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AI can serve as a partner when grounded in science and shaped by expert insight.
By Dan Wu, Ph.D.; Kevin Henderson, Ph.D.; Jonathan DeRocher, Ph.D.; Alicia Liew; Sam Lim, Ph.D.,Dow Inc.
AI is revolutionizing coatings by turning data into actionable insights. Success depends on modernized labs, curated data, and responsible adoption. From predictive modeling to Generative AI with retrieval-augmented generation (RAG), these tools accelerate development and promote confidence in digital solutions. AI can serve as a partner when grounded in science and shaped by expert insight, helping us drive sustainable innovation.
A single coating can contain dozens of interacting components, including binders, pigments, dispersants, and rheology modifiers, with properties shaped by composition and processing conditions from the formulating stages (i.e., grinding, mixing, and storage) and film preparation (i.e., substrate, humidity, and film build). Given the dependance of coating performance on a wide range of interdependent variables, empirical exploration of single factor contributions is slow and produces limited insight [1]. AI is reshaping coatings exploration to enable more efficient development and testing, but its success requires a solid data foundation guided by human expertise.
Modernizing the lab is thus essential to provide this grounding through a disciplined data infrastructure. Key considerations for this modernization are highlighted in Figure 1. The process begins with clear experiment planning and digitally connected instruments that capture data and metadata automatically. High throughput automation and standardized test methods ensure consistent, structured datasets. Centralized data storage, including electronic lab notebooks and materials/laboratory information management systems, consolidate information into well organized, interoperable data repositories that should be easy to access and navigate. Integrated data handling should automatically store information in their intended location while programmatic access through searchable interfaces facilitates data search and retrieval, analysis, documentation, and sharing. Meanwhile, a culture of strong data stewardship, including cleaning and governance, maintains data quality and ensures the relevance of these systems.
A well-designed and maintained data infrastructure provides contextually relevant and curated data to the end users who need this information. Digitizing these processes has been successful in reducing non-value-added work by up to 70% and shortening throughput time by up to 48% [2], ultimately facilitating the downstream development of AI tools for reliable, actionable insights
With a robust data foundation paving the way for AI enablement, the coatings industry has leveraged a wide range of mature and emerging AI capabilities, detailed in Figure 2. Machine learning remains a workhorse for tasks like forward and inverse modeling, property prediction, and multi‑objective optimization—all used to narrow design spaces and shorten R&D cycles. Today, these approaches remain essential and are increasingly complemented by new technologies. Computer vision has rapidly evolved with improvements in data quality and imaging capabilities. These advances have enabled applications in automated defect detection, area measurement, and color comparison, replacing subjective visual ratings with quantitative measures. Robotics and automation have streamlined sample preparation and repetitive testing, improving throughput and consistency and in some cases, more quantifiable and accurate testing. Natural language processing (NLP) has been used to extract insights from technical literature, patents, standards, SDSs, and customer feedback—supporting trend analysis, competitive intelligence, and faster method lookups.
Outside R&D, AI supports quality control, predictive maintenance, supply chain forecasting, pricing guidance, and customer experience improvements, reflecting broad adoption across functions.
More recently, advances in language-based AI and automation have set the stage for a new era of intelligent systems. Generative AI (GenAI) and Large Language Models (LLMs) have transformed the landscape by leveraging institutional knowledge from unstructured data. These approaches support summarization, contextual reasoning, and the quantification of subjective feedback in the context of a specific document or across a range of materials. When combined with domain expertise through in-context learning, LLMs can recognize coatings-specific terminology, producing outputs that are technically accurate and industry-specific. While chat-based interactions with popular language models may be most familiar to end users, the true value of GenAI to organizations does not lie in casual conversation but in structured, auditable access to internal, proprietary data systems, operating procedures, safety data sheets, patents, technical articles, and white papers. To mitigate the risk of “hallucination,” technical deployments increasingly use retrieval-augmented generation (RAG) to contextualize responses.
As illustrated in Figure 3, RAG grounds AI responses in a trusted and curated corpus, providing citations that formulators can validate before acting. This creates a “human-in-the-loop” safety net, ensuring reliability and accountability. By freeing experts from administrative data-gathering, GenAI accelerates decision-making and promotes consistency across global operations, ultimately driving value creation and innovation.
Recent advances in agentic and multimodal systems will further enhance the value of RAG-based models. Digitally savvy organizations that can bridge these emerging technologies with their proprietary knowledge will be well-positioned to harness the full potential of their data. Together, AI technologies represent a natural progression beyond numerical prediction toward an intelligent ecosystem one where AI collaborates with experts to provide actionable insights, enhance efficiency, and support informed decision-making across the coatings value chain.
AI is no longer confined to experimental pilots. It is now embedded across the entire coatings value chain. From internal tools that accelerate R&D and streamline manufacturing to customer-facing solutions that enhance engagement and drive sales, AI technologies spanning predictive modeling, computer vision, and GenAI are reshaping how industry innovates and delivers value. The following categories illustrate the breadth of these applications, demonstrating how AI impacts every stage of the journey.
