reach US$25.7 billion by 2030, up from around US$4 billion today, according to market research firm Mordor Intelligence. AI-driven drug discovery is also expected to exceed US$20 billion by 2030, per research organization Grand View Research, as firms seek faster routes to novel compounds and more precise trial matching. These forecasts underscore that AI is much more than merely a back-office optimization tool; it is becoming integral to how life sciences companies design, test and deliver therapies, with growing expectations from regulators, investors and patients alike.

Our findings confirm this transition of AI from experimentation to practical application. Tools are being embedded in product design, trial optimization, diagnostics, drug target identification and commercial execution. Organizations are also adapting internally—reassessing governance structures, workforce capabilities and legal frameworks to ensure AI can scale sustainably and in compliance with complex legal frameworks. Board-level involvement is growing, and forward-looking investment strategies are being developed to match the pace of innovation.

This research explores the sector's priorities and pain points in detail. The report begins by mapping current use cases and business goals, showing how companies are deploying AI to address real operational needs—from shortening development cycles to improving diagnostic accuracy. It then turns to the structural challenges that remain, including the legal and regulatory complexities surrounding general AI deployment and use, data protection, intellectual property (IP) and cross-border compliance. These risks are shaping how organizations approach partnerships, procurement and policymaking.

Investment is a central theme. Budgets are shifting from discretionary pilots to embedded line items, with many companies pursuing joint ventures, acquisitions or internal buildouts to accelerate capability development. Local sourcing is often favored, but appetite for cross-border expansion remains in markets with advanced regulatory pathways or concentrated AI talent.

In conclusion, the report examines how success is being defined and why it matters. Metrics such as diagnostic accuracy, cost reduction, and patient access are becoming essential to both internal planning and external validation. Encouragingly, the vast majority of respondents believe AI will improve patient outcomes, while investors increasingly view AI maturity as a signal of innovation-readiness and long-term value creation.

With AI moving rapidly up the agenda in boardrooms and regulatory agencies, understanding how to scale responsibly and legally is critical. This report offers a grounded view of what effective AI adoption in life sciences looks like today—and where the next key opportunities and risks lie.

Methodology

In 2025, White & Case, in partnership with Mergermarket, surveyed 200 senior executives of life sciences organizations. The organizations surveyed included human pharma and biotech companies (75), healthcare providers (50), medical device companies (50) and animal health companies (25). Respondents from each company type were split equally between EMEA (66), Asia-Pacific (67) and North America (67).

The state of the market

organic molecules

Opportunities in AI

petri dish

Patient, commercial and regulatory concerns

DNA sequences

How companies are investing in AI

MRI scans

Conclusion: A healthy future for AI in the life sciences arena

Orion spacecraft

Five key takeaways

stethoscope
stethoscope

Five key takeaways

Insight
|
2 min read

With the learnings of this report in mind, five priorities emerge for companies aiming to translate their AI ambitions into concrete, lasting advantage:

1: Define success before scale

The organizations that adapt most strongly to the AI revolution will be those that decide early what success really means. Whether that is diagnostic accuracy, access/equity, cost reduction or speed of trial activation, measurable targets need to be set up-front. That clarity helps in allocating resources, selecting projects and comparing outcomes, making pilots more likely to scale and investments less likely to be wasted.

2: Prioritize high-impact, data-ready use cases

The biggest gains will come from areas where data is cleaner, workflows are less burdened and feedback loops are tight. To achieve results, AI should not be deployed across functions for multiple purposes. Organizations need to choose use cases where underlying data readiness, regulatory alignment and measurable outcomes are favorable. ROI is consistently higher when AI is used selectively for well-scoped high-value problems rather than spread thin across the enterprise.

3: Build governance and legal clarity as enablers, not blockers

Across practical obstacles and legal concerns, three issues arise repeatedly: data security, IP/licensing and legal uncertainty. While these are undoubtedly challenges, they can also unlock investment when properly addressed. Companies that already have AI training, documented data provenance and oversight are more likely to meet investor and regulator expectations and are thus more likely to successfully scale. Embedding governance early avoids slowdowns later.

4: Investor optics matter

Failure to adopt AI effectively will damage attractiveness to investors. That means life sciences companies must treat good AI strategy, clean metrics and credible execution as signals to capital providers. High-quality AI execution can influence valuations, ease of access to venture or equity financing and the terms of partnerships. For companies in need of capital, the difference can be meaningful.

5: Patient outcomes will define reputational and regulatory success

Operational gains are necessary and will attract interest. However, the ultimate litmus test will be whether patients benefit from improved diagnostics, more precise and effective interventions, and better access to treatment. For regulators and payers, the priority is whether a drug, device or service is safer, fairer or more effective. Companies that build their AI with patient outcome metrics at the core will be better positioned both ethically and commercially.

White & Case means the international legal practice comprising White & Case LLP, a New York State registered limited liability partnership, White & Case LLP, a limited liability partnership incorporated under English law and all other affiliated partnerships, companies and entities.

This article is prepared for the general information of interested persons. It is not, and does not attempt to be, comprehensive in nature. Due to the general nature of its content, it should not be regarded as legal advice.

© 2026 White & Case LLP

Top