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Innovation in the plastics and chemicals industry has often been sloth-like. This is partially due to longer innovation cycles compared with the production of other goods, extensive research requirements, regulatory hurdles, and late adoption of certain technologies. But the industry is undergoing a monumental change, and at the heart of it is AI.
Today, AI is revolutionizing everything from how plastics, polymers, surfactants, and other chemicals are designed, to how they are produced, recycled, and tracked through their entire lifecycle:
- AI + Materials = Magic: AI is helping create super-smart polymers that are stronger, greener, and more adaptable.
- Manufacturing Made Smarter: From detecting tiny defects to predicting when machines will break, AI is making production lines run like a well-oiled machine (without the oil).
- Supply Chain Sorcery: AI is forecasting demand, tracking materials, and making sure the supply chain runs smoother than a freshly waxed surfboard.
- Recycling Gets a Tech Upgrade: AI is helping sort and recycle plastics like a pro, turning yesterday’s trash into tomorrow’s treasure—minus the landfill.
- Smart Surfactants, Smarter Processes: AI is unlocking next-gen surfactants that optimize cleaning, emulsifying, and stabilizing in ways we couldn’t imagine—faster, greener, and more efficient than ever.
- Green Metrics, Real-Time: AI is crunching the numbers on your carbon footprint and keeping your sustainability goals in check.
- Small Biz Struggles: For SMEs, AI can feel like an expensive unicorn that’s hard to catch, but affordable tools and training are on the horizon. DeepSeek game changer?
- Rules, Robots, and Ethics: Companies will need to dodge regulatory hurdles and handle AI with care—because, spoiler alert, robots aren’t all-knowing (yet).
- Growth Galore: From eco-friendly packaging to high-performance materials, AI is opening new growth opportunities—and businesses that jump on board are poised to ride the wave.
AI is speeding up R&D, improving sustainability, and driving efficiency at every turn. In short, AI is changing the game in plastics and polymers—if you’re not paying attention, you might just miss the future (and your chance to benefit). Scratch that, you’ll be left behind and your competitors will drink your milkshake and likely take over your business. Yes, it’s that important.
But remember this critical fact: today’s AI is the worst AI you’ll use.
It only gets better, faster and cheaper from here.
Let’s jump in.
AI in Material Innovation
AI-driven research is beginning to change how chemicals and other materials are developed: think of it as the cool new lab partner that we never knew we needed.
The Wall Street Journal has reported on several ways in which generative AI is being used to help businesses develop products and materials faster. For example, Johnson & Johnson is using AI to “determine the best time to conduct a solvent switch, a process where one solvent is swapped for another to crystallize a molecule” so that new drugs can be created. The WSJ also notes that generative AI spending is increasing, and is expected to continue to increase in the coming years, as shown in Figure 1 below:
Figure 1: Wall Street Journal. Generating AI Spending.
In addition, according to McKinsey, generative AI in the chemicals industry can potentially provide significant added value for businesses. New applications and new molecule discovery have a lot of promise, as well as improvements to other research and commercial processes. Their predicted impacts can be seen in Figure 2 below:
Figure 2. McKinsey. Generative AI can help create value across all business functions in chemicals.
Along with creating new materials, AI helps to ensure their quality and sustainability. Scientific literature and databases can also be mined at speed by large language models (LLMs), a little bit like researchers who’ve had too much caffeine. This means critical design principles and useful information can be extracted faster than ever.
One important part of modern materials design is the shift from fossil-fuel-derived plastics to polymers made from biomass and other byproducts. AI is also used to accelerate material discovery, stimulate performance outcomes, and reduce the time-to-market for new surfactants and specialty chemicals. These are often used in personal care and cleaning products. Plant-based and more sustainable surfactants are also being developed with the help of AI. Some of these developments also improve the performance of products like shampoos, detergents, and cleaning agents, while reducing environmental impact. For example, P2 Science is using AI and machine learning models to develop a wide range of compounds for use in personal care products and fragrances, while trying to keep their products environmentally friendly.
