{"id":22322,"date":"2025-02-18T14:02:27","date_gmt":"2025-02-18T14:02:27","guid":{"rendered":"https:\/\/makingpharmaindustry.it\/?p=22322"},"modified":"2025-02-18T14:09:57","modified_gmt":"2025-02-18T14:09:57","slug":"genai-the-leap-from-experimentation-to-production","status":"publish","type":"post","link":"https:\/\/makingpharmaindustry.it\/en\/manufacturing\/technology\/genai-the-leap-from-experimentation-to-production\/","title":{"rendered":"GenAI, the leap from experimentation to production"},"content":{"rendered":"

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.<\/strong><\/p>\n

According to Deloitte’s “State of generative AI in the enterprise”<\/a> report, major barriers include issues related to data management, regulatory compliance and governance difficulties.<\/p>\n

The Data Challenge<\/h2>\n

One of the main barriers to large-scale GenAI adoption is data quality and security. 55 percent of companies avoid some uses of GenAI<\/strong> because of data issues, such as the use of sensitive information and compliance with privacy regulations.
\nAccording to the Deloitte report,75 percent of companies have increased investment in data lifecycle management<\/strong> 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).<\/p>\n

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.<\/p>\n

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“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.”<\/p>\n<\/blockquote>\n

Deloitte’s “State of generative AI in the enterprise”<\/strong><\/p>\n

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.<\/p>\n

Regulatory Obstacles<\/h2>\n

Regulatory uncertainties are a significant drag on GenAI adoption.<\/p>\n

36 percent of pharmaceutical companies cite regulatory compliance as a major barrier,<\/strong> 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<\/a> 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<\/strong> to ensure transparency and security in GenAI development processes.<\/p>\n


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