AI-Powered Lab Platforms: From Data Overload to Data Fluency

Beyond SaaS | 1 In labs around the world, scientific teams are applying the software-as-aservice model to generate more data than ever before. And while that data has enormous potential to drive innovation, it’s highly fragmented—created by different instruments, stored in different systems, and interpreted through different points of view. For years, labs have responded to this challenge by centralizing their data. Add another system. Build another data lake. But whether your lab data is spread across ten systems or gathered in a big central location, it’s not usable if it’s not contextualized. That’s where Lab 4.0 comes in, bringing with it the promise of digital transformation as a gateway to better, faster decisions. Centralizing data is no longer enough—today, labs seek to transform that data into smart, contextualized, and actionable insights. At the heart of this evolution are two key innovations. First, there’s the extension of software-as-a-service into its next generation: service-as-a-software, or what we call SaaS 2.0. Then there’s the emergence of agentic AI, which harnesses contextual awareness and scientific domain expertise to understand what users need, even when users themselves aren’t sure. These innovations add up to more than a product update. They’re enabling a whole new philosophy for how work gets done in the lab. It’s about putting lab workers directly in conversation with their data, ensuring they get a meaningful, contextualized response—not after days of hands-on analysis, but in the moment, right when it’s needed. This evolution won’t start with a big bang. It’s a journey made up of small, high-impact changes: one repetitive task eliminated, one key insight surfaced, one workflow simplified. At LabVantage, we’re committed to making that journey with our customers, helping every lab become a lab of the future one step at a time. — Mikael Hagstroem CEO, LabVantage Solutions Inc. This evolution won’t start with a big bang. It’s a journey made up of small, high-impact changes: one repetitive task eliminated, one key insight surfaced, one workflow simplified. At LabVantage, we’re committed to making that journey with our customers, helping every lab become a lab of the future, one step at a time. — Mikael Hagstroem CEO, LabVantage Solutions Inc. Beyond SaaS: How Service-as-a-Software and Agentic AI Are Powering Labs of the Future Beyond SaaS | 2 THE EVOLUTION OF LABORATORY INFORMATICS SaaS 1.0: A Necessary First Step For labs, the first wave of SaaS was revolutionary. On-premise systems gave way to new, cloud-based LIMS platforms offering more flexibility, lower maintenance costs, and improved scalability. No more making do with outdated legacy systems—now labs could access the latest features without a costly upgrade. No more extensive data entry—now labs could count on fast, reliable results without bottlenecks and time-consuming manual tasks. And no more disconnected teams—with a cloud-based platform, labs could collaborate with other areas, like manufacturing, in real time. But with this shift to SaaS 1.0 came new challenges. The volume and variety of data generated in the lab grew exponentially, creating a deluge of analytical outputs, instrument data, sample logs, QC records, and more—much of it trapped in organizational silos, making it difficult to access, analyze, and put to use. Data lakes emerged as a potential solution, but aggregating siloed data in a centralized location only solved half of the problem. Managing and contextualizing large volumes of data isn’t typically a core competency for scientists. They’re trained to generate insights—not to wrangle spreadsheets, navigate siloed systems, or piece together incomplete data sets. But without those steps, lab workers are repeating completed tasks, missing key test steps, and wasting hours hunting for information that should have been at their fingertips. These inefficiencies stem not just from data overload, but from the unusability of that data. And they don’t just slow innovation—they introduce risk, lower reproducibility, and drain resources. To solve these challenges, what labs needed wasn’t a replacement for SaaS 1.0—they needed its natural evolution. Enter SaaS 2.0, designed not only to centralize data but to understand it, connect it, and make it work for science. From Data Access to Data Fluency The core SaaS 1.0 concept of software-as-a-service gave labs a flexible, cloud-enabled platform. By enriching that platform with agentic AI, SaaS 2.0 evolves that dynamic into service-as-a-software. Agentic AI allows lab workers to interact with intelligent agents in natural language. The “service” in service-as-a-software is what happens next: Those AI agents respond to conversational prompts, understand lab workflows, initiate tasks, and surface contextualized data exactly when it’s needed. These embedded AI agents aren’t just chatbots or analytical tools. They’re digital coworkers with extensive, domain-specific training that’s grounded in company and lab data. They’re the gateway to less friction in lab workflows, and more innovation—and they’re fundamentally changing and accelerating the way science gets done. THE “SERVICE” IN SOFTWARE-AS-A-SERVICE Agentic AI allows lab workers to interact with intelligent agents in natural language. The “service” in service-as-a-software is what happens next: Those AI agents respond to conversational prompts, understand lab workflows, initiate tasks, and surface contextualized data exactly when it’s needed. Beyond SaaS | 3 Traditional