Some of the materials being designed today are high-performance polymers, made with tools such as PolyID, a machine-learning neural network. Tools like PolyID can predict polymer performance and material properties based on information about the structure of the molecules in the design. It can go through millions of potential designs at a rapid pace and choose the best ones, speeding up the process of turning yesterday’s trash into tomorrow’s treasure. This accelerated design process also allows for the customization of polymers to meet specific client needs, such as heat resistance, flexibility, or biodegradability. Such capabilities are critical in industries ranging from aerospace to medical devices, where material performance is paramount. It’s analogous to precision medicine and precision agriculture.
The image in Figure 3 below shows how machine learning-based design can increase the output of high-performance materials while increasing their sustainability.
Figure 3: US Department of Energy. Accelerating Materials Discovery.
Biodegradable and recyclable alternatives to conventional plastics are also being developed with the help of AI. For example, a research organization in the Netherlands has developed an AI algorithm for developing biodegradable polymers, and a team at the University of Texas has used AI to develop a new enzyme that eats conventional plastics.
AI also accelerates R&D for material innovation by crunching data, running simulations, and predicting outcomes at a speed and scale far beyond human capability. For instance, McKinsey found that AI can provide a “more than 30 percent increase” in impacts from reviewing the literature when compared to humans alone. Figure 4 below shows how this is done:
Figure 4. McKinsey. Generative AI can rapidly synthesize and score evidence across a broad scope of scientific data sources.
AI algorithms can also design new polymers with the perfect balance of strength, flexibility, or biodegradability before you even touch a test tube. These algorithms can predict how molecules will interact, optimize 3D printing approaches, and can also make materials more sustainable just by focusing on the polymers. AI-powered robots are also able to automate repetitive testing, which frees researchers up for other tasks. A recent paper by Aidan Toner-Rodgers at MIT found that material scientists using AI “discover 44% more materials, resulting in a 39% increase in patent filings and a 17% rise in downstream product innovation.” These advances are also highly likely to improve as models become more effective. One significant implication of AI’s ability to optimize materials design is the potential for hyper-customization. Industries can now request tailored materials suited to specific environments, such as high-temperature-resistant polymers for aerospace applications or biodegradable plastics for single-use packaging. This capability is revolutionizing how manufacturers meet customer demands, driving innovation across industries.
One example of a startup in this space is Protein Evolution, which uses AI-designed enzymes that turn pre-processed plastic into reusable raw materials. These materials can then be used by plastic manufacturers to make recycled and recyclable plastic. Researchers at Georgia Tech are also using AI to accelerate materials discovery. Their models can predict polymer properties and formulations before they are created, meaning that only the best candidates are selected for actual production.
Finally, AI is also being increasingly combined with quantum computing, which enables increasingly accurate simulations of molecular interactions. Quantum computers process the complexity of polymer systems quickly, while AI analyzes the results to identify patterns and optimize designs. This combination accelerates the development of advanced materials, such as stronger composites or biodegradable plastics, reducing both costs and time-to-market for innovative solutions.
AI in Manufacturing Processes
Alongside material innovation, AI is also helping to improve and develop the manufacturing processes themselves. For example, AI-powered vision systems can now perform real-time defect detection in polymer production. Some of these systems are so advanced that they can spot flaws invisible to the human eye. One example of this is the tools produced by Maddox AI, which uses AI tools to detect irregularities, surface smoothness, deformations, and component accuracy of plastic items. Tools like this ensure consistent product quality while reducing waste and can free up employees to take on other tasks. Businesses can leverage these tools to lower rejection rates and make production more efficient.
In surfactant production, AI-driven analytics are revolutionizing processes by optimizing formulations, minimizing waste, and enhancing the efficiency of mixing and processing. By accurately predicting optimal production parameters, AI significantly reduces energy consumption while improving the consistency and quality of surfactants.
