Key Takeaways
- Artificial Intelligence (AI) adoption grows but confidence lags: Only 32% of scientists and informaticians feel confident in leveraging scientific data for AI initiatives, highlighting foundational data challenges across biopharma.
- Data access and integration remain top hurdles: 70% of respondents report difficulty accessing data for AI, citing issues like siloed systems, manual capture, and outdated infrastructure.
- Standardization is critical to unlocking AI’s value: With just 39% of organizations using standardized data formats and ontologies, the industry must prioritize data harmonization to move from the “Age of Data Management” to the “Age of AI.”
A new global survey from Zifo Technologies reveals that while many scientists and informaticians are beginning to integrate artificial intelligence (AI) and machine learning across the R&D, clinical, and manufacturing value chain, significant challenges remain around data infrastructure. Despite growing momentum, only 32% of respondents expressed confidence in their company’s ability to effectively leverage scientific data for AI initiatives.
The Data Readiness Survey, which included participants from more than 30 science-driven organizations, highlights both the accelerating adoption of AI and the persistent data-related hurdles facing biopharma. Among the most pressing issues: 70% of respondents reported difficulty accessing the data needed to support AI projects, pointing to ongoing problems with standardization, siloed systems, and data integration. Manual data capture, lack of interoperability, and aging infrastructure further undermine progress. Notably, most data management tools, such as electronic lab notebooks (ELNs), are ill-equipped to handle the vast, unstructured data generated in high-performance computing environments—leaving a critical gap in the AI pipeline.1,2
Are Biopharma Companies Ready to Harness the Full Power of AI?
“The current state of AI and machine learning adoption and integration within scientific organizations is still in its early stages,” said Paul Denny-Gouldson, chief scientific officer, Zifo Technologies, in the report. “A key focus is on understanding and educating scientists about the necessity of high-quality data that is well documented with metadata. This ensures the data can be effectively utilized for algorithm development and machine learning initiatives. While AI can assist in data cleaning, the primary challenge lies in establishing a foundation of clean, well described data. The speed at which this landscape changes over time will be a key indicator of progress. If minimal improvement in data accessibility for AI is observed in the near future, it might suggest that current strategies are insufficient or that this is a more protracted issue.”
Areas of AI Interest Across the Value Chain
Despite these hurdles, interest in AI remains strong, with 32% of respondents indicating enthusiasm for AI applications in research and 27% showing interest in development. Additionally, 18% are exploring clinical applications, while 11% noted interest in manufacturing quality control, and 8% in precision medicine. Five percent said they remain unsure which areas of AI to prioritize moving forward.
Perceived Benefits and Long-Term Goals
When asked about the greatest potential benefit of AI in biotech and pharma, 30% of respondents cited a combination of increased efficiency and cost savings, accelerated discovery, enhanced scientific insights, and improved patient outcomes. The remaining respondents identified one of these individual benefits as most impactful.
Laying the Groundwork for the Age of AI
According to the report, standardizing data and enabling seamless exchange across research, development, manufacturing, and clinical operations is becoming essential for science-driven sectors such as pharma, biotech, chemicals, and related industries. The authors suggest that today’s emphasis on strengthening data infrastructure may ultimately define this period as the “Age of Data Management”—a foundational era paving the way for the “Age of AI.”2
“While improved patient outcomes should ideally be the end goal, the survey results suggest that respondents are also strongly focused on other tangible benefits of AI,” concluded Denny-Gouldson, in the report. “These include increased efficiency and cost savings, accelerated discovery, and enhanced scientific insights. These intermediate goals are often seen as direct pathways to achieving better patient outcomes, as improvements in these areas contribute to the development of better products and processes.”
References
- Zifo's Global Survey Reveals Early Momentum for AI in Biopharma, But Data Readiness Remains Key Hurdle. PR Newswire. July 24, 2025. Accessed July 25, 2025. https://www.prnewswire.com/news-releases/zifos-global-survey-reveals-early-momentum-for-ai-in-biopharma-but-data-readiness-remains-key-hurdle-302513061.html
- Early Days for AI but Scientific Data Management Gains Momentum. Data Readiness Report. Accessed July 25, 2025.
