SLAS 2026 Recap: "Just Try It" and the Road to Digital Discovery

Navigating AI, Ethics, and Data Quality.

Members of #TeamKC are always looking for ways to improve, learn, and stay connected in our rapidly growing industry. Attending conferences and symposiums is just one of the many ways we help tailor our white-glove services to the unique needs of our clients. Staying up to date on the latest breakthroughs ensures that we remain effective liaisons between cutting-edge technology vendors and the scientific teams we serve. 

Laboratory Informatics Specialist Caitlin Tormey sat down with our team after attending SLAS International Conference & Exhibition 2026 in Boston to give highlights, takeaways, and hot takes regarding the future of laboratory automation. As a consultant specializing in automating data movement and integrating complex instruments into LIMS and ELN systems, Caitlin brought a unique perspective to this year’s event.

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Here's what Caitlin had to say:

Q: What was your goal for attending SLAS 2026?

Caitlin: “My goal in attending SLAS 2026 was to learn about the latest innovations in automation and AI and to better understand the topics top of mind for industry experts. It was a great opportunity to hear where the field is headed. And of course, I was excited to see some really cool lab equipment up close!”

Q: Did you notice overarching themes throughout the conference?

Caitlin: “A clear theme was: just give it a try. Especially with AI, the message across many panel discussions was that the best way to learn is to jump in, experiment, and push the limits of what it can do. That is closely connected to conversations about education and the need to upskill scientists as labs become increasingly automated.”

Q: What’s the most interesting insight you took away?

Caitlin: “One of my biggest takeaways was the clear industry shift from generating massive volumes of data to prioritizing high-quality, meaningful datasets for AI. Building truly predictive models requires integrated multi-omics and rigorously curated data. By leveraging automation to drive precision and consistency, we can finally close the loop on the DMTA cycle with models that deliver real predictive power.”

Q: Did you have a favorite talk, booth, or presentation?

Caitlin: “My favorite series of talks was on AI-Driven Lab Next-Generation Sequencing: Breakthroughs and Challenges in Screening Applications. From using vector embeddings to predict the pathogenicity of genetic variants to integrating neuron villages and multiomics to better understand sex differences in neuropsychiatric disorder risk, the range of topics was impressive. It was a powerful reminder that the technology is broadly applicable and is helping tackle some of the most complex and important challenges in human health.”

Q: What was your “hot take” from the conference?

Caitlin: “One quote that stuck with me was: “Einstein didn’t need a bench to figure out relativity.” It challenges us to imagine a future where scientists may not always need a physical wet bench to drive discovery. As computational and agentic AI systems become more powerful, elements of hypothesis generation and experimental design may increasingly shift into the digital realm. I’m not ready to say we can abandon lab work altogether, but it does push us to think critically about where we, as scientists, provide the most value.”

Q: What conversations or themes from the conference do you hope will carry forward into broader industry dialogue? Is there anyone specific you had/heard these conversations from?

Caitlin: “A theme I hope continues to gain momentum is the discussion around ethics in laboratory innovation. Several sessions explored the responsible use of AI, including a thoughtful debate on its role in scientific publications, and it was encouraging to see SLAS take this seriously. I also appreciated insights on sustainability from James Connelly of My Green Lab. As innovation accelerates, pairing it with accountability and environmental responsibility is more important than ever.”

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The insights from SLAS 2026 reinforce a growing truth: the ‘modern lab’ is no longer about a physical space, but a sophisticated data ecosystem. Whether it’s accelerating the DMTA cycle through smarter automation or navigating ethical complexities of AI, the goal remains: drive faster and more impactful discovery. 

Collaboration at events like these drives innovation, and at Karchem Consulting we’re dedicated to helping teams bridge the gap between complex laboratory workflows and the digital systems that support them. We ensure our clients are equipped with the strategic foresight and foundation to thrive in a bench-augmented future. 

Curious to learn how we support automation for the "Benchless” lab? Contact us to get started.