Additionally, AI enables the development of “digital twins” for manufacturing plants, which serve as real-time virtual simulations of production processes. These digital replicas allow manufacturers to test changes in workflows without interrupting actual operations. For example, machine settings or process parameters can be adjusted and simulated within the digital twin environment to assess their impact on efficiency, costs, and waste reduction before implementation.
AI’s influence also extends to the advanced monitoring of production tools. By analyzing operational data, AI systems can identify inefficiencies, recommend precise adjustments, and optimize processes for better sustainability and energy usage. Furthermore, AI-powered tools can determine the most effective factory layouts, improving material flows and maximizing resource utilization.
AI models for predictive maintenance can also be used to improve equipment management, as shown in Figure 5 below. By using large amounts of data (e.g. sensor data), AI can keep track of equipment wear and tear, as well as expected lifespans. This allows AI models to forecast failures before they happen or warn that proactive maintenance needs to take place. This reduces unplanned downtime and failures, lowers repair costs, and extends the lifespan of machinery.
Figure 5. McKinsey. Generative AI can rapidly process reliability data to identify root causes, flag bad actors, and evolve preventative-maintenance programs.
Finally, AI is being increasingly used for automation in polymer production, with robotics, smart systems, and data-driven tools. From raw material handling to final product packaging, AI ensures synchronized operations that boost productivity and reduce human error. The Engineering Institute of Technology notes that the industrial automation market is expected to grow 9.3% year on year, reaching a market value of USD 307.7 billion by 2030.
AI in Supply Chain and Lifecycle Management
While improving the design and manufacturing of plastics and polymers, AI is also changing the supply chain and lifecycle management of these products. Machine learning models can analyze historical sales data, market trends, and other data to predict inventory needs with high accuracy. This reduces the likelihood of overstocking or shortages and prevents waste. AI-powered systems can also increase supply chain visibility, allowing companies to track materials in real-time. You can see a sample of potential use cases in Figure 6 below. Improvements to recycling and waste management are also being made with the use of AI, through automating sorting, processing, and material reuse.
AI and blockchain technologies are enabling precise lifecycle tracking of materials, from raw production to end-of-life disposal. AI collects and analyzes data across the supply chain, while blockchain ensures this data remains tamper-proof and transparent. Companies like Circularise have already developed this technology and are using it with large manufacturers, such as collaborating with Porsche to track plastics through the automotive production process.
Figure 6: World Economic Forum. Traceability use cases.
The Wall Street Journal has covered how AI-powered tools are increasing sustainability by providing real-time analysis of carbon emissions and environmental performance in numerous sectors. The plastics industry is no exception, and companies can use tools like those mentioned above to help them track the emissions from any given supply chain. One tool being used, for example, for the manufacturing of fabrics and fashion is Carbonfact. Similar tools for digital tagging and tracing can also be used in the plastics industry to share data and determine just how much of a carbon footprint is produced from the entire supply chain of a product, not just its initial manufacturing.
Challenges and Limitations
While AI tools have several benefits for the industry, there are also some challenges that stand in the way of their wider uptake. One of the major challenges with all AI tools is data availability and standardization. Data often comes from diverse sources—such as sensors, suppliers, and recycling facilities—each using different formats and levels of detail. Inconsistencies and gaps in this data can reduce the accuracy of AI models. Standardizing data collection processes and improving data-sharing processes will need to take place for AI to reach its full potential use in the sector. This issue is highlighted in Figure 7 below.
Figure 7: Ellen MacArthur Foundation. Data collection & engineering.