1. Visual Interpretation for Quality and Appearance
AI is transforming subjective assessments into objective insights across the coating’s lifecycle. In R&D, computer vision systems, such as Dow’s integrated imaging solution, can quantify appearance and defects, enabling reproducible metrics across global sites [3]. Digital color matching platforms like BASF’s Refinity [4] and PPG’s VisualizID [5] accelerate shade selection and reduce reliance on physical decks, improving productivity and first-time-right accuracy. On the shop floor, inline quality control powered by vision AI detects defects such as blisters, peeling, algae, and shade variation with high precision, cutting inspection time and enhancing customer satisfaction. Large-scale deployments, including Asian Paints’ AI-driven QC systems, demonstrate how these capabilities scale from lab to production and ultimately improve customer experience [6].
2. Data-driven formulation decisions
Materials informatics is reshaping formulation strategies by combining domain expertise with sequential learning to prioritize high‑value experiments under constraints (e.g., TiO2 reduction, surfactant removal), using uncertainty‑aware models and domain descriptors like HLB and binder–pigment ratios [7, 8]. Industry reports show AI narrowing design spaces for sustainable ink formulations, tuning flow, gloss, and durability without hundreds of trials [9]. In parallel, DOW™ Paint Vision applies predictive intelligence to suggest optimal ingredient combinations and balance sustainability, cost, and performance—significantly reducing formulation time and helping teams move from data to decisions faster [10].
3. Connected Processes and Predictive Operations
AI connects R&D and production through digital twins that integrate shop-floor data with physics-informed models. These systems enable real-time optimization, predictive insights, and faster troubleshooting, always with human oversight [11]. Siemens’ digital twin deployments in coating lines demonstrate how virtual models anticipate process deviations and optimize curing conditions before issues occur [12]. Similarly, automation strategies highlighted by Advanced Technology Services from Coatings World show AI-driven predictive maintenance reducing downtime and improving throughput [13]. As adoption grows, success depends on robust data readiness and expert validation to ensure these connected systems deliver measurable efficiency gains.
4. Turning Unstructured Data into Actionable Insights with GenAI
LLMs when prompted with coatings specific taxonomies (e.g., coverage, flow, spatter, noise), can transform painter feedback from videos and documents into structured insights. This approach generates standardized rating tables and relative formulation comparisons, validated by subject matter experts. By converting unstructured narratives into quantitative data, the process accelerates decision making and ensures consistency across trials [14]. Furthermore, the usage of the GenAI models on historical formulation decisions optimizes the coatings formulation process through reducing trial-and-error and accelerating innovation [8].
AI is not a magic wand, but it is a powerful amplifier for coatings innovation when paired with disciplined data practices and human-centered adoption. Its impact is clearest when organizations combine strong data foundations with thoughtful execution, whether accelerating development through machine learning or unlocking institutional knowledge through Generative AI. These advances remind us that governance, standardization, and curated data are not optional; they are the bedrock of trustworthy insights and measurable outcomes. Leaders who treat AI as a collaborator, anchored in science, guided by responsible practices, and propelled by people will move beyond experimentation to scalable solutions that deliver efficiency, innovation, and competitive advantage for the decade ahead. CW
1. Facilitating Coatings Product Development with Artificial Intelligence
2. Digitalizing the Paints and Coatings Development Process | MDPI
3. From Pixels to Performance: Advancing Coatings with Dow’s Integrated Research Imaging Solution – Coatings World
4. BASF Coatings sets new standard in end-to-end digital color solutions for body shops with Refinity
5. PPG – PPG to launch VISUALIZID software for DELFLEET Evolution commercial vehicle coatings in U.S. and Canada
6. AI vision helps Asian Paints cut defects faster | NTT DATA
7. White-Paper-Materials-Informatics-for-Coatings-Formulations.pdf
8. Enhancing Paint Formula Innovation Using Generative AI and Historical Data Analytics
9. AI and Machine Learning In Coatings and Ink Formulation – Coatings World
10. DOW™ Paint Vision | Dow Inc.
11. Will AI Improve Coating Projects or Take Your Job? | CoatingsPro | Association for Materials Protection and Performance
12. Podcast Coating the Future with Digital Twins – Part 2 on Siemens Blog
13. The Role of Automation in Improving Coatings Manufacturing Efficiency – Coatings World
14. AI-Powered Coatings | PCI Magazine
About the authors
Dan Wu, Ph.D., is associate R&D/TS&D director at Dow Coating Materials. Her team focuses on digital formulation, high throughput research, computer vision, and responsible AI adoption to accelerate customer innovation.
Kevin Henderson, Ph.D., is a senior R&D/TS&D scientist at Dow Coating Materials. His work centers on R&D lab digitalization, data management and architecture, and strategy development and implementation.
Jonathan DeRocher, Ph.D., is a senior R&D/TS&D scientist at Dow Coating Materials. Jonathan specializes in coating science, data engineering, data management, and application development to support digital transformation and enable advanced formulation workflows.
Alicia Liew is a data scientist at Dow Coating Materials. Alicia focuses on applying machine learning, predictive modeling, and Generative AI (GenAI) to accelerate innovation and enhance digital formulation strategies.
Sam Lim, Ph.D., is a data scientist at Dow Coating Materials. Sam’s expertise includes computer vision, model development, and bringing physical AI solutions to R&D labs for more efficient experimentation workflows.
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