How AI in the Water Laboratory Can Drive Smarter, Faster Results

Although many labs still rely on manual review and traditional analysis methods, a growing number are exploring AI to support tasks like real-time monitoring, interpreting results, and predictive forecasting. For labs navigating high sample volumes and tight budgets, AI tools aren't just futuristic—they're increasingly practical to save time and money.

Here's a closer look at AI and machine learning (ML), how they're already transforming lab workflows, and what it could mean for the future of water quality testing.

A Brief Definition of AI and ML

AI performs tasks that typically require human intelligence, such as analyzing data, recognizing patterns, and making decisions. In lab and water testing environments, AI can streamline and automate these steps, freeing up team members to focus on higher-level tasks.

ML is a branch of AI that enables computers to learn from data and improve with experience but without being explicitly programmed. For example, ML models can be trained on historical testing data to detect contaminants, predict outcomes, and track water quality trends. This evolving technology offers water labs new opportunities to enhance accuracy and efficiency.

Smarter Data Analysis and Contaminant Detection

AI excels at recognizing patterns, making it especially useful for analyzing the complex datasets that water labs generate. Traditional methods often require a technician to analyze readings, identify outliers, and manually flag abnormal results. With AI, labs can reduce the risk of human error and the time spent on routine tasks, like flagging anomalies.

AI-powered models can process sensor and spectral data to identify contaminants, such as PFAS, heavy metals, and microbial pathogens. One recent study on machine learning and water quality demonstrated how algorithms can rapidly distinguish between clean samples and those containing dangerous pollutants. This can boost lab efficiency and open the door to real-time or near-real-time monitoring—especially valuable in drinking water systems where early detection can prevent threats to public health downstream.

Predicting Problems Before They Start

AI's usefulness isn't limited to analysis after the fact. It can also help predict when and where water quality issues might arise. In the lab setting, predictive models can flag when equipment calibration or sample integrity are at risk or patterns in test results suggest an upstream problem, such as agricultural runoff or infrastructure corrosion.

These alerts allow labs to respond in time to act rather than waiting for failures or compliance violations. As demand increases for source water monitoring, early warning systems could become essential. AI-driven forecasts can help utilities and regulators plan ahead, allocate resources, and protect public health more effectively.

Integrating AI with Laboratory Information Management Systems (LIMS)

One of the most promising applications comes from tying AI into LIMS. These platforms already handle data processing and recordkeeping for many labs. When enhanced by AI, they can go further, automatically flagging inconsistencies, prioritizing samples for review, or generating real-time quality alerts. AI-powered LIMS platforms can enable laboratories to use AI to reduce data entry errors, analyze trends across batches, and improve operational performance.

AI integration can also help improve compliance. By continuously cross-checking results against regulatory thresholds or method requirements, AI can help ensure that labs meet standards, like those set by the Environmental Protection Agency, The NELAC Institute, or the International Organization for Standardization. This can not only improve defensibility but also reduce audit preparation time.

AI Proves Its Worth

While many labs are still investigating AI, a few applications offer a glimpse of what's possible. At Utah State University, researchers developed an AI-powered tool to monitor potential contamination events in rural water systems. Using local sensors and water flow volume from the National Water Model, the tool forecasts spikes in E. coli and other indicators upstream of municipal water sources.

Virginia Tech's AI and Cyber for Water and Agriculture (ACWA) lab is another leader in the space. The lab focuses on protecting water resources through cyberbiosecurity and AI-enabled detection systems. The ACWA lab's interdisciplinary work includes water testing along with policy recommendations and systems design.

Preparing for the Promise of AI

AI can be a powerful tool for water labs seeking to improve accuracy, efficiency, and responsiveness. By supporting smarter data analysis, predictive modeling, and seamless LIMS integration, AI can help labs meet rising expectations while continuing to protect public health and the environment.

Adopting AI comes with requirements, including access to high-quality data, strong IT systems, and data privacy safeguards. Still, as in other industries, AI will likely play an increasingly important role in lab operations. Now is the time for water test labs to learn what's possible.