SaaS and SaaS 2.0: A Comparison Software-as-a-Service (Traditional SaaS) Service-as-a-Software (SaaS 2.0) Core philosophy Cloud-delivered software AI-driven services that enable lab workers to talk to their data What users can expect Manual software operation AI agents that respond to natural language prompts Workflow design Standardized Personalized and adaptive Intelligence Limited automation Context-aware AI that understands scientific workflows and user intent Interoperability Prone to siloed operations Built for interoperability and API integration Data handling Basic reports Predictive and domainspecific analytics Glossary of Terms SaaS 2.0: SaaS 2.0 leverages a “service-as-a-software” philosophy, enabling lab workers to “talk to their data” via AI agents who respond with contextualized, actional insights. Semantic Framework: A semantic framework is a structured, ontology-based system that contextualizes lab data, enabling AI agents to interpret and adapt to scientific environments and provide insightful, accurate information to lab workers. Agentic AI: Agentic AI refers to intelligent, domain-trained agents that understand lab workflows, respond to natural language, and function like digital coworkers to support scientific tasks in real time. Digital Workforce: This term refers to the AI agents embedded in SaaS 2.0 platforms. These digital collaborators are trained to handle repetitive tasks, surface valuable insights, and accelerate research by working alongside scientists. Beyond SaaS | 4 The SaaS 2.0 Philosophy in Practice Human-centered by design What does it mean, practically speaking, to move from data access to data fluency? It means adopting a digital interface that’s designed for the way people really work inside a lab. There may be people with different workflows, different preferences, and different levels of technical proficiency, but they share a need: to get context-rich answers to their most pressing scientific questions, grounded in lab data and ready for fast, effective application at the bench. In other words, lab workers need a way to talk to their data. Not as data scientists writing SQL queries or deciphering complex visualizations, but as people speaking naturally, like you would with a coworker who understands your language, context, and goals. That’s exactly how the intelligent agents powering SaaS 2.0 are designed: as coworkers and collaborators. Alongside people in the lab, they can interpret intent, apply lab-specific logic, and surface answers instantly. They free teams to focus on science, not software. The interactions between lab workers and their digital coworkers don’t stop with the first question. Because these AI agents work within a semantic framework, they can handle follow-up inquiries, observe and adapt to evolving workflows, and refine their responses in real time as users’ needs or goals evolve. Built for the unique challenges of lab work This shift to SaaS 2.0 and a digital workforce only works if that workforce is grounded in truth. There’s no room for AI-driven hallucinations or traceability issues. That’s why the data fabric underpinning SaaS 2.0 is so important, especially in the highly regulated lab environment. The key is to govern AI agents using rigorous data ontologies. That means developing structured, domain-focused guardrails to define how data relates to lab workflows. Using these ontologies, AI agents can build their contextual understanding and apply lab-specific logic to deliver accurate and traceable insights. That means: • Reliable results, not hallucinations: By grounding SaaS 2.0 agents in verified internal data, the risk of encountering a fabricated or misleading response diminishes. • Built-in traceability: Instead of relying on probabilistic “bets,” lab-specific AI agents offer proven data pedigrees to back up critical insights. • Deep domain expertise: AI agents grounded in lab ontologies can offer the contextual fluency necessary to function in a technical and highly regulated environment. “When developing SaaS 2.0 solutions, the focus is often on building the AI agent itself, when instead it should be on the connective tissue—linking the agent to data it can trust.” — Mikael Hagstroem CEO, LabVantage Solutions Inc. Real-world Results: Three Ways That SaaS 2.0 Will Transform Lab Work 1. Smarter searches and deeper insights Until now, navigating petabytes of data from large-molecule studies or reviewing results across multiple sites has been a colossal task, driven by keyword queries and involving hours of hands-on technical work. The concept of “talking to your data,” a core innovation of SaaS 2.0, changes all of that. Using domain-specific large language models (LLMs) refined for lab environments, AI agents can quickly extract relevant knowledge in response to a conversational query from a lab worker. This will dramatically accelerate the process of identifying promising compounds, predicting test outcomes, analyzing supply chain dynamics, and completing other high-value lab objectives. Beyond SaaS | 5 AI Agent in Action Lab worker: “Have we run stability tests on any compound similar to molecule 1234 in the past 6 months?” AI agent:“Yes, stability tests were completed for molecules 4514, 8515, and 1451, each sharing >90% similarity. Here are the summarized results and any deviations noted.” This kind of real-time assistance helps reduce unnecessary effort, saving valuable time and materials. AI Agent in Action Lab worker: “Are there any studies out there relevant to this gene variation and sample type?” AI agent: “I found six relevant studies published within the last 18 months. Would you like me to summarize their conclusions?” As if tagging relevant data with the digital equivalent of a sticky note, AI agents can retrieve meaningful, contextually matched insights—even from external sources. 