Generative artificial intelligence (GenAI) is revolutionizing the pharmaceutical and life sciences sectors, offering new opportunities for drug research and development, manufacturing process optimization, and patient interaction. However, the transition from the experimental phase to large-scale implementation faces numerous challenges.
According to Deloitte’s “State of generative AI in the enterprise” report, major barriers include issues related to data management, regulatory compliance and governance difficulties.
The Data Challenge
One of the main barriers to large-scale GenAI adoption is data quality and security. 55 percent of companies avoid some uses of GenAI because of data issues, such as the use of sensitive information and compliance with privacy regulations.
According to the Deloitte report,75 percent of companies have increased investment in data lifecycle management to address these challenges, with a focus on security (54 percent), quality (48 percent), and updating data governance frameworks or developing new data policies (45 percent).
Experts suggest that companies that want to maximize the value of initiatives in generative artificial intelligence leverage their distinctive data in innovative ways, whether to optimize existing language models, develop new ones or integrate advanced business solutions.
“For GenAI to generate the impact expected by executives,” they warn, “it will be critical for organizations to become more confident in the use of their proprietary data, which may be subject to changing regulations.”
Deloitte’s “State of generative AI in the enterprise”
Companies also need to invest in data management and protection infrastructure, adopting clear governance frameworks and labeling strategies to ensure transparency and traceability of information used in GenAI models.
Regulatory Obstacles
Regulatory uncertainties are a significant drag on GenAI adoption.
36 percent of pharmaceutical companies cite regulatory compliance as a major barrier, especially with the introduction of regulations such as the European Union’s AI Act. IQVIA’s 2025 Safety and Regulatory Compliance Trends and Predictions for Pharma and Biotech white paper highlights how technology integration in pharmacovigilance and medical information management is occurring at an accelerating pace, leading to a growing need for regulatory standardization. To address this environment, companies should adopt proactive strategies to anticipate regulatory developments, such as implementing compliance monitoring systems and collaborating with regulatory bodies to ensure transparency and security in GenAI development processes.
Governance and Scalability
GenAI governance is one of the least mature areas in GenAI companies. Only 23% of organizations consider themselves highly prepared to manage the risks associated with generative AI. Moreover, 68% of companies have brought less than 30% of their experiments with GenAI into production, a sign of the difficulties in integrating the technology into business processes.
Addressing the scalability of GenAI requires a well-structured approach to integrate the technology into business processes in an effective and sustainable way. A key element is thecreation of centers of excellence for GenAI, which can serve as reference points for sharing best practices and standardizing the methodologies adopted.
In addition, it is critical to establish clear governance frameworks in which roles and responsibilities are well defined, thus ensuring effective management of the technology. Finally, investing in staff training and skills development is essential to ensure that teams are adequately prepared to manage and optimize the use of GenAI at scale. According to analysis by Biopharmed, a head hunter specializing in medical personnel, todaypharmaceutical companies are increasingly looking for professionals with soft skills and a strong background in new technologies, particularly experts in artificial intelligence.
