AI and Its impact on Laboratories and LIMS Solutions

Darren Mahoney, the driving force behind LabWare’s Analytics Product Development, is both a trend watcher and a trendsetter. He envisions a future where artificial intelligence (AI) and data analysis fundamentally transform laboratory operations, enhancing scientific workflows and boosting efficiency. With AI taking over routine tasks, researchers can concentrate on what truly matters: innovation and discovery. LABinsights asked Mahoney ten questions about the role of AI and machine learning (ML) in tomorrow’s labs and in the next generation of Laboratory Information Management Systems (LIMS).

1. AI is rapidly transforming various sectors. How do you expect AI will impact laboratories in general and LIMS solutions in particular?

At LabWare, we see AI/ML transforming laboratories in countless ways. The first drug discovered using Generative AI has already been submitted to the FDA. When it comes to LIMS solutions, AI enhances functionality and unlocks new capabilities that address common bottlenecks. More automation means less time spent on manual data processing—and more time to improve processes.

AI saves lab users time and reduces costs for organizations. It elevates automation to a new level, freeing teams from repetitive tasks and enabling them to focus on meaningful, high-impact work. Additionally, AI plays a role in accelerating discovery, making lab work more engaging and rewarding.

2. Is the laboratory sector ready to embrace AI and ML? Are these technologies already being implemented, or is there still a long way to go?

A: The sector is absolutely ready. AI and ML come up in nearly every strategic conversation we have with customers and colleagues. Early adopters are already realizing the benefits. We’re at a tipping point—moving from “we’d like that” to “we’re doing it.” Scientists want to spend more time on their core scientific work, and that is fueling strong interest in AI-driven solutions.

We’re seeing broad adoption across sectors—contract labs, food and beverage, and pharmaceutical and biopharmaceutical industries. Practical applications are emerging fast, from predicting water quality and end-product outcomes to automating audits in the pharmaceutical industry.

3. When we discuss machine learning (ML) in laboratory environments, are we primarily referring to machine learning (data-driven models) or instrument learning (device optimization)? How does LabWare distinguish between the two?

A: Both are important, but as a software company, LabWare’s primary focus is on data-driven machine learning. We apply ML to the data our customers generate within our systems and their labs. Significant effort has gone into capturing instrument data across the industry and integrating this into our platform. Users benefit from this and can use the data to optimize their laboratory processes.

4. There are different approaches to AI—some are language-driven, such as NLP-based systems, while others are more industrial, focusing on automation and predictive analytics. Which form of AI does LabWare primarily implement in its LIMS solutions?

A: Our primary focus is on automation and predictive analytics. In laboratories, solutions must be reliable, repeatable, and explainable. Everyone in the tech community is excited about emerging AI technologies. Like our users, we are learning from AI, and in collaboration with them, we are developing Generative AI.

The potential of these technologies is enormous, but it needs to be translated into tangible value. Our customers are looking for concrete ROI and consistent outcomes, which is why our emphasis remains on industrially oriented AI and ML.

5. What are the key requirements for a successful implementation of AI in a lab environment? What factors need to be in place for an effective AI-driven LIMS?

A: At LabWare, we take the real-world challenges our customers face as our jumping-off point. This ensures that everyone stays focused on solving the problem rather than becoming sidetracked by any one particular technology.

6. What infrastructure and data management capabilities are required to integrate AI into LIMS solutions?

A: The infrastructure and data management functionalities depend on the complexity of the AI or ML solution an organization wants to implement. Many ML applications can be implemented with minimal infrastructure upgrades. However, more advanced use cases—like those involving neural networks or generative AI—may require more advanced hardware.

7. What skills and training do laboratory staff need, in addition to technology, to effectively work with AI-optimized LIMS? How do you help laboratories bridge this knowledge gap?

A: For lab staff, workflows must remain intuitive and seamless. Whatever technology is running in the background, AI and ML must be integrated into the system in a way that delivers clear efficiency benefits for your staff. The user experience is essential for building trust in AI models. Staff don’t need to understand the technical details of AI models—they just need to understand and trust that the system is supporting their work effectively.

8. How does LabWare ensure that AI-driven insights are accessible and useful for laboratory staff? What strategies are used to turn AI functionalities into practical laboratory improvements?

A: This is exactly what our team at LabWare has been focused on for years. AI and machine learning can sometimes provide insights that are not immediately intuitive. That’s why we invest heavily in explaining our solutions. Users really appreciate that, especially when AI insights go beyond what traditional tools reveal. By keeping the user and their specific challenges at the center, we turn complex technologies into meaningful, practical improvements.

It all starts with clear project scoping and well-defined, understandable objectives. AI is no different in this regard. People’s familiarity with AI varies greatly. As AI has moved from academia into everyday lab environments, awareness, and comfort levels have grown. But for some, it can still feel like “magic.” That’s why open communication and managing expectations are so important.

To explain AI and ML in laboratories and create a common language, we have hosted “Tech Talks” at our customer conferences over the past few years. These sessions help demystify AI and turn the hype into tangible results. We say “Results Count” for a reason.

9. How does LabWare’s AI-driven data visualization transform raw data into actionable insights for laboratory staff?

A: For us, this isn’t just about AI—it’s about our people: how well do they work and listen? The better we understand what lab staff actually need, the better we can deliver the right visualizations. It’s about delivering the correct data, in the right format, at the right time. Once we’ve established that foundation, we build machine learning tools around it.

This process is often iterative. It takes time, feedback, and fine-tuning to get it exactly right. Our customers also benefit from innovations happening across the broader ecosystem, which we integrate into our solutions.

10. How have LabWare’s AI-driven solutions helped laboratories identify areas for improvement and reduce operational costs?

A: Our customers see real benefits from built-in data analysis and machine learning within LabWare—without needing to migrate data elsewhere. This saves time and effort, reduces errors, and is more cost-effective. Solutions like our “Data Science Engine” and “Lab Intelligence,” provide automated insights and trend analyses, helping labs identify inefficiencies and continuously improve their processes.