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In the European Union, large companies and most publicly traded firms will be required to publish updates on the environmental and social risks they face, with those reports due beginning in 2025.\u00a0<\/p>\n
Across the pond, the Securities and Exchange Commission earlier this month announced new rules that would require corporations to divulge to investors their greenhouse-gas emissions<\/a>. \u201cIt\u2019ll make it real for a lot of chief financial officers,\u201d says John Mennel, managing director at Deloitte<\/a>.<\/p>\n With disclosure requirements emerging in nations ranging from India to China, the demand for tools that help firms track environmental, social, and governance factors are expected to rise. Many of those tools are going to lean on artificial intelligence to help multinational companies remain compliant\u2014but also transform their businesses along the way.<\/p>\n \u201cWhat you see is about 40% of the world GDP now requiring not just climate disclosure, but full sustainability\u2014the E, the S, and the G based on what\u2019s called this sort of double materiality task, which is not just effects on the company, but the company\u2019s effects on the world,\u201d says Tim Mohin, partner and director at Boston Consulting Group<\/a>.<\/p>\n \u201cI think it\u2019s really great for businesses,\u201d says Meera Clark, an early stage investor at Redpoint Ventures. \u201cNow that they have the visibility, they have this benchmark that they\u2019re building towards as it relates to really setting up the reporting infrastructure that they will hopefully be able to rely on for the next 5, 10, or 20 years.\u201d<\/p>\n AI is already helping companies track their ESG objectives in a wide variety of ways, with some of the most popular use cases ranging from machine learning to improve the accuracy of ESG metrics and address disclosure gaps, to using AI-powered satellite technologies to assess environmental risks, and predictive modeling to calculate greenhouse gas emissions.<\/p>\n \u201cThere\u2019s just a whole range of areas where AI will play a meaningful role,\u201d says Matt Slovik, head of global sustainable finance at Morgan Stanley<\/a>.<\/p>\n But, Slovik adds, companies should proceed with some caution. \u201cIs this a problem that AI can help solve?,\u201d Slovik asks. \u201cAnd if so, what does that solution look like and what does it mean within the context of your organization, your cost structure, and your other goals to ultimately get to the right decision.\u201d<\/p>\n At Redpoint Ventures, Clark says the firm spends a majority of their focus on software and data infrastructure companies, looking for mass market opportunities and a key reason why a startup didn\u2019t exist previously but should today.<\/p>\n \u201cThe regulatory environment continues to evolve,\u201d says Clark, which in her view justifies Redpoint Ventures\u2019 led investment<\/a> in a $13 million Series A for AI sustainability platform Greenplaces. \u201cThere\u2019s a very clear need for businesses to be able to more effectively report on this data.\u201d<\/p>\n One question that\u2019s emerging amid the rise of demand for AI and generative AI tools is the energy use<\/a> needs for such computation. \u201cData centers continue to consume an outsize portion of energy and unless that energy is sort of sourced renewable or there is some way to mitigate its actual consumption, we\u2019re going to have a bigger and negative side to the story,\u201d warns Mohin.<\/p>\n Susannah Shattuck, head of product at AI governance software provider Credo AI, says that if an organization has set a target to achieve carbon zero by 2050, they need to make risk-informed decisions and be \u201caware of the fact that these large language models can have a tremendous carbon footprint and therefore be making decisions about deploying them really in the use cases that have the potential to have the greatest impact on my business.\u201d<\/p>\n Large language models that can contain hallucinations, bias, or subject an organization to adversarial attacks can cause a model to lead them in the wrong direction, resulting in new governance risks when companies rely on these tools.\u00a0<\/p>\n \u201cAn organization that wants to make use of that technology safely, needs to ensure that they\u2019re the proper guardrails in place to protect against those possible risks and negative outcomes,\u201d says Shattuck.<\/p>\n At Deloitte, Mennel says AI tools for ESG not only helps companies remain compliant with new standards, it actually can transform them. An agricultural company can use AI to track the environmental footprint of a new, lower carbon alternative source of protein, as an example, and market those claims to consumers. \u201cWith the data that I can produce, where are there the opportunities to create fundamentally new products or new businesses that generate value,\u201d asks Mennel.<\/p>\n Canada-based Geotab has used AI for more than a decade to help Fortune 500 firms and the public sector manage their fleet, offering data intelligence to make more informed decisions about the efficiency, safety, and sustainability of the vehicles they use. \u201cSometimes there\u2019s a strong overlap between some of the sustainability decisions and efficiencies,\u201d says Neil Cawse, CEO of Geotab.\u00a0<\/p>\n The most sustainable solution is a simple one: reduce vehicle idling. From there, Cawse says the public and private sectors can ascertain the range of their routes, and make sure the vehicle type matches the requirements of the route. But one common mistake is that some companies switch too aggressively to electric vehicles, a costly error if some vehicles in the fleet are sidelined because they cannot complete a journey.<\/p>\n \u201cGood decisions are driven by good data,\u201d adds Cawse. \u201cLet the AI figure out what it is that you need to focus on first.\u201d<\/p>\n