AI Market in Pharma to Surge Over 600% by 2034, Yet Challenges Remain: Report

The report, titled "Artificial Intelligence at the Helm: Revolutionizing the Life Sciences Sector," highlights AI's transformative impact on drug discovery, clinical trials, personalized healthcare, and precision medicine.

The artificial intelligence (AI) market in pharmaceuticals is projected to reach $16.49 billion by 2034, while AI-driven medical devices are expected to grow to $97.07 billion by 2028, according to a recent report released by EY-Parthenon and Microsoft.

The report, titled "Artificial Intelligence at the Helm: Revolutionizing the Life Sciences Sector," highlights AI's transformative impact on drug discovery, clinical trials, personalized healthcare, and precision medicine.

However, despite its vast potential, the widespread adoption of AI across the pharmaceutical industry continues to face significant challenges.

Key Barriers to AI Adoption

The report identifies three primary categories of challenges that are slowing down AI adoption in pharma.

Ethical concerns include algorithmic bias and a lack of transparency in AI-driven decision-making, which raises questions about fairness in treatment protocols and the potential for discriminatory outcomes.

Technical challenges are also a major obstacle, with data privacy, security issues, and complex regulatory compliance creating hurdles for large-scale AI integration.

As regulations evolve, pharma companies must take a strategic and informed approach to ensure compliance.

Operational barriers, including a shortage of AI-skilled professionals and resistance to change, further slow adoption.

The report notes that as AI automates repetitive tasks, professionals in the life sciences sector will need to transition toward more strategic, AI-augmented roles to maximize its benefits.

Roadmap for Successful AI Integration

The report outlines five key pillars for successful AI adoption in the pharmaceutical industry. The first is implementing AI-first business and operating models, embedding AI-driven decision-making across all functions.

This requires technology stack enhancements to support large-scale AI deployment and continuous innovation.

A strong AI-ready data strategy is critical to maintaining security, compliance, and accuracy and ensuring that the data infrastructure effectively supports AI applications.

Workforce readiness is another essential pillar, with a need for change management and interdisciplinary skill development to equip employees with the expertise required to work alongside AI-driven technologies.

The final pillar emphasizes the necessity of robust risk and compliance frameworks to govern AI applications, ensuring transparency, cybersecurity, and regulatory adherence. Companies risk ethical pitfalls, data breaches, and operational inefficiencies without these measures.

Industry Leaders on AI's Growing Role

Suresh Subramanian, National Lifesciences Leader at EY-Parthenon India, stated that AI is no longer a futuristic concept but a fundamental force reshaping life sciences.

He emphasized that AI is accelerating drug discovery, optimizing clinical trials, and revolutionizing pharmaceutical manufacturing, bringing efficiencies across the entire value chain.

However, he warned that successful AI adoption requires a structured approach rather than fragmented experimentation.

The AI Maturity Framework, developed by EY-Parthenon, provides a roadmap to help pharma companies transition from isolated AI initiatives to enterprise-wide transformation.

It ensures that organizations investing in AI maturity today will emerge as industry leaders of the future.

Trupen Modi, Senior Industry Executive for Pharma and Life Sciences at Microsoft, highlighted AI's impact on manufacturing and supply chain processes, improving efficiency and reliability.

He also noted that AI is reshaping the regulatory landscape by automating document analysis, regulatory submissions, and compliance monitoring, reducing time to market while enhancing accuracy.

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