As AI becomes an increasingly foundational tool in life sciences R&D and lab workflows, the pressure to produce clean, high-quality and context-rich scientific data has never been higher. AI models are only as effective as the data they’re trained on, yet many labs today still grapple with fragmented instrumentation, manual workflows, disconnected analysis tools and inconsistent metadata capture. These challenges not only hinder scientific productivity but also significantly limit the reliability and impact of AI/ML applications across the R&D pipeline.
In this webinar, the featured speakers will explore how scientific organizations are addressing these gaps by modernizing and automating their lab and analytics workflows. Attendees will hear how automation can streamline the capture, processing and structuring of experimental data — creating AI-ready data assets that are reliable, reproducible and traceable. The speakers will also examine the role of rich metadata, standardized context and structured workflows in elevating both the scientific and operational rigor of experiments.
Through real-world examples and best practices, this webinar will highlight how integrated automation and analytics platforms are empowering R&D teams to spend less time wrangling data and more time accelerating discoveries.
Whether dealing with high-throughput assays, managing complex instrumentation or preparing for AI initiatives, this session will provide practical steps to align data strategy with scientific ambitions.
Speakers
Nick Floeck, Head of Automation & Analytics, Benchling
Nicholas Floeck is a veteran product leader with over 15 years of experience shaping data and AI/ML-driven software in complex, highly regulated industries. He currently serves as Head of Automation & Analytics at Benchling, where he leads product strategy for instrument data management, automation and analytics. Prior to Benchling, Nick held senior product leadership roles at Guidewire and Verisk Analytics, focusing on insurance data management and embedded AI/ML to accelerate workflow and policy development decision-making.
Message Presenter
Nari Kang, Product Manager, Automation, Benchling
Nari is a Product Manager at Benchling, where she leads Benchling Connect, focusing on integrating lab instruments and automating data workflows to accelerate R&D productivity and data integrity. With over a decade of experience in R&D informatics, she has delivered enterprise-scale laboratory software solutions for biotech and pharmaceutical organizations, helping modernize how science is done.
Message PresenterWho Should Attend?
This webinar will appeal to:
- R&D and lab operations leaders seeking to modernize lab data infrastructure
- Scientists and assay developers managing high-throughput or complex workflows
- Data scientists and informatics professionals building or supporting AI/ML use cases that need high-quality data and improved collaboration with wet lab teams
- IT and digital transformation leaders focused on automation and data quality
- Individuals involved in scaling scientific workflows or improving decision-making through data
What You Will Learn
Attendees will learn:
- Why consistent, high-quality data is critical for AI success in R&D
- How to automate instrument data capture and workflow processing across experiments and assays
- Strategies to embed rich metadata and context for improved traceability and data integrity
- About the impact of automation on scientific productivity and reproducibility
- How automation and analytics platforms accelerate time-to-insight and power AI/ML model development
Xtalks Partner
Benchling
Benchling is a cloud-based platform for biotechnology research and development. Benchling helps scientists accelerate the full R&D lifecycle with our R&D Cloud, which consists of data, collaboration, and insights built on a trusted and open platform.
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