New AI Initiative Integrates Laboratory Instruments, Intelligent Automation and Scientific Informatics, Opening New Growth Opportunities for the Analytical Instrumentation Industry

MILFORD, Massachusetts – October 13, 2026 – <Entity value="Waters Corporation" category="company" disambig="Analytical laboratory instruments and scientific software"/> (NYSE: WAT), a global leader in analytical laboratory technologies, has announced the launch of Waters AI Link, a new software capability designed to connect artificial intelligence (AI) agents with laboratory instruments, analytical data and automated experimental workflows.

By integrating Anthropic's Model Context Protocol (MCP) into its waters_connect informatics platform, Waters aims to transform traditional liquid chromatography-mass spectrometry (LC-MS) operations into intelligent, AI-directed laboratory workflows. The initiative connects research and development activities with downstream method development and regulated quality-control processes, ultimately extending the workflow into Waters' established Empower Software environment.

The launch represents an important step toward the development of the next-generation intelligent laboratory, where AI does not simply analyze scientific data but also helps design experiments, coordinate instruments, execute analytical procedures and use experimental results to guide subsequent research.

1. From AI-Assisted Research to Automated Laboratory Experiments

One of the most significant features of Waters AI Link is its support for automated laboratory experimentation, commonly described as a "lab-in-the-loop" approach.

Traditionally, AI-assisted scientific research has largely focused on computational activities, including literature analysis, molecular prediction, experimental design and data interpretation. However, translating these computational predictions into physical experiments still requires scientists to manually configure instruments, prepare samples, initiate analytical runs and interpret results.

Waters AI Link is designed to reduce this gap between computational research and laboratory execution.

Through a natural-language interface, scientists can connect AI agents and large language models (LLMs), including Anthropic's Claude, with waters_connect Software. These agents can support activities such as creating analytical methods, preparing sample lists, assigning instruments, initiating data acquisition, processing results and coordinating connected laboratory automation systems.

In a lab-in-the-loop workflow, the AI model generates an experimental hypothesis, the laboratory instrument performs the physical measurement, and the resulting analytical data is returned to the AI system for further evaluation.

The model can then refine its predictions and propose the next experiment, creating an iterative cycle of hypothesis generation, physical testing, data analysis and experimental optimization.

This approach has the potential to significantly reduce repetitive manual operations while increasing experimental throughput and improving research productivity.

2. Connecting waters_connect with Empower Software

Another important aspect of Waters' strategy is the continuity of scientific workflows from early-stage research to regulated pharmaceutical manufacturing.

Waters uses two major informatics platforms to support different stages of the scientific process.

waters_connect Software provides instrument control, data acquisition and scientific workflow management for LC-MS research and development applications. It serves as the foundation for connecting AI agents with analytical instruments and laboratory automation.

As molecules advance through development and approach regulated quality-control environments, workflows can transition into Empower Software, Waters' established chromatography data system and compliant system of record.

This connection is particularly important for pharmaceutical companies, biotechnology organizations and contract research laboratories.

In regulated environments, analytical results must be supported by reliable documentation, traceability, data integrity and appropriate quality controls. Laboratories cannot simply replace established compliance procedures with unrestricted AI-generated decisions.

By connecting research workflows with downstream regulated informatics infrastructure, Waters aims to help laboratories maintain scientific and operational continuity as molecules progress from discovery and development toward quality-control release.

The company's broader strategy is to make AI increasingly useful throughout the entire analytical lifecycle, rather than limiting AI applications to isolated research tools.

3. Anthropic MCP and the Future of Intelligent Laboratory Automation

Waters AI Link also introduces Anthropic's Model Context Protocol (MCP) into the waters_connect ecosystem.

MCP is an open standard designed to allow AI models and agents to communicate with external software applications, data sources and connected systems through a common interface.

For laboratory users, this creates new possibilities for interacting with analytical instruments through natural-language instructions instead of relying exclusively on traditional software menus and manual configuration.

In addition to AI Link, Waters has joined Anthropic's Model Hardware Standard (MHS) research program.

MHS is intended to simplify the integration of AI agents with physical laboratory devices, including analytical instruments, liquid handlers and robotic systems.

Historically, integrating laboratory instruments from different manufacturers has often required weeks or months of customized software engineering. Standardized hardware communication could potentially reduce this integration process to hours or minutes.

The longer-term vision includes AI agents capable of coordinating experiments, adjusting parameters as analytical results become available, managing connected laboratory equipment and supporting overnight experimental operations.

Together, MCP and MHS represent two complementary developments: connecting AI agents to laboratory software and enabling those agents to interact with physical laboratory hardware.

4. AI Could Become a New Growth Engine for Laboratory Instruments and Consumables

Beyond software innovation, the launch of AI Link carries important commercial implications for the analytical instrumentation industry.

As AI accelerates scientific hypothesis generation, laboratories may need to conduct substantially more physical experiments to validate computational predictions.

Every additional experiment can require analytical measurements, sample preparation, chromatography columns, solvents, consumabl