People, planet, profits. By now, companies are more than familiar with the concept of the triple bottom line. This is because while making a profit is ideal, more and more laws are making taking better care of the environment and the people who live in it legally required. Compliance is difficult, so companies are turning to a technology that can make it dramatically easier: AI.
In this installment of our series on technology and analytics trends for 2024, industry experts at Lingaro’s supply chain analytics (SCA) practice share their insights on how AI can help leaders and decision-makers make their organization’s supply chains more sustainable.
Three major ways where AI can help sustainability efforts
Last year, the European Commission adopted the European Sustainability Reporting Standards (ESRS), which must be used by all firms covered by the Corporate Sustainability Reporting Directive (CSRD). The Global Reporting Initiative (GRI) and the International Sustainability Standards Board (ISSB) standards, the most widely adopted sets of sustainability reporting standards in the world, are being aligned with ESRS. This is being done so that companies would find it easier to comply with both EU and global standards and avoid having to needlessly submit sustainability reports twice. Still, there’s a lot that companies must deal with.
For example, the CSRD now requires companies under its purview to prepare reports on the 2024 fiscal year. Additionally, other government agencies are cracking down on greenwashing. With the regulatory landscape still changing, it can be hard to keep up, which is why companies are using AI to help them with all things related to sustainability.
“Overall, the use of AI and machine learning in sustainability is a growing trend that is expected to accelerate in 2024 and beyond,” shared Mateusz Panek, Lingaro’s supply chain and sustainability expert and enterprise and business solutions architect. “More and more organizations are expected to adopt them to improve their sustainability performance.”
Currently, there are three general areas wherein AI is being applied to, namely on sustainability data platforms, on sustainability reporting tools, and on sustainability decision support systems.
AI-powered sustainability data platforms
The AI in sustainability data platforms automate the collection, cleaning, and analysis of sustainability data from a variety of sources. In short, AI accelerates the conversion of a company’s sustainability data into reliable and relevant insights. Organizations will not only see where they’re hitting and missing their marks but will also discover ways on how to improve operational performance. Moreover, from a meta perspective, AI helps organizations save time and money and improves the accuracy of their sustainability data. In other words, AI promotes a virtuous circle of sustainability optimization.
AI-powered sustainability reporting tools
AI-powered sustainability reporting tools help organizations automatically generate sustainability reports that are more comprehensive, accurate, and timely than if they were created manually by humans.
Tracking and reporting environmental and societal effects are difficult because of two main factors. First, the geographical scope of the report is wide and can cross borders. Second, it involves long and complex value chains wherein value can come in the form of profit, ecological enhancement, and improvement in the quality of life of the people impacted by the organization.
Thankfully, companies can use AI to automatically generate sustainability reports that fulfill relevant requirements, such as regulatory compliance guidelines and internally defined frameworks.
To learn more about how AI can help your enterprise with compliant and comprehensive sustainability reporting, go to our website’s dedicated page on sustainability analytics.
AI-powered sustainability decision support systems
These systems help organizations make more informed decisions about their sustainability strategies by providing them with insights from their sustainability data.
Major companies already use AI for sustainability:
- Nokia offers AI energy management to help telecommunications and radio networks achieve up to 30% energy savings.
- DeepMind AI reduces how much energy Google utilizes to cool its data centers by up to 40%. Additionally, DeepMind’s AI-powered safety monitors weighs every action committed in the data center for uncertainty. If the AI monitors calculate their confidence that the action is a good action, then it is presented as an option to human data center operators, who’ll then consider fulfilling that action. Low-confidence actions, on the other hand, aren’t put up for consideration.
- Blue River Technology’s See & Spray precision weed control uses machine learning and computer vision to detect and spot-spray weeds. This saves on herbicide use by up to 77% compared with broadcast spraying. The savings allows farmers to justify the use of more complex herbicide mixes to target herbicide-resistant weeds and prevent their proliferation. This method is more sustainable than broadcast-spraying weeds, which doesn’t address the problem of induced herbicide resistance beyond increasing the toxicity of the herbicide.
Data strategy comes first
Indeed, AI will be a key feature in sustainability efforts moving forward, but its impact to the triple bottom line depends on the company’s data strategy. “From tracking data points like emissions throughout the value chain to building ML models for generating compliant reports and insights for improving sustainability efforts, companies need an effective end-to-end data strategy,” claimed Panek.
Lingaro’s Supply Chain Analytics practice, for example, provided a data strategy that helped a multinational FMCG company tangibly measure and track the carbon footprint of their freight operations while automating its business processes and workflows. Through a data-driven approach and AI-powered capabilities, businesses and stakeholders can do good business that is also good for the world.