From USB drives to unstructured files, fragmented data is holding labs back from AI. We surveyed 100 biotech and pharma IT leaders to uncover the biggest blockers. Take this assessment to gauge your lab’s AI-readiness and get actionable next steps.
of labs surveyed are actively implementing data integration to automate workflows and reduce manual data entry.
of labs surveyed are actively implementing data integration to automate workflows and reduce manual data entry.
of labs surveyed are actively implementing data integration to automate workflows and reduce manual data entry.
of labs in the survey reported their data as FAIR compliant—highlighting widespread challenges in making data findable and accessible.
of labs in the survey reported their data as FAIR compliant—highlighting widespread challenges in making data findable and accessible.
of labs in the survey reported their data as FAIR compliant—highlighting widespread challenges in making data findable and accessible.
of labs structure their data with metadata tags—but 63% of those who do capture it automatically, setting the stage for AI.
of labs structure their data with metadata tags—but 63% of those who do capture it automatically, setting the stage for AI.
of labs structure their data with metadata tags—but 63% of those who do capture it automatically, setting the stage for AI.
of labs in the survey say they have a formal data strategy—but only 49% tie it directly to regulatory milestones like IND filings
of labs in the survey say they have a formal data strategy—but only 49% tie it directly to regulatory milestones like IND filings
of labs in the survey say they have a formal data strategy—but only 49% tie it directly to regulatory milestones like IND filings
of respondents said they currently structure their data with metadata tags or associations, 63% who do capture metadata do so automatically.
of respondents said they currently structure their data with metadata tags or associations, 63% who do capture metadata do so automatically.
of respondents said they currently structure their data with metadata tags or associations, 63% who do capture metadata do so automatically.
The data you already have, trapped in disconnected lab instruments and app silos, can power automation, FAIR data integrity, and a rich foundation for AI. Get a demo or expert advice on how Ganymede can support your data strategy.
Your lab is AI-ready! You have strong data practices and infrastructure in place. Leverage this momentum: consider advanced automation tools or AI-driven platforms to further accelerate R&D. You might explore thought-leadership whitepapers for cutting-edge use cases or even schedule a Ganymede demo to see how to push your capabilities further. Keep striving for innovation, as you’re well positioned to capitalize on AI opportunities immediately.
Your lab has potential to be AI-ready, but there are a few gaps to address. You’re doing some things right (capitalize on those strengths!), but other areas need improvement before you can fully embrace AI. We recommend working through an AI-readiness checklist – for example, ensure your data is centralized, clean, and tagged with metadata, and invest in team training where needed. Strengthening data hygiene and consistency will pay off. With incremental changes, you can quickly elevate your lab’s capability. Focus on those weak spots now, and you’ll unlock more value from your data (and future AI tools) moving forward.
Your lab is not yet ready for AI – and that’s okay. The good news is you’ve identified foundational areas to improve. Start with the basics: establish a centralized data infrastructure (so all your experiment data isn’t scattered), and implement metadata tagging and standard operating procedures for data capture. These steps will create order from chaos, turning that “data swamp” into a usable lake. As you get your data house in order, take advantage of learning resources – for instance, Ganymede’s guides and webinars on lab data management can help your team build competency. By focusing on core data practices now, you’ll build a strong foundation and be ready to tackle AI and automation in the future. Remember, every lab begins this journey somewhere – and you’re now pointed in the right direction.
Ganymede surveyed 100 IT leaders across biotech and pharma to benchmark how teams are managing their lab data, what tools they’re using, and where
Why metadata, integration, and standardization are top priorities in labs today and beyond
How labs are preparing their data infrastructure for automation and AI
Where most organizations still fall short on FAIR data practices
FAIR Principles: Findable, Accessible, Interoperable, Reusable
Practical Lab AI Readiness Starts with Data Management.
Top 5 benefits of digital labs in life sciences R&D.
Digital evolution: Novo Nordisk’s shift to ontology-based data management.