In addition, AI tools can be expensive for small and medium-sized businesses. Some tools and automated systems have high upfront costs and require technical expertise to operate. To improve access and greater use of these systems, affordable AI tools will need to be developed, as well as training programs or potentially government incentives or support, to enable smaller businesses to jump on this bandwagon. However, the recent release of DeepSeek has upended the current cost structure of AI and caused a sell-off in tech giants like NVidia. Its model is offered at a fraction of the cost of current US models and may open a move level playing field for SMEs. Due to its Chinese origin, the model appears to be limited by what Chinese authorities allow on subjects like Tiananmen Square or current Chinese leadership/policies.
Like other industries, regulatory hurdles are preventing AI from being adopted as widely as possible in the plastics and polymers industry. This includes environmental regulations, data privacy laws, and ethical considerations. Eco-friendly surfactants must still meet stringent industry standards, and navigating complex regulatory hurdles adds a layer of complexity to AI-driven formulations. Many of these are for the protection of consumers and make good sense, but clear regulatory guidelines about the use of AI and the testing of AI-developed products can help companies innovate faster. Equally important are the ethical considerations surrounding AI adoption. Issues such as algorithmic bias, the safeguarding of sensitive data, and the environmental footprint of AI’s computing infrastructure require proactive attention. Addressing these challenges with transparency and clear strategies will not only promote responsible and equitable AI deployment but will also build trust among stakeholders, ensuring that the industry evolves in a sustainable and ethical manner. In addition, workforce displacement could also be a challenge for the industry to manage, particularly for roles that carry out repetitive manual processes. Automation is highly likely to replace such types of jobs, and employees in the industry will need to be retrained into new roles or to use such tools to support them.
Impact on Sustainability and Circular Economy
The plastics, surfactants, and chemicals industry are being increasingly encouraged to adopt sustainable and environmentally friendly practices. Eco-friendly products are becoming popular with customers and business partners, and AI can assist in the development process.
One of the major ways in which the plastics industry can benefit from AI for sustainability is through plastic recycling. As mentioned above, AI tools can significantly improve the efficiency and precision of sorting and processing systems. For example, AI can be combined with infrared light detection systems to detect plastic types and sort plastics more efficiently. As AI-powered systems evolve, they will be able to handle increasingly complex recycling challenges.
As shown in Figure 8 below, AI will also be used in areas such as robotics, neural networks, IoT, cloud computing, and data analytics, all of which can improve the sustainability of the industry and lead to more environmentally friendly development approaches and products.
Figure 8: European Environment Agency. Use of digital technologies in waste management.
For instance, AI tools improve production processes in ways that improve efficiency, which reduces emissions and conserves resources. AI tools can also analyze large data sets to identify inefficiencies in manufacturing, reducing waste or unnecessary steps in the process. Future advancements will likely include predictive modeling to design low-impact materials and processes.
AI is also driving the shift toward a circular economy by enabling closed-loop systems that keep materials in use longer. Predictive analytics help design products for easier disassembly and recycling. As illustrated in the image above, data on material usage and waste patterns within the industry can be collected and analyzed to develop predictive models to reduce the waste. Some of these models will be able to match recycled materials with demand in real-time.
AI is also driving sustainability in surfactant production by optimizing formulations to use fewer resources, reducing toxic byproducts, and improving biodegradability. Additionally, AI-powered systems help track surfactant products in the supply chain, ensuring that they are sustainably sourced and disposed of.
In the future, AI could facilitate fully automated recycling plants, dramatically increasing scalability and efficiency. For example, the London-headquartered chemical recycling company Plastic Energy is working with Siemens to recycle chemicals using automated technologies. Plastic Energy has developed a process that converts plastic into a “feedstock” called Tacoil, which is then used by companies such as Unilever, Tupperware, and L’OCCITANE en Provence to produce packaging. Other automated plastic recycling technologies have been developed by Schneider Electric and GR3N, which are said to “reduce human error by 40% and engineering costs by 30%.”
