The retail sector currently accounts for 25% of global emissions, more than almost any sector other than energy. With many young consumers prioritizing ethical spending, emissions reduction is not just important for the planet, but for business longevity.

Up to 98% of sector emissions currently occur outside of retailers’ direct control as part of ‘scope 3’ emissions. However, many retailers are still struggling to make serious headway cutting emissions across the supply chain.

Today, artificial intelligence (AI) and machine learning offer a route to better understand, control, and optimize those supply chains. But where to start? What are the challenges, and how can standards help to apply AI safely, transparently and effectively? And, ultimately, how can this help to reduce emissions?

The emissions challenge facing retail

Every year since 2021, we’ve surveyed UK business leaders about their understanding of, and efforts towards, net zero targets. In the latest edition of our Net Zero Barometer report, we found that 82% of retailers are already taking concerted action to reduce emissions.

Retail supply chains offer significant potential for emissions reduction. Yet, 39% of those we surveyed still haven’t made any progress on this front. 33% identify the difficulty of finding suppliers with net zero credentials as the main barrier to cutting emissions in their supply chain.

So how can AI help to reduce emissions, build trust, and increase transparency across the supply chain?

The role of AI in supply chain emissions reduction

AI is unique in its ability to collect, integrate and interpret data. It offers major potential for unpicking the notoriously complex, interconnected and global nature of modern retail supply chains.

Prior to the advent of AI, there was no way for retailers to cost-effectively harness the vast quantities of data they were producing. Today, AI technology can be unleashed on big datasets to find efficiencies and drive down emissions. Here are just some of the ways the technology can crunch the numbers for sustainability gains: 

1. Greater traceability and transparency

Given the fragmented nature of the modern supply chain, mapping the activity of suppliers can be immensely challenging. Yet it’s essential for measuring and driving down Scope 3 emissions.

Gathering and analyzing early-stage supply chain data (for example, the origin of cotton for a clothing manufacturer) has historically been almost impossible. Today, AI systems can help tie together and interrogate disparate data sources and identify practices or vendors with poor sustainability credentials. AI start-up, Altana, is currently working with US Customs and Border Protection and Maersk to map supply chains and uncover issues like forced labor.

2. Reduced energy usage

AI technology can also help retailers uncover ways to slash energy usage. In a 21 month AI trial, Tesco was able to eliminate 835 tonnes of CO2 emissions associated with refrigeration.

Data analyzed by AI allowed the Tesco maintenance team to find an improved balance between operational efficiency and energy consumption. Under the trial, refrigeration temperatures were increased by 1°C. While this might have seemed counter-intuitive, it resulted in major savings without impacting food waste.

3. Demand forecasting and reduced waste

The Intergovernmental Panel on Climate Change estimates that food waste contributes up to 10% of total man-made greenhouse gas (GHG) emissions. AI also offers the potential to substantially reduce this waste through improved demand forecasting.

AI makes it easier to combine and analyze huge data sets on everything from historical sales data, seasonal patterns and real-time consumer behavior. The insight gleamed from this can help can reduce the risk of overstocking and cut down on waste. 

AI can also make it simpler to share demand forecasts across the supply chain and build collaborative supply chain management. Retailers can provide suppliers with automated and continuously available real-time data on inventories and stock levels at different locations. AI can also help retailers monitor supplier performance and automatically prioritize greener options when restocking.

4. More efficient, greener logistics

Logistics, freight and warehousing currently accounts for around 7% of global greenhouse gas emissions. AI can enable retailers to better optimize logistics and transport operations and reduce emissions. Alongside combining vast historical data sets on things like traffic patterns, weather conditions and fuel efficiency, it is possible to process data and react in real-time.

Walmart claims to have already used AI-powered smart routing technology to eliminate 30 million unnecessary miles driven. AI is even being used to load trucks more efficiently, factoring in routing so products are packed in sequence, increasing efficiency and reducing emissions.

The role of standards

Standards can help organizations maintain and develop the comprehensive, trustworthy and secure data that AI technologies rely on. They can also help foster the collaboration across the supply chain that’s essential for improved data availability and transparency.

Here are just some of the standards that can help organizations build trustworthiness and facilitate the safe and secure adoption of AI technologies.

• PD ISO/IEC TR 24028:2020 Information Technology. Artificial Intelligence. Overview of trustworthiness in artificial intelligence: This is an international standard on artificial Intelligence (AI). It surveys topics related to trustworthiness in AI systems, including approaches to establish trust.

• BS EN ISO/IEC 42001:2026 Information technology. Artificial intelligence. Management system: This standard provides a certifiable AI management system framework within which AI products can be developed as part of an AI assurance ecosystem.

• PD ISO/IEC TR 24368:2022 Information technology. Artificial intelligence. Overview of ethical and societal concerns: This standard provides a high-level overview of the ethical and societal concerns in relation to AI, alongside related principles, processes, and methods. 

• PD ISO/IEC TR 27563:2023. Security and privacy in artificial intelligence use cases. Best practices: This standard outlines best practices on assessing security and privacy in artificial intelligence use cases.  

AI and existing standards

Whilst, in many cases, existing sustainability standards might not detail AI explicitly, the application of AI technologies can significantly enhance the ability to apply standards. This might include better monitoring, data analysis, support for decision-making, and otherwise enhance the ability to adhere to the guidelines and principles of the standard.

Some of the standards relevant to supply chains in this regard might include:

• BS EN ISO 14067:2018 Greenhouse gases — Carbon footprint of products — Requirements and guidelines for quantification

• BS EN ISO 26000 Guidance on social responsibility

• BS ISO 20400:2017 Sustainable procurement. Guidance

AI in tandem with standards

Improved supply chain management offers the most fertile ground for retail emissions reduction. But it is also one of the most complicated areas to get right. AI technology, used in tandem with standards, can help retailers make sense of the complexity and make genuine progress on emissions reduction.

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