Sustainable design starts with AI<\/strong><\/h2>\n\n\n\nConsider this: in 2023, around 430 million tonnes of plastic were manufactured, and a staggering 65% of it quickly became waste, according to World Environment Day<\/a>. Products shouldn\u2019t be destined for landfills the moment they\u2019re made. They should be engineered with longevity and sustainability in mind from the beginning.<\/p>\n\n\n\nThat\u2019s where AI can play a role.<\/p>\n\n\n\n
By transforming the design process, AI makes it possible to design for environmental impacts early, right where it matters most. Since roughly 80% of a product\u2019s environmental footprint is determined during the design phase, the ability to simulate and test ideas before production is a game-changer.<\/p>\n\n\n\n
Pairing AI with the comprehensive Digital Twin<\/a> takes this a step further. It allows companies to virtually explore thousands of design variations, evaluate them against real-world data and optimize for performance and sustainability. This approach drastically reduces development timelines, helping businesses innovate quickly and efficiently at scale.<\/p>\n\n\n\n![\"Automation](\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/19\/2025\/05\/Pharma-White-Paper-Image-4_original-1024x576.jpg\")
Automation innovation uses AI to enhance their glass mold cleaning to achieve cost savings and sustainability results.<\/em><\/figcaption><\/figure><\/div>\n\n\nFor example, Automation Innovation,<\/a> a company focused on cleaning molds used in glass production, previously used a generic approach to their cleaning process, resulting in longer cleaning cycles, reliance on harmful chemicals and wasted energy. But by implementing Siemens automation solutions with AI-driven analytics, the company was able to tailor the cleaning process to each mold’s condition using a digital twin. The result? Cleaning times dropped from five hours to two seconds per mold, and harmful chemical usage was eliminated.<\/p>\n\n\n\nThe broader impact was even more impressive. Their results also include: <\/p>\n\n\n\n
\n- 700,000 tons of raw materials saved annually<\/li>\n\n\n\n
- One billion kilograms of CO\u2082 emissions avoided<\/li>\n\n\n\n
- 30% reduction in on-site energy consumption<\/li>\n<\/ul>\n\n\n\n
AI-powered generative design tools offer even more possibilities. These technologies enable engineers to explore a vast range of design options\u2014factoring in performance, cost, material efficiency, and carbon footprint. Previously, such extensive experimentation was cost-prohibitive and time-consuming. Now, with AI, rapid prototyping is not only possible\u2014it\u2019s efficient.<\/p>\n\n\n\n
This unlocks new opportunities for designing with circularity in mind, such as components that are easier to recycle or reuse. What used to take months can now be achieved in days, opening the door to faster, more sustainable product development.<\/p>\n\n\n\n
From smart design to smarter production<\/strong><\/h2>\n\n\n\nAI\u2019s value doesn\u2019t end at design. It\u2019s also reshaping how products are made. On the factory floor, AI is creating agile and responsive production systems capable of adjusting in real time. As industrial systems become more connected and autonomous, AI supports better collaboration between people and machines, enabling teams to detect inefficiencies, plan maintenance proactively and keep operations running smoothly.<\/p>\n\n\n\n
For example, AI-driven predictive maintenance<\/a> can determine the remaining lifespan of machinery, allowing for timely repairs or replacements. This not only minimizes downtime but also supports more customized and efficient production processes which contributes to broader sustainability goals.<\/p>\n\n\n\nAI is also a powerful tool for making supply chains more transparent. By analyzing environmental and social data from suppliers, businesses can make more informed choices about who they work with, aligning partnerships with their sustainability values.<\/p>\n\n\n
\n![\"Data](\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/19\/2025\/05\/sigreen_original-1024x566.png\")
Data sharing results in a collaborative environment where companies can showcase their commitment to sustainability by reducing carbon emissions.<\/em><\/figcaption><\/figure><\/div>\n\n\nA great case of this is the Together for Sustainability initiative<\/a> in the chemical sector. Participating companies share data in a neutral environment to align on common standards and metrics. This creates a unified framework where suppliers and buyers speak the same \u201cdata language,\u201d enabling full visibility into product footprints.<\/p>\n\n\n\nBy standardizing data formats and using compatible APIs, this collaboration allows for seamless communication across systems. Once the data is harmonized, AI can be applied to create digital twins of supply chains\u2014offering deep insights that help businesses make more ethical and sustainable decisions.<\/p>\n\n\n\n
New level unlocked: Using AI for sustainability <\/strong><\/h2>\n\n\n\nWhile AI might seem like a sustainability cheat code, it\u2019s not a magic solution. It takes effort, openness and a willingness to change. Organizations must invest in training, adopt a collaborative mindset and be ready to share data to truly unlock AI\u2019s potential.<\/p>\n\n\n\n
