Every year, near the end of January, the Society for Laboratory Automation and Screening (SLAS) hosts an international conference. Traditionally, the SLAS conference has been a place where leading developers can showcase the latest in automated technology and cutting-edge tools. These showcases have, historically, been very focused on advances in hardware—which improves data generation—over software, which typically facilitates data analysis. Exploring the floor, it wouldn’t be surprising to see the demonstrations being dominated by new robotic tools or exciting advances in liquid handling systems. In practice this means that advancement in automation has been driven by the ability to produce more data, with more reproducibility and accuracy.
However, as Scott Hanton, the editorial director for Lab Manager, explored the technology on display at this year’s conference, he noticed the beginnings of a shift as companies move their focus away from hardware and towards software. This change could have large implications both for the technology developers and for the researchers who use these automative tools, as data analysis may soon become the driving factor in automation advancement.
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Shift from push to pull
Over the past few decades, technical progress has led to some truly revolutionary advances in laboratory equipment and automation. These developments created a push-based model where automation was generating more data than could be analyzed by the current—for the time—software. Recent advances in artificial intelligence (AI) and machine learning (ML), however, may cause this push model to invert to a pull-based model.
“The advances I saw this year at SLAS in the data generating hardware were relatively minor in comparison to the progress made in the analysis software, largely thanks to AI,” comments Hanton. Now with easily affordable AI tools, researchers can analyze massive quantities of data in seconds, which seemed impossible 10 years ago. Suddenly, the hardware is struggling to keep up with the speed of the software, meaning data analysis will soon outpace data generation. AI has seemingly catalyzed a paradigm shift in how automation is being approached by equipment developers, as well. Companies must now greatly improve their software on offer if they want to remain competitive.
“This trend was very clear at SLAS this year,” continues Hanton. “Big names in the automation equipment space like Revvity and Agilent were much more interested in talking about their AI and software advances than what I’ve seen in previous years.”
Indeed, Hanton says that when he spoke to Kevin Quick and Chet Murray of Revvity, vice president of platforms and head of public relations and marketing, respectively, they informed him that Revvity intended to expand AI integration to numerous products and workflows over the next few months.
“Revvity wasn’t alone in this focus on AI,” says Hanton. Agilent also reported a major change in focus towards software and services. Lars Hartvig Kristiansen, a general manager at Agilent, told Hanton that they were truly just scratching the surface of what will be achieved with AI.
Says Kristiansen: “AI and ML will be great for scientists. Automation will increase data output by five to 10 times and AI and ML [will be needed] to do that much data analysis to drive to better solutions.”
Impacts across industries
The recent advances in AI and ML are disrupting how many laboratory equipment manufacturers are approaching automation and business. In response, many equipment manufacturers are either shifting their business focus or entering into new partnerships.
Before the introduction of AI, most companies built their hardware to only function with the company’s software. Now, however, the siloing of software IP isn’t feasible. To keep up, organizations that have solely focused on hardware upgrades are now collaborating with software developers or, in some cases, opening up their hardware compatibility so that users can purchase the software of their choice. For example, ABB, a company with 50 years of robotics development experience, entered the life science automation field five years ago and is now collaborating with Mettler Toledo to produce accompanying software.
What the long-term effects of this opening up will be for the automation industry is unknown, but for now it is clear that finding any avenue to be competitive in the software space is a high priority for lab equipment manufacturers. According to Jonathan Gross, the CTO at Labguru, “Key advancements in AI-driven decision-making in the lab will occur in months, not years.”
Whether it’s by new collaborations or through heavy investment in AI and ML technology, the lab automation space is shifting towards a focus on analysis software that can be as fast as and potentially overcome the hardware data generation speed. As with the 2025 SLAS conference, future conferences are likely to showcase greater advances in software than in hardware automation, at least for the time being. In the end, this could herald a new golden age for data generation and analysis as researchers are able to process far more data quicker than ever before.
