Why Your Lab Automation Stack Wasn't Built for AI
(And What to Do About It)
Many laboratories find themselves stuck in the early stages of innovation, struggling to scale beyond pilot projects despite the obvious potential of AI. The primary roadblock? A lack of integration between legacy equipment and data infrastructure. In many cases, labs rely on outdated or siloed systems that were never designed to handle the vast amounts of data AI tools require or seamlessly connect with newer technologies.
Pilot projects may showcase AI’s potential, but these successes often don’t scale to larger, more complex workflows due to infrastructure limitations. Many laboratory automation systems were built for traditional manual processes and lack the flexibility needed for AI’s adaptive capabilities. Even with heavy investment in automation tools, without proper software integration, data flow, and cloud infrastructure, these systems operate in isolation, making it difficult to gain actionable insights. Labs need a scalable framework that connects automation with AI-ready data pipelines to overcome these barriers and fully realize the potential of their technology.
Why Legacy Infrastructure Can't Support AI
The core issue holding labs back isn’t the AI models themselves, they’re advancing rapidly and are more than capable of transforming workflows. Rather, the challenge lies in the fragmented, hardware-centric automation stacks that labs have built over the years. These systems were designed to perform specific tasks within isolated environments, not as integrated data and orchestration platforms that can seamlessly support AI-driven decision-making. In short, the infrastructure that powers most lab operations today wasn’t built with the flexibility, scalability, or connectivity required to make AI a true enabler of efficiency and insight. Until these automation stacks evolve into holistic, interconnected systems, AI will struggle to reach its full potential in the lab.
As part of a partnership between CellVoyant and Automata, we have pioneered the integration of AI foundation models with automated cell culture. CellVoyant’s flagship product and Automata’s LINQ™ platform leverage foundation models, computer vision and in silico modelling to enable real-time, non-destructive cell analysis and process optimization. This real-time closed-loop automation delivers vast improvements in the speed of obtaining and quality of cell differentiation data.
What does an “AI-ready” lab actually look like in practical terms?
It means a shift toward a lab environment where every aspect of the workflow is standardized and digitally interconnected. An AI-ready lab captures experimental context from start to finish, ensuring that every step, from sample preparation to final analysis, is logged and standardized in real time. This allows for easier access to data, minimizing the confusion of mismatched formats or incomplete datasets.
With integrated scheduling and orchestration, workflows are automatically coordinated across machines and tasks, reducing manual handoffs and the potential for errors. Machine-readable protocols ensure that the systems can understand and execute procedures autonomously, while version-controlled workflows guarantee consistency and traceability over time. Finally, true AI readiness incorporates feedback loops, allowing AI models to continually improve by feeding insights back into the system to optimize future experiments and operations. This interconnected ecosystem lays the foundation for leveraging AI to enhance productivity and drive smarter decisions.
Automata LINQ enables just that, a connected lab environment that orchestrates workflows and iterates on experimentation through feedback loops.
The Challenge of Fragmented Automation Systems
Most of today’s lab automation was built with a narrow focus on individual instruments such as liquid handlers or plate readers, each with their own dedicated control systems. These point solutions were often tied together through ad hoc scripts with each piece of equipment working independently within its silo. While this setup works for routine tasks, it presents significant challenges when it comes to scaling or integrating technologies like AI.
These systems can be frustratingly unreliable, prone to failure when updates or changes are made. Vendor lock-in also compounds the issue, as labs often find themselves tied to a specific manufacturer's ecosystem, further limiting flexibility. On top of that, visibility across workflows is often limited, leaving data stranded in isolated instruments or within separate ELN/LIMS instances. This fragmented landscape makes it difficult to gain a holistic view of the lab's operations, let alone utilize AI to derive insights or optimize processes.
When AI initiatives attempt to sit on top of such infrastructure, they inevitably stall. These legacy systems are not built to expose standardized, contextualized data that AI requires for deep learning or real-time decision-making. As a result, even the best AI models are unable to work effectively in environments where data is disorganized and therefore inaccessible. Without a cohesive, integrated platform that allows AI to interact with real-time, contextual data and adjust workflows on the fly, any attempt to scale AI will be like trying to run cutting-edge software on outdated hardware, it simply doesn’t work.
Automata LINQ: An Orchestration Platform for AI-Ready Labs
At Automata, we build systems that are not only instrument vendor agnostic but also ELN/LIMS provider agnostic, we are able to bring together different instances of instrument software and ELN/LIMS software to offer a fully orchestrated system. This class of lab platforms treats automation as a combination of hardware and orchestration software, all designed from day one to support AI and advanced analytics.
Key principles of our platform include open integration with any instrument, central orchestration of workflows coupled with a cloud-based layer that connects execution with simulation and monitoring. With modular benches and workcells, our systems are built for scale. Labs can start with a single workflow and easily expand to multi-workcell deployments without having to replace their existing instruments. Designed for collaboration, our platform offers secure, browser-based access, deep version control as well as GitHub sync and last but not least, remote run management. These features make it easier for distributed teams and external partners to share and replicate workflows, ensuring greater transparency and consistency.
Automata has already operationalized this new model through LINQ, a platform designed to close the AI gap in lab automation. LINQ empowers labs to create modular, robot-enabled workcells that can connect any combination of benchtop instruments to offer flexible, scalable workflows that combine human and robotic access. At its core, LINQ features a powerful orchestration layer that allows teams to design complex workflows visually or via code using a Python-based SDK and manage runs from a single, browser-based interface. This centralization of control simplifies lab operations and ensures that teams can rapidly iterate on workflows without dealing with fragmented systems or siloed data.
AI-Ready Labs are Within Reach
The barrier to AI in the lab is not ambition or the power of the algorithms, it's the automation system itself. Many labs are still relying on fragmented, outdated systems that weren’t designed to handle the data-intensive, integrated workflows that AI demands. These legacy systems simply can't support the real-time data processing and orchestration required for AI to make an impact at scale. This is exactly the problem that Automata LINQ is designed to solve. With LINQ, labs don’t need to start from scratch, they can transform their existing instruments and teams into an AI-ready and collaborative ecosystem.
If you're ready to turn your current lab into a high-performing, AI-powered engine for innovation, we invite you to book a live demo with our team at SLAS 2026. Let’s discuss how Automata LINQ can help scale your lab automation and transform the way you work. The future of lab automation is here, let’s make sure you're ready for it.