Still, as AI tools become more accessible and easier to integrate, the barriers are lowering. With the right strategy, mindset, and data infrastructure, companies can use AI not just to meet sustainability targets, but to lead the charge in transforming industries for the better.<\/p>\n\n\n\n
Want to dive deeper into AI and sustainability? Tune in to our podcast series<\/a> to explore how AI is shaping a more sustainable future.<\/p>\n\n\n\n
\n\n\n\nSiemens Digital Industries Software<\/strong> helps organizations of all sizes digitally transform using software, hardware and services from the Siemens Xcelerator<\/a> business platform. Siemens\u2019 software and the comprehensive digital twin enable companies to optimize their design, engineering and manufacturing processes to turn today\u2019s ideas into the sustainable products of the future. From chips to entire systems, from product to process, across all industries. Siemens Digital Industries Software<\/a> \u2013 Accelerating transformation.<\/p>\n","protected":false},"excerpt":{"rendered":"In various video games, players sometimes access hidden advantages through secret combinations known as \u201ccheat codes.\u201d These tricks can grant…<\/p>\n","protected":false},"author":95296,"featured_media":12044,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"spanish_translation":"","french_translation":"","german_translation":"","italian_translation":"","polish_translation":"","japanese_translation":"","chinese_translation":"","footnotes":""},"categories":[166,63],"tags":[13706,12,1920,11,2,196,13813,4,2466],"industry":[145,144],"product":[],"coauthors":[13711],"class_list":["post-12040","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-learning-resources","category-featured","tag-ai-2","tag-artificial-intelligence","tag-comprehensive-digital-twin","tag-digital-twin","tag-digitalization","tag-generative-design","tag-predictive-maintenance","tag-simulation","tag-sustainability","industry-industrial-machinery","industry-industrial-machinery-heavy-equipment"],"featured_image_url":"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/19\/2025\/05\/siemens-industrial-metaverse-Vrbrille_original.jpg","_links":{"self":[{"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/posts\/12040","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/users\/95296"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/comments?post=12040"}],"version-history":[{"count":3,"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/posts\/12040\/revisions"}],"predecessor-version":[{"id":12049,"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/posts\/12040\/revisions\/12049"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/media\/12044"}],"wp:attachment":[{"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/media?parent=12040"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/categories?post=12040"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/tags?post=12040"},{"taxonomy":"industry","embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/industry?post=12040"},{"taxonomy":"product","embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/product?post=12040"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/coauthors?post=12040"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
That\u2019s where AI can play a role.<\/p>\n\n\n\n
By transforming the design process, AI makes it possible to design for environmental impacts early, right where it matters most. Since roughly 80% of a product\u2019s environmental footprint is determined during the design phase, the ability to simulate and test ideas before production is a game-changer.<\/p>\n\n\n\n
Pairing AI with the comprehensive Digital Twin<\/a> takes this a step further. It allows companies to virtually explore thousands of design variations, evaluate them against real-world data and optimize for performance and sustainability. This approach drastically reduces development timelines, helping businesses innovate quickly and efficiently at scale.<\/p>\n\n\n For example, Automation Innovation,<\/a> a company focused on cleaning molds used in glass production, previously used a generic approach to their cleaning process, resulting in longer cleaning cycles, reliance on harmful chemicals and wasted energy. But by implementing Siemens automation solutions with AI-driven analytics, the company was able to tailor the cleaning process to each mold’s condition using a digital twin. The result? Cleaning times dropped from five hours to two seconds per mold, and harmful chemical usage was eliminated.<\/p>\n\n\n\n The broader impact was even more impressive. Their results also include: <\/p>\n\n\n\n AI-powered generative design tools offer even more possibilities. These technologies enable engineers to explore a vast range of design options\u2014factoring in performance, cost, material efficiency, and carbon footprint. Previously, such extensive experimentation was cost-prohibitive and time-consuming. Now, with AI, rapid prototyping is not only possible\u2014it\u2019s efficient.<\/p>\n\n\n\n This unlocks new opportunities for designing with circularity in mind, such as components that are easier to recycle or reuse. What used to take months can now be achieved in days, opening the door to faster, more sustainable product development.