Is Your R&D Data Ready for the AI Revolution?
The buzz around AI in the chemical and pharmaceutical industries is deafening. Everywhere you look, companies are talking about using machine learning and advanced analytics to drive faster innovation, optimize processes, and make better decisions.
However, there's a critical prerequisite that many organizations are overlooking as "Many organizations start the conversation with AI/ML possibilities — whether AI can suggest conditions, be more suggestive or predictive. But we have to check the ground reality. Many times the foundation is not strong — they have not yet moved their data onto a digital system, and then they expect the system to provide AI/ML", Dr Manish Khandagale, Revvity Signals.
The good news? AI's transformative potential in R&D is within reach — and the path forward starts with understanding where your data stands today. A recent webinar revealed that while many chemical and pharma companies are still building their data foundations, those who invest in data readiness now will be best positioned to capture AI's full value. In other words, before you can unlock the power of AI in your R&D workflows, you need to ensure your data is ready.
The 5 Levels of R&D Data Maturity
So where do you start? The path to AI-ready R&D begins with understanding where your organization stands today. Data maturity isn't binary—it's a progression. Organizations typically evolve through five distinct stages, each building on the capabilities of the last.
One can consider 5 key stages of R&D data maturity that organizations need to progress through:
- Paper and Spreadsheets: If your experimental records, material tracking, and process data are still primarily captured on paper and in Excel, you're at the lowest level of maturity.
- Digital but Disconnected: Even if you've digitized your data, it's likely still siloed across multiple systems that don't talk to each other.
- Structured and Searchable: Once your data is connected into a unified platform, the next step is ensuring it is standardized, indexed, and searchable.
- Knowledge Mapped: Going beyond just structured data, true AI-readiness requires building context and meaning.
- AI-Assisted R&D: At the highest level of maturity, your R&D environment is fully integrated with AI capabilities. This includes everything from automated experiment summarization to generative models that can propose new reactions and connect the dots across hundreds of historical trials.
Where Does Your Organization Fit In?
Take a moment to assess where your own R&D data stands on this maturity ladder. Are you still trapped in paper and spreadsheets? Have you connected your systems but struggle with data silos? Or are you ready to start deploying AI models to drive the next phase of innovation?
Wherever you are today, the key is recognizing that AI is not a silver bullet. It's an outcome — one that can only be achieved by building a strong foundation of connected, structured, and contextualized data.
Manish Khandagale
Senior Field Application SpecialistDr. Manish M. Khandagale is a Senior Field Application Specialist on the Revvity Signals Team. He has extensive experience in pharmaceutical research and development, as well as scientific support roles in analytical instruments and scientific software industry. He is responsible for providing technical and strategic support to drive our mission of empowering scientists to make data-driven decisions by utilizing our informatics solutions