AI is also enabling innovative business models such as “materials as a service.” Ernst & Young notes that “many chemical producers are piloting new models for chemical ”leasing,” advanced recycling technologies for plastic-to-plastic repurposing, and conversion of solid wastes to hydrogen”. The United Nations Industrial Development Organization even gives out a Global Chemical Leasing Award, which is given to chemical leasing companies that adopt innovative and sustainable management principles. Isn’t that a prize you’d like to win? The diagram in Figure 9 below shows how the chemical leasing process works:
Figure 9. UNIDO. Chemical Leasing.
Other companies offer services like “circular take back” in which they pick up various material types and deal with recycling and reuse. Companies like Mocom are also offering recycling-as-a-service, through which polymers and other chemical compounds can be recycled. AI can track material lifecycles, forecast demand, and optimize reverse logistics, ensuring materials are returned for recycling or repurposing.
Companies that adopt such approaches are “well-positioned to capitalize on the growing interest and investment of governments, regulators and even consumers,” according to Ernst & Young, and can create new revenue streams that are sustainable in the long term.
Future Growth Opportunities
Many future growth opportunities are also available in this industry with the increasing adoption of AI. For example, AI is being used to develop smart packaging, by optimizing material usage, reducing packaging weight, and enhancing durability, particularly for reusable packaging systems. Packaging will also be increasingly embedded with sensors and data systems for real-time tracking, freshness monitoring, and customer interaction. Packaging Gateway notes that the “combination of smart packaging and IoT is still in its early stages, but its potential is enormous.”
One transformative application of smart packaging lies in food safety. By embedding AI-powered sensors into packaging, companies can monitor factors like temperature, humidity, and bacterial growth. These sensors can alert both retailers and consumers when a product is nearing spoilage, reducing food waste and improving supply chain efficiency. Moreover, AI-enabled packaging can offer personalized marketing by adapting labels or digital displays based on consumer preferences or local market data.
Polaris Research reports that the investment in AI tools in the chemicals market is growing steadily and is expected to continue to grow, particularly in North America and Europe. As shown in Figure 10 below, the compound annual growth rate (CAGR) is expected to be around 40% from 2024-2032.
Figure 10. Polaris Market Research. AI in the Chemicals Market.
Polaris notes that “more companies are integrating these technologies to optimize manufacturing processes, resulting in greater demand for reliable and efficient production methods.” In addition, AI tools will likely continue to enhance product development, improve demand forecasting (resulting in better decision-making), and refine quality testing and product safety.
Some companies such as Nouryon, which produces surfactants, are also increasingly expanding on niche and specialized products. The ability of AI to develop these and tailor them to markets is one way in which the industry is likely to grow and change, with AI support. Other companies like Unilever are using AI to optimize brands and surfactants, “using a data-driven tool that recommends which Stock Keeping Unit (SKUs) to delist, watch, protect or grow, based on the benefit to customers, consumers and Unilever.” Potion AI is also offering AI-driven formulation tools, based on ingredient and formula databases, so that novel surfactant molecules can be targeted for specific applications.
The convergence of AI and IoT is transforming polymer production, with smart factories using interconnected sensors and AI tools. For instance, ALPLA, a producer of packaging for companies like Coca-Cola and Unilever, is using “visual inspection systems” in most of their plants. They note that “data from the sensors was used to automate the machines in real-time, telling them when to eject defective products and informing nearby operators on the factory floor about equipment performance, which they could then tune and optimize.” These tools also allow predictive maintenance and supply chain optimization.
Finally, AI is revolutionizing the development of high-performance polymers for fields such as aerospace development. The German Aerospace Centre reports that AI is being used for material development to improve the service life of aircraft, reducing aircraft weight, and reducing fuel consumption. American startup Solugen is also using AI to optimize enzymes for developing more environmentally friendly chemicals and materials to be used in the oil and gas industry, the aerospace industry, personal care products, and renewable fuels.
