Need to Know

• Machine learning can analyze formulation and experimental data, run simulations and narrow the most promising candidates before physical testing.
• When connected to process equipment, AI can support real-time quality monitoring and help identify application problems before more material is wasted.
• Poor training data, opaque model reasoning, hallucinations and intellectual-property concerns can undermine recommendations.
• Governance and a human-in-the-loop approach are essential; AI should extend chemists’ and operators’ expertise rather than replace it.

AI Catalyzes Coatings, Inks and Adhesives

AI is driving tremendous growth in various industries. Its market value will reach an estimated $1.68 trillion by 2031, achieving a 37% compound annual growth rate from 2026 to 2031. While its penetration rate in the paint and coatings industry is relatively low, its potential is undeniable. Early adopters are using it as a catalyst for expansion. 

A catalyst is defined as something that accelerates a chemical reaction without being consumed. AI does exactly this — it is an agent of change that initiates growth, driving innovation and challenging the status quo.  

Unlike other modern technologies, AI goes beyond incremental improvements in isolated areas. Rather than exclusively improving dataset organization or coating application, it can uplift administrative and operational workflows. 

This technology is versatile enough to enhance everything from chemical formulation to quality control, depending on its training and integrations — and decision-makers can implement it virtually anywhere, with the proper technology stack. 

A machine learning model can analyze massive datasets of chemical compounds or experimental results. It can output assessments, recommendations or predictions, supporting exponential growth and efficiency in coatings research and development. Advanced algorithms and AI-enabled hardware allow formulators to accelerate innovation. 

How AI Is Optimizing Coating Formulation

Achieving perfect, repeatable uniformity in a complex mixture is challenging. How materials combine is directly determined by speed and time — excessive or insufficient mixing can cause particles to stay clumped or sensitive additives to break down. Traditionally, operators must pay attention to visual cues like torque changes or temperature changes to prevent issues. 

While skilled professionals are the lifeblood of the industry, human error is an inevitable by-product of manual processes. If someone misses a visual cue, they risk having to start over, thereby wasting resources and delaying innovation. 

Rather than dealing with quality control issues and long research and development cycles, industry professionals can leverage AI to automate and optimize processes. This technology complements their existing expertise by bridging knowledge gaps and automating basic, repetitive workflows. 

This technology can run thousands of simulations simultaneously, accelerating in silico prediction. A generative model can identify novel chemical formulations that align with the latest regulatory, budget, branding and market needs. 

An algorithm can also strategically select ingredients for desirable qualities, such as ultraviolet blocking, anti-fouling, conductivity, corrosion resistance or self-stratification. For instance, since silver has well-documented, broad-spectrum antibacterial properties, it is ideal for antimicrobial coatings. The algorithm could identify it as a functional additive for select formulations. 

Is Machine Learning Faster or Cheaper?

While no solution is universally faster, cheaper or better than its counterpart, machine learning technology shows promise. AI ensures quality control with real-time, intelligent monitoring, controlling stability, surface and flow properties. For example, when spray-coating a surface with paint, it can identify pigment clogging to ensure uniform coverage and good color intensity. 

If the model is embedded into connected technologies, it can immediately correct mistakes in the spraying process and send the operator a message detailing the exact cause of the error. An AI-enabled process enables them to control 100% of the application in real time. This approach minimizes paint waste and lowers electricity consumption. 

In addition to reducing the need for quality checks, this technology can save resources. Traditionally, facilities use large-batch reactors with a 100-liter capacity to complete chemical discoveries. With infrared thermography, thermal imaging and supervised machine learning, these reactions can take place in microfluidic reactors containing just a few drops of liquid.

Reducing waste and energy consumption streamlines the typical trial-and-error process, saving money, materials and time. This is particularly beneficial during experimentation since testing each material combination can take months or even years. 

Generative models can use plain language to recommend the proper proportions. This approach drastically accelerates formulation optimization. With AI, professionals only need to conduct five experiments rather than 100. Since they can rapidly identify the most viable combinations, they can present the top five within hours. 

Considering Legitimate Concerns About AI

Although machine learning technology has demonstrated potential in material selection, formulation and quality control, success is not guaranteed. Early adopters should not rush into implementation. 

Data scarcity and quality are common concerns. To ensure model output is accurate, timely and relevant, professionals must thoroughly vet their training data sources. For instance, the model could recommend a material that does the opposite of what it claims. Alternatively, it may infringe on another company’s intellectual property.  

Ensuring training data is accurate is essential because interpretability becomes increasingly challenging as models become more advanced. This is known as the black box problem. If an algorithm’s reasoning is opaque, it becomes difficult to determine whether it is telling the truth or not. 

Since machine learning models aren’t conscious or sentient, they cannot intentionally seek to deceive users. However, they can still hallucinate or output biased information. Organizations can address these issues by developing a robust governance framework.

Implementation Advice for Industry Leaders

AI’s penetration rate could increase exponentially. Leaders predict it will eventually become the industry standard for research and development in the paint and coatings sector. With the correct data and algorithms, organizations can make informed decisions about resource expenditures and people hours. 

Large-scale data aggregation is necessary for AI to be truly effective. Instead of collecting whatever information is available, companies should seek high-quality, accurate and timely data points. After initial selection, they should routinely reevaluate the sources for relevancy and accuracy.

A governance framework is also essential. By strategically implementing policies, procedures and practices to govern AI, business leaders can keep output accurate and unbiased. A long-term plan is vital because model performance can degrade over time if real-world information strays too far from training data.

Once decision-makers have these frameworks in place, they should consider automation’s role in their operations. Embedding AI into collaborative robots with computer vision systems would enable them to achieve greater productivity gains. They should not replace workers entirely, though, since humans deeply understand engineering, chemistry, physics and problem-solving.

A human-in-the-loop approach integrates people’s expertise and feedback into AI-powered processes to improve accountability and accuracy. Professionals’ knowledge and skills are as important as a robust data infrastructure.

AI Makes Expedited Innovations a Reality

AI could revolutionize the formulation, production and application of coatings, inks and adhesives. Professionals must still do the groundwork, but it gives them an optimal starting point, streamlining day-to-day operations and lightening their workload. 

This technology also enhances data-driven decision-making, helping leaders uncover business opportunities and mitigate risks. With strategic implementation, they can lead the next generation of coatings innovations, thereby gaining a significant competitive advantage.

For coatings formulators and manufacturers, artificial intelligence in coatings is becoming increasingly relevant to formulation screening, quality control and process decisions, making data quality and human oversight part of the technology conversation.