In the specialty chemicals industry, scientists must continually develop new products and formulations, to meet demands for increased performance, improved sustainability, and lower cost. With data management software that harnesses artificial intelligence (AI), companies can streamline processes, accelerate new product development, and boost profitability.
 

A Modern Data Platform Facilitates Continuous Product Improvement

New product development—whether the goal is a polymer, coating, additive, or battery material—generates enormous amounts of data. Efficiently accessing and analyzing this data is critical for success.

With modern data management software like Revvity Signals Research Suite, R&D organizations capture and organize scientific experiments and data in a single platform. They can search experiments, ingredients, and formulations across the enterprise, and analyze data efficiently, for better-informed decisions.

When this data-management software is enhanced with AI and machine-learning (ML), users benefit from additional capabilities and even greater power. It all starts with structured data.
 

Structured Data Enables AI Technology

As specialty chemical companies seek to leverage the power of machine learning and AI, they often face a significant roadblock: Their data are siloed, unintegrated, and unstructured. At a recent SupplySide West conference, 70% of attendees cited “lack of structured data” as a major barrier to adopting enterprise-level AI technology.

A modern data management platform like Signals Research Suite tackles this challenge. By correctly unifying and modeling data to be fully AI-ready, the platform makes it possible for data to be interrogated, analyzed, and evaluated by AI and machine-learning tools.

Optimizing data preparation enables AI and machine learning to be harnessed for enormous benefits, such as more-effective search, automation of routine tasks, and streamlined experimentation and synthetic process development. AI-assisted product development improves decision making, accelerates discoveries, and paves the way to better products.
 

Semantic Search Streamlines Data Retrieval

Accessing relevant historical data is crucial during R&D. Whether creating heat-resistant polymers or sustainable compounds, researchers save time and avoid duplication by leveraging past experiments.

AI-enabled semantic search in platforms like Signals Research Suite interprets queries based on intent rather than exact keywords. Unlike traditional keyword searches, semantic search delivers contextually relevant results, from both text and graphics. This approach streamlines data retrieval, enhancing efficiency and supporting AI-enabled chemistry innovation.
 

With AI-Enabled Automation, Researchers Focus on Innovation

By integrating generative AI and large-language modeling (LLM), platforms like Signals Research Suite can automate mundane, routine work.

During experimentation, AI assists with generating templates, predicting workflows, assigning tasks, and ensuring compliance with standards and regulations. When studies are complete, the Summarization Assistant generates reports quickly, while keeping company IP securely within the platform.

This automation unleashes scientists to focus their time and attention on the core of their R&D work: innovation.
 

Predictive Experimentation Accelerates Discoveries

The power of an AI-enhanced data management platform extends far beyond efficient search of past experiments and handling routine work. Predictive tools accelerate the planning and execution of experiments, shortening the path from data to insights.

In Signals Research Suite, AI and machine learning support hypothesis testing and predictive design of experiments (DOE), determining which tests will be most useful to reduce the number of experiments carried out.

These tools also reveal how variants affect results, identify outliers, and ensure accurate data analysis. During product formulation, AI predicts and optimizes chemical properties, enabling smarter ingredient selection.

Chemistry-informed machine learning (ML) can then facilitate the development of synthetic processes, by predicting chemical reactions and providing insights into how a reaction will change with different inputs and experimental conditions. It can recommend suitable catalysts, solvents, and other key reaction parameters.

As AI and machine learning capabilities streamline experimentation, they simultaneously reduce risks in new product development, increasing R&D success rates.
 

Confidence and IP Protection with AI

While AI offers transformative potential, companies must implement appropriate guardrails. A secure infrastructure, sufficient training, phased implementation, and good governance ensure responsible AI use.

A common concern about using AI in industry is maintaining data security and protecting intellectual property (IP). Unlike open AI technologies, platforms like Signals Research Suite protect sensitive data by creating a closed, secure ecosystem that safeguards proprietary R&D and commercial information.

In fact, an AI-enhanced platform like Revvity Signals can reduce IP risk—and the likelihood of project failure—through ready access to internal and external data in an enterprise-wide platform.

In the case of an intentional theft or an unintended data breach, Signals Research Suite can ensure a swift response by producing comprehensive audit trails.
 

Summary: Accelerating Innovation with AI

In the continuous quest for innovation, chemical companies generate vast quantities of data. The latest AI-enhanced data management software such as Signals Research Suite unifies and structures that data, enabling AI and machine learning tools. With the power of semantic search, automated report generation, and AI-enabled predictive chemistry, researchers access past data, save time, and make better-informed decisions. The result is accelerated new product development and greater R&D success.

Learn more at Chemicals & Materials Solutions | Revvity Signals Software