These tools are also being used for healthcare and medical systems, such as biocompatible polymers for implants and drug delivery systems. A paper in the Journal of Biomaterials Applications notes that these approaches are “expected to become an extensive driving force to meet the huge demand for customized designs.” As these markets grow, businesses that harness AI for specialized solutions can achieve significant competitive advantages and long-term profitability. Moreover, advancements in AI-driven biomaterials are paving the way for regenerative medicine. By analyzing patient-specific data, AI can aid in designing polymers that mimic natural tissues, offering groundbreaking solutions for organ repair, prosthetics, and targeted drug delivery. This level of customization not only improves patient outcomes but also redefines the role of polymers in modern healthcare.
Discussion
AI adoption is poised to reshape innovation in the plastics and polymers industry. Companies that invest early in AI-driven technologies—whether for material innovation, process optimization, or smart packaging—will gain a significant competitive edge. Studies indicate that businesses using AI for R&D can reduce product development times by up to 30%, while also improving product performance and cost efficiency. This technological leap is likely to accelerate innovation cycles, creating new market leaders and disrupting established players. For instance, companies that integrate AI into their R&D pipelines can conduct virtual simulations of material performance, reducing the dependency on costly and time-consuming physical trials. AI tools can also analyze historical data and predict how polymers will perform under various conditions, enabling businesses to create customized solutions for niche applications such as aerospace, renewable energy, or advanced medical devices. This rapid iteration capability could significantly shorten the time required to bring new products to market, fostering an environment of constant innovation.
While AI offers significant opportunities and greater sustainability, such as lowering carbon emissions and increasing recycling rates, the development of high-performance polymers for new applications—like medical devices or electronics—may increase the demand for non-renewable resources. Nonetheless, AI use can help businesses reduce their carbon emissions by up to 10% and cut energy costs by 10-20%. Circular economy approaches are also likely to become more widespread, with companies increasingly using AI to design products that can be easily recycled or repurposed. AI is also paving the way for unprecedented advancements in lifecycle analysis (LCA). By integrating AI-driven tools, companies can monitor and optimize the environmental impact of their products throughout the entire lifecycle, from raw material sourcing to end-of-life disposal. These tools enable real-time data collection and predictive modeling, helping businesses make informed decisions about energy usage, material selection, and waste management. For example, AI can identify which production stages generate the highest emissions, allowing for targeted interventions to minimize environmental impact.
As AI continues to transform the plastics and polymers industry, there are also long-term considerations that must be addressed. Companies will need to balance the drive for economic growth with workforce displacement, data privacy and AI decision-making transparency, and the ethical and regulated testing of AI-developed chemicals and products.
Final Thoughts
In conclusion, AI is fundamentally transforming the plastics and polymers industry, offering numerous opportunities for innovation, sustainability, and efficiency. The future of material development looks increasingly driven by AI, as businesses explore high-performance polymers for emerging fields, products, and markets. The convergence of AI with other technologies, such as IoT and quantum computing, also promises to accelerate advancements, allowing companies to develop smarter, more sustainable solutions that meet growing consumer demand for eco-friendly products. For example, quantum computing could unlock new possibilities in polymer research by solving highly complex molecular equations, enabling the discovery of materials with unprecedented properties. When combined with AI’s ability to analyze and process vast datasets, this technological synergy could revolutionize how polymers are designed and tested.
However, these advancements come with challenges and considerations that businesses must navigate to succeed in the evolving landscape. Issues like data standardization, the high costs of AI adoption for smaller players, and the need for clear regulation will shape the industry’s next moves. Standardizing data collection protocols across the industry (and company) is critical to unlocking the full potential of AI. Organizations such as trade associations or international regulatory bodies could play a pivotal role in developing universal standards that ensure compatibility and transparency across different AI systems. Additionally, governments and private investors must work together to subsidize AI adoption for small and medium-sized enterprises (SMEs), leveling the playing field and fostering innovation across the entire value chain.
For businesses, the key to success will lie in using AI not just as a tool for efficiency, but as a cornerstone for creating a more sustainable and competitive future in the plastics and chemicals sector.
