FINAL WORD

Industrial AI solutions contextualize key performance and sustainability data with Artificial Intelligence and human knowledge .

AI is increasingly playing a vital role in the world ’ s transition to greener operations . In fact , about 87 % of industrial leaders see AI as a useful tool in the fight against climate change , according to a recent BCG survey . Approximately 43 % plan to leverage the science in their own efforts against climate change .

AI technologies are already supporting companies on the path to achieving their netzero emissions goals . AI is already helping to decarbonize hard-to-reduce sectors . While generative AI has dominated the headlines recently , industries are also incorporating other types of AI technologies in different ways . They are using it to integrate new sources of renewable energy into production lines , drive greater productivity and efficiency , leverage new insights to make better decisions and build more agile and resilient operations .

What AI technologies are used in the industry today ?

Let ’ s take a look at just four main AI technologies that are used across the industrial spectrum today .

• AI-driven predictive analysis can help companies anticipate demand , optimize supply chains , forecast anomalies in assets and optimize inventory levels in real time . By using statistical algorithms and Machine Learning technologies , current and historical data can be analyzed to predict future events , including forecasting overall GHG emissions . As a result , costs and resource usage are reduced , which in turn decreases the environmental impact of overproduction and unnecessary resource consumption .

• The next step is predictive asset optimization . Here , dynamic simulation tools , combined with predictive analysis and advanced visualization , create a hybrid digital twin . Users gain a true 360-degree view of operational risks and can identify and solve problems earlier , as well as forecast the remaining useful life of assets to maximize uptime , availability and profitability . When incorporated into the design of future assets , these insights trigger a cycle of continuous improvement . In real terms , predictive asset optimization accurately forecasts performance degradation and greenhouse gas emissions in depth at a granular level .

• Generative AI is perhaps the most well-known way people encounter AI , both in their daily lives and in industrial applications . The technology has existed for over half a century , but it has now come into its own as massive large language models ( LLMs ) are available to the public . They enable operators to quickly make sense of large sets of knowledge , or serve as creative partners to support innovation , for example , simulating asset design options according to specific parameters or creating engaging technical learning material . When used in conjunction with real-time data leveraging specialized software , it can also provide deeper insight into data , including assistance in the complex analysis of sustainability issues .

• Gray-box modeling , one of the most advanced industrial AI technologies , has

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