<\/p>\n\n\n\n AI\u2019s value doesn\u2019t end at design. It\u2019s also reshaping how products are made. On the factory floor, AI is creating agile and responsive production systems capable of adjusting in real time. As industrial systems become more connected and autonomous, AI supports better collaboration between people and machines, enabling teams to detect inefficiencies, plan maintenance proactively and keep operations running smoothly.<\/p>\n\n\n\n For example, AI-driven predictive maintenance<\/a> can determine the remaining lifespan of machinery, allowing for timely repairs or replacements. This not only minimizes downtime but also supports more customized and efficient production processes which contributes to broader sustainability goals.<\/p>\n\n\n\n AI is also a powerful tool for making supply chains more transparent. By analyzing environmental and social data from suppliers, businesses can make more informed choices about who they work with, aligning partnerships with their sustainability values.<\/p>\n\n\n A great case of this is the Together for Sustainability initiative<\/a> in the chemical sector. Participating companies share data in a neutral environment to align on common standards and metrics. This creates a unified framework where suppliers and buyers speak the same \u201cdata language,\u201d enabling full visibility into product footprints.<\/p>\n\n\n\n By standardizing data formats and using compatible APIs, this collaboration allows for seamless communication across systems. Once the data is harmonized, AI can be applied to create digital twins of supply chains\u2014offering deep insights that help businesses make more ethical and sustainable decisions.<\/p>\n\n\n\n While AI might seem like a sustainability cheat code, it\u2019s not a magic solution. It takes effort, openness and a willingness to change. Organizations must invest in training, adopt a collaborative mindset and be ready to share data to truly unlock AI\u2019s potential.<\/p>\n\n\n\n Still, as AI tools become more accessible and easier to integrate, the barriers are lowering. With the right strategy, mindset, and data infrastructure, companies can use AI not just to meet sustainability targets, but to lead the charge in transforming industries for the better.<\/p>\n\n\n\n Want to dive deeper into AI and sustainability? Tune in to our podcast series<\/a> to explore how AI is shaping a more sustainable future.<\/p>\n\n\n\n Siemens Digital Industries Software<\/strong> helps organizations of all sizes digitally transform using software, hardware and services from the Siemens Xcelerator<\/a> business platform. Siemens\u2019 software and the comprehensive digital twin enable companies to optimize their design, engineering and manufacturing processes to turn today\u2019s ideas into the sustainable products of the future. From chips to entire systems, from product to process, across all industries. Siemens Digital Industries Software<\/a> \u2013 Accelerating transformation.<\/p>\n","protected":false},"excerpt":{"rendered":" In various video games, players sometimes access hidden advantages through secret combinations known as \u201ccheat codes.\u201d These tricks can grant…<\/p>\n","protected":false},"author":95296,"featured_media":12044,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"spanish_translation":"","french_translation":"","german_translation":"","italian_translation":"","polish_translation":"","japanese_translation":"","chinese_translation":"","footnotes":""},"categories":[166,63],"tags":[13706,12,1920,11,2,196,13813,4,2466],"industry":[145,144],"product":[],"coauthors":[13711],"class_list":["post-12040","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-learning-resources","category-featured","tag-ai-2","tag-artificial-intelligence","tag-comprehensive-digital-twin","tag-digital-twin","tag-digitalization","tag-generative-design","tag-predictive-maintenance","tag-simulation","tag-sustainability","industry-industrial-machinery","industry-industrial-machinery-heavy-equipment"],"featured_image_url":"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/19\/2025\/05\/siemens-industrial-metaverse-Vrbrille_original.jpg","_links":{"self":[{"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/posts\/12040","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/users\/95296"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/comments?post=12040"}],"version-history":[{"count":3,"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/posts\/12040\/revisions"}],"predecessor-version":[{"id":12049,"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/posts\/12040\/revisions\/12049"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/media\/12044"}],"wp:attachment":[{"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/media?parent=12040"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/categories?post=12040"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/tags?post=12040"},{"taxonomy":"industry","embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/industry?post=12040"},{"taxonomy":"product","embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/product?post=12040"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/coauthors?post=12040"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}\n
From smart design to smarter production<\/strong><\/h2>\n\n\n\n
New level unlocked: Using AI for sustainability <\/strong><\/h2>\n\n\n\n
\n\n\n\n