The next era of discovery: AI, data, and scientific innovation.

In the following Q&A, Revvity leaders Bryan Kipp, senior vice president, technology & licensing, James Maniscalco, chief technology officer of Revvity Signals Software, and Arvind Sundar-Rajan, senior vice president, chief digital & strategy officer, discuss how AI is shaping drug discovery, the growing importance of data and partnerships, and the need for responsible innovation to advance scientific progress.

How is AI helping accelerate the timeline from scientific discovery to real-world patient impact?

ASR: AI is helping researchers pursue diseases that may have historically been too complex to study at scale while accelerating the path to new therapies. Solutions like the Signals XyntheticaTM platform can help improve decision-making, speed research, and ultimately shorten the timeline from discovery to patient impact.

BK: Upstream, AI is transforming how researchers identify and evaluate drug targets. Advances in protein engineering, protein folding, and cellular targeting are helping scientists better understand disease biology and design therapies with greater precision.

AI is often associated with efficiency, but how does it change the volume and nature of lab work required?

BK: Rather than reducing the need for laboratory science, AI is expanding it by helping researchers connect genes, RNA, proteins, cellular pathways, and disease mechanisms in entirely new ways.

Those insights create more opportunities to identify novel drug targets and therapeutic approaches, but they also increase the need for experimental validation and translational research. AI will accelerate the pace of discovery while increasing the scale and sophistication of the scientific work required to bring new therapies to market.

ASR: AI enables researchers to complete more decision cycles in less time, which increases the need for wet lab work. We increasingly see this evolving into a 'lab-in-a-loop' model, where in silico discovery and experimental validation continuously inform one another. Laboratory data strengthens and refines AI models over time, while AI helps guide the next round of experimentation.

The quality of the AI is directly tied to the quality of the underlying experimental data.

JM: AI may create efficiencies in some areas, but it will ultimately increase the importance of trusted research platforms, systems of record, and scientific data environments that support experimentation and decision-making. AI doesn't replace those systems, it amplifies them.

BK: This is where Revvity is particularly well-positioned, as much of our life sciences portfolio supports the wet lab workflows needed to validate and characterize biological activity.

AI may help narrow the decision tree upstream, but biology remains highly complex, meaning those insights still need rigorous experimental validation. In many cases, the resulting laboratory data also helps strengthen and refine the AI models themselves.

What is the importance of partnerships in scaling the benefits of AI across the industry?

ASR: Partnerships are central to scaling AI because no single company has all the expertise, data, and technology required. Collaborations across the ecosystem help create better solutions for researchers and ultimately better outcomes for patients.

JM: We will increasingly see collaborations between life sciences companies, cloud infrastructure providers, and frontier AI developers as AI reshapes how research is conducted.

BK: Through collaborative partnerships, the industry is beginning to move beyond building AI infrastructure and toward real-world execution. There is growing momentum around the 'lab of the future', highly connected, automated laboratory environments built around a 'lab-in-a-loop' approach, where AI can help guide experimentation, analyze assays, and accelerate iterative scientific workflows.

What would you say to those who view AI as a disruption risk rather than an opportunity in life sciences?

ASR: AI should be viewed far more as an opportunity than a threat. It allows researchers to process complexity at unprecedented scale, accelerate discovery, and focus more time on higher-value scientific problems and innovation.

JM: AI has the potential to fundamentally improve how scientists interact with technology and derive value from data. Historically, software often required users to adapt to the limitations of the interface, but AI changes that dynamic.

Researchers will increasingly interact with systems naturally through language, images, audio, and context-driven prompts, making software more intuitive and responsive. In laboratory settings, that means scientists can automate routine tasks, create experiments more seamlessly, surface insights faster, and interrogate complex datasets in entirely new ways. Importantly, this enhances, rather than replaces, scientific expertise and core systems of record.

As AI becomes more embedded in drug discovery, what does “responsible AI” mean in a scientific context?

ASR: Responsible AI means deploying the technology ethically, securely, and in alignment with organizational values and regulatory expectations. At Revvity, teams across legal, cybersecurity, HR, finance, and digital functions work together to evaluate AI through lenses including ethics, privacy, compliance, and security.

JM: As AI becomes more integrated into research workflows, issues such as bias, data integrity and appropriate use become increasingly important. We are focused on ensuring AI systems operate responsibly, transparently, and with scientific rigor.

BK: Responsible AI in life sciences also starts with trust, data stewardship, and patient privacy. As AI systems gain access to larger biological datasets, maintaining patient trust and protecting sensitive information will be critical.

What is the most underappreciated way AI could positively impact human health over the next decade?

JM: AI could fundamentally change the economics of drug discovery and expand research into rare diseases that have historically been underserved by reducing the time and cost required to test and refine hypotheses.

BK: AI also has the potential to shift healthcare from reactive treatment to continuous, personalized health optimization by helping interpret longitudinal health data and identify risks earlier.

ASR: Beyond accelerating therapies, AI could have an equally transformative impact on diagnostics and preventative health by helping people identify risks earlier, make more informed decisions, and take a more proactive approach to managing their health.