2. AI-powered automation, personalized for labs Reducing operational overhead is one of the most immediate returns on an investment in the SaaS 2.0 evolution. AI agents can proactively assess lab activities to flag redundancies, identify gaps, and integrate into personalized workflows. This isn’t about replacing lab workers—it’s about freeing them to focus on higher-value scientific work, while a trusted digital coworker handles the repetitive, time-consuming tasks that used to inhibit forward progress. Beyond SaaS | 6 AI Agent in Action Lab worker: “Add Sample #4378 to this run.” AI agent: “Just so you know, you’ve already run this sample. It was tested last week under the same protocol.” Proactive alerts reduce duplicate testing, potentially saving hours—or even days—across busy labs. AI Agent in Action Lab worker: “Finalizing this submission.” AI agent: “Reminder: You haven’t added the stability test that’s typically required for this workflow. Would you like to include it now? Because it understands the procedures unique to a specific lab or workflow, the digital coworker can prevent missed steps that could delay results or trigger costly rework. 3. Compliance that labs can trust Regulatory expectations are continuously evolving, often in the direction of more stringent oversight—particularly in this age of AI. That increases the pressure on labs to prove the pedigree of compliance-related data. Where did it come from? What method was used to generate it? What is its full lineage? Probabilistic methods that rely on pattern recognition to answer these questions won’t stand up to the scrutiny of a regulator, which is why SaaS 2.0 is built on semantic ontologies that enable data centricity. That means every result is traceable back to its source. And because the AI driving SaaS 2.0 is grounded in verifiable internal data and lab-specific logic, it can dynamically evolve in lockstep with shifting compliance requirements. Beyond SaaS | 7 AI Agent in Action Lab worker: “Where did this test result come from?” AI agent: “This result is from sample #8913, run by HPLC on instrument ID 78D in the Liquid Chromatography lab on September 14th, 2024.” With this level of built-in data lineage, every result is verifiable—no guesswork or probabilistic answers involved. AI Agent in Action Lab worker: “Are there any regulatory updates that could impact this workflow?” AI agent: “There are proposed updates to 21 CFR Part11 that could lead to changes in your electronic signature process. Would you like a summary?” Agents can monitor regulatory shifts and highlight potential impacts, helping labs stay ahead of compliance issues. ABOUT LABVANTAGE SOLUTIONS A recognized leader in enterprise laboratory software solutions, LabVantage Solutions dedicates itself to improving customer outcomes by transforming data into knowledge. The LabVantage informatics platform is highly configurable, integrated across a common architecture, and 100% browser-based to support hundreds of concurrent users. Deployed on-premise, via the cloud, or SaaS, it seamlessly interfaces with instruments and other enterprise systems – enabling true digital transformation. The platform consists of the most modern laboratory information management system (LIMS) available, integrated electronic laboratory notebook (ELN), laboratory execution system (LES), scientific data management system (SDMS), and our advanced analytics solution (LabVantage Analytics); and for healthcare settings, a laboratory information system (LIS). We support more than 1500 global customer sites in the life sciences, pharmaceutical, medical device, biobank, food & beverage, consumer packaged goods, oil & gas, genetics/diagnostics, and healthcare industries. Headquartered in Somerset, NJ., with global offices, LabVantage has, for four decades, offered its comprehensive portfolio of products and services to enable customers to innovate faster in the R&D cycle, improve manufactured product quality, achieve accurate record-keeping, and comply with regulatory requirements. For more information, visit labvantage.com. For more information, visit www.labvantageclinical.com. ©2025 LabVantage Solutions, Inc. All rights reserved. LabVantage is a registered trademark of LabVantage Solutions, Inc. Other product or service names mentioned herein are the trademarks of their respective owners. 2025-01 LabVantage Solutions, Inc. 265 Davidson Avenue, Suite 220 Somerset, NJ 08873 Phone: +1 (908) 707-4100 www.labvantage.com Welcome to the Lab of the Future By enabling contextual conversations with data, SaaS 2.0 marks a pivotal shift for lab informatics. Lab workers will be able to collaborate directly with context-aware AI agents to accelerate key decisions, shorten the pathway to breakthrough innovations, and ensure compliance with a rapidly shifting regulatory environment. This may be a big promise, but getting there doesn’t necessarily require a big leap. It starts small: a missed test flagged just in time, a duplicate step avoided, a smarter insight delivered exactly when it can make a difference. These everyday wins are the foundation linking lab teams to their digital platform, enabling a more intelligent, efficient, and future-ready Lab 4.0. TO LEARN MORE ABOUT how our team can bring SaaS 2.0 to your lab, contact us at info@labvantage.com or visit labvantage.com.