AI tools supporting sustainable business
Artificial intelligence (AI) tools specifically designed for enterprises are quietly helping businesses meet their sustainability goals, alongside the popular ChatGPT. Classic AI is already being widely used in various areas, while generative AI is rapidly evolving to address new classes of use cases. AI is being utilized in asset management, inventory management, schedule optimization, anomaly detection, and compute optimization to drive sustainability efforts. Companies are also deploying generative AI applications to process sustainability information and streamline ESG reporting. A Responsible AI approach and AI Governance framework are necessary for the responsible use of AI.
AI tools designed for enterprises are quietly helping businesses achieve their sustainability goals. While ChatGPT may be grabbing headlines, classic AI techniques and emerging generative AI are playing vital roles in different areas to support sustainability efforts. The potentials of AI in areas such as energy efficiency, decarbonisation, waste reduction, and sustainability reporting are high.
For example, AI solutions are used in asset management to collect and analyse asset performance data, predicting asset health and risk of failure.
In inventory management, AI optimises stock levels, considering demand forecasting, last-mile delivery, and routing optimisation factors. Schedule optimisation ensures the appropriate alignment of talent in areas like asset maintenance.
AI’s anomaly detection capabilities are used in manufacturing to monitor each production stage, catching defects and discrepancies early on. This not only reduces waste but also saves energy that would be required for rework.
Another area where AI makes a significant impact is compute optimisation. By understanding compute demand over time, AI helps data centers optimise the use of computing and cooling resources, resulting in energy savings.
Looking ahead, generative AI applications can further support sustainability goals. Intelligent document understanding can streamline the production of environmental, social, and corporate governance (ESG) reports by retrieving and summarising relevant information from various business systems. AI can also combine ESG reports with purchase order information to estimate environmental impact and process ESG reports in bulk to create shortlists of sustainable companies.
Furthermore, generative AI models fine-tuned with domain-specific data are expected to play a significant role in intelligent text processing and geospatial data modeling for risk assessment and mitigation.
By reducing energy consumption, avoiding waste, and optimising resources, businesses can achieve financial benefits alongside environmental advantages. AI-powered sustainability applications enable companies to make decisions that align with their sustainability goals, promoting responsible and efficient resource management.
About author
DW Team
The GIP Digital Watch Observatory team, consisting of over 30 digital policy experts from around the world, excels in the fields of research and analysis on digital policy issues. The team is backed by the creative prowess of Creative Lab Diplo and the technical expertise of the Diplo tech team.
