"Soon, Everyone Will Have an AI-Research Assistant."

Interview with Alan Marcus, Chief Growth Officer at LabVantage – adapted from an interview originally published in Dutch in LabInsights (Issue 03/2025)

Considering AI and cloud-connected data, what are the most significant developments in today’s LIMS market? How can laboratories take advantage of artificial intelligence and cloud computing to become more efficient, accurate, and future proof? Alan Marcus, Chief Growth Officer at LabVantage, shares his vision on how to seize the opportunities that AI offers: “In five years, I expect more dark labs in production environments, with even more automation and almost no human intervention.”

The rise of artificial intelligence (AI), cloud computing, and integrated platforms is set to fundamentally change how laboratories operate. Data is the new currency, and innovation is the key to progress. The lab market is on the brink of a transformative shift. LABinsights spoke with Alan Marcus, Chief Growth Officer at LabVantage, who predicts: “Soon, everyone will have an AI research assistant.”

Online Exclusive – In this article, we explore the five key trends shaping the future of LIMS, based on an exclusive interview with an industry expert.

The laboratory world is in the midst of a tech revolution. Where data was once static and fragmented, it is now—or will soon be—dynamic, always connected, and the driving force behind innovation. Cloud-based solutions, AI-driven analytics, and advanced data integration are no longer futuristic concepts. However, these advancements also raise practical questions: How do you migrate existing systems to the cloud? How can AI be leveraged to analyze not just new data but also historical datasets? And how do you ensure compliance with increasingly stringent regulations?

What are the key trends currently influencing the LIMS market?

The biggest development is undoubtedly cloud adoption. More than 80% of our new customers choose a cloud-based LIMS. This offers significant benefits, such as lower IT costs, better disaster recovery, and reduced reliance on local infrastructure. However, migrating existing systems remains a challenge, not just for us but for the entire sector.

AI and machine learning are playing an increasingly significant role in laboratories. How is this impacting LIMS?

AI and ML are particularly valuable for automation, data analysis, and predictive insights. In R&D, this means we can integrate and analyze complex datasets more efficiently. But AI is not just useful for new data—it can also reanalyze historical datasets, preventing laboratories from repeating unnecessary experiments.

How is data analysis evolving within LIMS?

Traditional SQL-based LIMS systems struggle with the increasing volume and complexity of datasets. This has led to a shift toward advanced data management and analytics. In the latest LIMS versions, such as LabVantage 8.9, visualization tools and data-driven workflow optimization are key priorities.

What does this mean for integration with other systems?

LIMS is evolving into a fully integrated platform that seamlessly collaborates with other systems. This means LIMS is no longer a standalone solution but is being linked to ELNs (Electronic Lab Notebooks), LES (Laboratory Execution Systems), and ERP systems like SAP. This integration improves cross-departmental collaboration and accelerates decision-making.

Compliance regulations are becoming stricter, especially with new AI legislation. How does LIMS address this?

Compliance is an increasing challenge, particularly in highly regulated sectors like pharmaceuticals. LIMS helps ensure traceability, transparency, and data integrity. A good example is how pharmaceutical companies work with Contract Research Organizations (CROs): with an integrated LIMS, audit trails, electronic signatures, and data controls guarantee that data remains secure and compliant.

Which trends will further shape the future of LIMS?

We see a clear shift toward LIMS as a central scientific data platform. Just as ERP systems centralize financial data, LIMS should be able to manage all scientific data. Additionally, IoT and digital twins are becoming more important: IoT devices collect real-time data, while digital twins digitally simulate processes. We are also seeing a rise in mobile LIMS solutions, particularly useful in forensic research and environmental testing.

How is AI already transforming LIMS today?

AI is automating routine tasks, improving data analysis, and enabling predictive maintenance. This allows scientists to spend less time on repetitive work and focus on innovation. AI also enhances decision-making, especially in quality control and compliance.

What are the biggest obstacles to adopting these technologies?

The two main barriers are human resistance and regulations. People are naturally cautious about change, and regulations often lag behind technological advancements. A good example is the concept of ‘dark labs’—fully automated laboratories with no human personnel. Technically, this is possible, but from a regulatory and practical standpoint, we are not there yet.

What is your vision for LIMS in five years?

I expect to see more dark labs in production environments, while AI-driven assistants become a standard tool in R&D. Personalized medicine will experience tremendous growth, with genetic data playing a central role in treatment decisions. Additionally, interactions with LIMS will become increasingly intuitive, featuring natural language interfaces and advanced visualization tools that help laboratories work faster and more accurately.