Beyond the Hype: The Real Impact of AI Integration in R&D

Introduction

Artificial intelligence has moved from the periphery to the center of scientific discovery. But its most transformative impact isn't coming from flashy demonstrations or futuristic promises, it's emerging through thoughtful integration into the informatics platforms and workflows scientists use every day, made possible by AI that’s finally accessible to working researchers.

In this candid conversation, Kevin Willoe, President Revvity Signals, David Gosalvez, Chief Strategy Office, Revvity Signals and Josh Bond, Head of Product Management at Revvity Signals, share their perspectives on where AI is delivering genuine value in R&D, how embedded intelligence is reshaping scientific workflows, and what it takes to build AI tools that researchers actually want to use. From platform architecture to partnership strategies, they explore the practical realities of implementing AI at scale, and how AI is evolving from a specialized capability to a core layer of the R&D experience that enhances how scientists think, collaborate, and discover.

Q&A With the Leadership Team

Q: Why is now a pivotal moment for AI in scientific research?

Kevin Willoe (President, Revvity Signals Software):

We’re at a point where AI is both accessible and practically applicable. For years, the industry has talked about how AI could transform drug discovery & development, reduce experimentation cycles, and improve decision-making. But we’ve reached a new phase, it’s no longer theoretical.

Large language models and user-friendly platforms have put powerful AI tools directly into scientists’ hands, without requiring deep technical expertise. In pharma, for example, AI can now support better decisions at every stage, from target identification to formulation.

In materials science, it reduces the reliance on trial-and-error. The value is tangible, but realizing it requires thoughtful integration, not just bold vision.

Q: Where are you seeing AI deliver the most meaningful impact?

David Gosalvez (Chief Strategy Officer, Revvity Signals):

One of the biggest shifts is how scientists interact with data. Rather than needing to manually extract insights, they can now work with AI to summarize documents, interpret results, and even recommend the next best action.

For example, we’ve seen AI copilots embedded into research applications, providing intelligent summarization, semantic search, and context-aware suggestions. These features remove friction and allow scientists to focus more on experimentation and less on admin. That’s powerful.

Q: How does platform architecture affect AI effectiveness?

Josh Bond (Chief Product Officer, Revvity Signals):

It’s everything. A lot of organizations try to layer AI on top of fragmented tools. That’s rarely effective. You need a unified platform, one that integrates ELNs, chemical design tools, analytical dashboards, and knowledge capture systems.

When your infrastructure is connected, AI can operate across workflows. That’s when it becomes intelligent, not just in one task, but across the entire R&D lifecycle. That connectivity is what makes AI useful at scale, not just impressive in demos.

Q: What does it mean to embed AI, rather than just offer AI features?

Kevin Willoe:

Embedding means the user doesn’t have to think, “Now I’m using AI.” Instead, AI quietly improves the experience, making it faster to summarize data, easier to find relevant literature, or more accurate to predict experimental outcomes.

It’s about putting intelligence where it belongs: inside the tools scientists are already using, helping them work faster and more confidently.

Q: What’s your philosophy for building AI that scientists actually want to use?

David Gosalvez:

Start with the scientist. We look at pain points, what slows them down, what distracts them from actual discovery, and then we ask how AI can remove that friction.

It’s not about showing off what AI can do. It’s about asking what researchers need and designing intelligent workflows that feel intuitive. AI is just the enabler.

Q: Are organizations ready for this shift? What’s the adoption curve like?

Josh Bond:

We’re seeing a significant shift. Many organizations already realize that siloed systems are slowing them down. The appetite for unified, AI, enabled platforms is growing because teams need to move faster, but they also need confidence in their decisions.

The challenge isn’t just building AI, it’s making sure the underlying data is AI-ready. That means structured, clean, and contextual data that AI can understand. Without that, even the best algorithms won’t deliver.

Q: What excites you most about what’s next in this space?

Kevin Willoe:

I’m excited by the idea of moving from insight to foresight. AI won’t just summarize what happened, it will help recommend what to do next. That’s where things are heading: prescriptive, context-aware science.

David Gosalvez:

For me, it’s seeing the pace of research increase without sacrificing quality. When scientists are freed from manual tasks, their creativity comes through. AI can’t replace that, but it can unlock more of it.

Josh Bond:

We’re just getting started. As AI tools evolve and more specialized models emerge, the ability to personalize research support at the individual or team level becomes very real. That’s going to change how science is done, not just faster, but smarter.

Beyond the Hype: The Real Impact of AI Integration in R&D — Artificial intelligence has moved from the periphery to the center of scientific discovery.

Conclusion

The role of AI in science is changing, from a specialized capability to a core foundational layer of the R&D experience. It’s about enhancing the way scientists think, collaborate, and discover. That is, amplifying human expertise rather than competing with it.

Through embedded AI, connected platforms, and a focus on usability, scientific organizations are beginning to shift how research is done, from fragmented systems to intelligent environments.
As the tools mature, the distinction won’t be between who uses AI and who doesn’t, but between those who treat AI as a feature, and those who treat it as an integrated partner in discovery.