RALEIGH, N.C., CAMBRIDGE, England and CHENNAI, India, July 24, 2025 /PRNewswire/ -- A new survey of scientists and informaticians reveals that while investment in artificial intelligence (AI) and machine learning (ML) is rapidly accelerating across the R&D, manufacturing, and clinical value chain, persistent data silos and integration headaches are stalling progress -- raising crucial questions about whether science-focused companies are truly ready to harness the full power of AI.
The Data Readiness Survey, conducted by Zifo Technologies, polled scientists and informaticians from over 30 science-driven companies, revealing both enthusiasm for AI's transformative potential and persistent challenges in data management and integration.
While nearly two-thirds of organizations have begun investing in AI and ML across their value chains, only 32% of respondents express high confidence in their company's ability to leverage scientific data effectively for AI initiatives.
A striking 70% report that accessing the data needed for AI projects is either difficult or somewhat difficult, underscoring a widespread struggle with data accessibility. Data is not harmonized in terms of storage or metadata, the report noted, highlighting the lack of standardized practices that hampers progress.
Data silos, interoperability, and automation gaps emerge as major obstacles. Nearly half of organizations find it "somewhat difficult" or worse to pipeline and integrate data from lab instruments, with aging infrastructure and a lack of common standards complicating seamless data exchange. Automation of data capture is growing, but 26% still rely primarily on manual processes, and 10% use no automation at all. Adoption of standardized data formats and ontologies is mixed, with 39% agreeing their organization has them, and an equal proportion either unsure or disagreeing.
The report also noted that a critical gap exists in current data management solutions for High-Performance Computing (HPC) environments. Most systems, such as Electronic Lab Notebooks (ELNs), are not designed to handle the petabytes of unstructured data generated during complex analysis. While the initial capture of instrument data and the final storage of analyzed data products are well-automated, the crucial intermediate processing stage on HPC systems remains poorly supported.
As Zifo's Chief Scientific Officer Paul Denny-Gouldson observes, "Data management is fundamental to ensuring data reuse and data retrieval, because that is the lifeblood of what enables FAIR [Findable, Accessible, Interoperable, Reusable] data".
Despite these hurdles, AI adoption in R&D is advancing, with 39% of organizations reporting moderate adoption and another 26% noting minimal uptake.
The focus for AI applications is strongest in research (32%) and development (27%), with clinical, manufacturing, and precision medicine also showing growing interest. Organizations are prioritizing targeted, incremental AI interventions rather than large-scale, disruptive deployments. "The true value is increasingly seen in integrating AI and machine learning into routine workflows across the entire value chain," Paul said.
Key challenges identified include data quality issues (27%), data privacy concerns (23%), and integration difficulties with lab instruments (21%). Privacy anxieties have been amplified by the rise of generative AI, prompting many organizations to develop in-house solutions to retain control over proprietary data.
The report also states that because organizations tend to be large and diverse, and each department is focused on producing results with available technologies, it is difficult to gauge each department's ability to use AI-driven programs to enhance productivity and efficiency.
When asked about the greatest potential benefits of AI, respondents highlighted accelerated discovery, increased efficiency and cost savings, and enhanced scientific insights. "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," Paul notes. These intermediate gains -- faster research cycles, optimized processes, and deeper data-driven insights -- are seen as stepping stones to better products and, ultimately, improved outcomes for patients and consumers.
Looking ahead, Zifo emphasizes that data standardization and seamless data exchange among different lab instruments are critical for science-driven industries to fully realize the promise of AI. "The current era could be called 'The Age of Data Management,' which will eventually lead to 'The Age of AI,'" the report concludes. Companies that invest in robust data infrastructure, cross-functional collaboration, and targeted AI applications will be best positioned to turn data into discovery -- and innovation into impact.
To Read and Download the Data Readiness Survey Report, please click here: https://zifornd.com/blogs/early-days-for-ai-but-scientific-data-management-gains-momentum/
About Zifo
Zifo is the leading global enabler of AI and data driven enterprise informatics for science driven organizations. With extensive solutions and services expertise spanning research, development, manufacturing, and clinical domains, we serve a diverse range of industries, including Pharma, Biotech, Chemicals, Food and Beverage, Oil & Gas, and FMCG. Trusted by over 190 science-focused organizations worldwide, Zifo is the partner of choice for advancing digital scientific innovation. https://zifornd.com/; https://zifornd.com/practical-ai-blueprints/
About Zifo:
Zifo is the leading global enabler of AI and data driven enterprise informatics for science driven organizations. With extensive solutions and services expertise spanning research, development, manufacturing, and clinical domains, we serve a diverse range of industries, including Pharma, Biotech, Chemicals, Food and Beverage, Oil & Gas, and FMCG. Trusted by over 190 science-focused organizations worldwide, Zifo is the partner of choice for advancing digital scientific innovation.
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