They say necessity is the mother of invention, and this couldn’t be truer for the ESG industry. ESG policies and the industry as a whole were created with the best of intentions, aiming to give organizations a set of measurable frameworks and progressive targets in which the transition to more sustainable business models, reduced carbon emissions and ethical governance could be achieved in a realistic and timely manner.
However, driving meaningful change with ESG policies has proven to be much more challenging than originally expected. In particular, one of its most significant challenges is data management.
This is crucial for audit readiness, sharing data with stakeholders and understanding where carbon emissions can be reduced.
Yet the need to ensure the ESG delivers on its goals has never been greater. The continued rise of global temperatures shows no signs of slowing. April 2024 was the hottest on record, with countries from Portugal to India sweltering under these new temperature records.
The dire situation is helping to usher in a new wave of innovation for the ESG industry, with entrepreneurs leveraging advances in AI to tackle the scale of data management, reduce the resource drain associated with ESG management and deliver results that can be validated for quality-assured stakeholder reporting.
Here are 3 things for entrepreneurs to keep in mind when it comes to AI and ESG data.
The challenge with ESG data validation
ESG presents a formidable challenge for organizations due to its complexity and time-intensive nature. Over time, this can become a sizable drain on resources which can slow the pace of delivering results in turn.
Teams in this industry can spend a disproportionate amount of time on data validation, diverting attention from strategic initiatives and goal achievement. These manual processes are prone to human error and can be slower than automated alternatives. Scalability is also an issue, and as ESG reporting requirements expand, manual validation becomes increasingly unsustainable.
Our team has worked with some of the largest companies globally to get to the root of data management issues. We found that teams invest a lot of time and effort on creating a Standard Operating Procedure (SOP) for ESG data and evidence collection and validation. They painstakingly ensure the SOPs for data collection and validation will help them meet ESG compliances and meet audit requirements.
However, this same work can make it difficult for ESG teams to stay assurance-ready and focus on achieving their ESG goals, as they are often bogged down by the tedious task of manually validating data and documents.
By leveraging advanced technologies like GenAI, businesses can prioritize ESG performance more efficiently and accurately, paving the way for a more sustainable and responsible future.
Leveraging AI and automation to improve ESG assurance and auditing
AI has huge potential to make SOPs workable for global teams. Automating this validation process can save a significant amount of time, enabling enterprises to focus on strategic initiatives rather than manual data checks. This is especially true for companies with many facilities.
Maintaining assurance readiness throughout the year is crucial for companies to demonstrate their commitment to ESG and build trust with stakeholders. GenAI-based SOP engines can also enable continuous assurance readiness by automating the validation of ESG data and evidence against the organization’s SOPs.
This means that at any point in time, the ESG data is accurate, complete, and backed by valid documentation. The system proactively identifies issues and inconsistencies, allowing ESG teams to address them promptly and ensuring the company is always prepared for audits or requests from investors, regulators, or other stakeholders.
By leveraging GenAI to streamline the validation process, companies can confidently report on their ESG performance and progress towards sustainability goals without the last-minute scramble to get everything in order.
Integrating a GenAI-based SOP validation engine into the ESG data validation process can significantly reduce the time and effort required to support audits. The central repository for storing audit evidence eliminates the back-and-forth communication through email threads, making it easier for ESG teams to comply with an auditor’s request for necessary documentation.
Further, by identifying commonalities between compliance frameworks and standards, the GenAI-based SOP engine can help consolidate audit efforts, reducing duplication and increasing efficiency. This automation conserves time and minimizes human error, leading to more credible audit results and freeing up valuable resources for ESG teams to focus on achieving their sustainability goals.
AI innovations helping to measure global supply chain performance
Outside of the above, improving on ESG targets can help to support how a company interacts with global supply chains. Accenture estimates that supply chains generate 60% of all carbon emissions globally.
A number of innovative startups are rising to the challenge to create tailored solutions that help corporations access the information they need to inform decisions that meet ESG targets.
Pendulum’s AI-powered solutions, for example, enable organizations to reduce product waste, revenue loss, and excess greenhouse gas emissions. Built on AWS, its software can predict demand, plan supply, and geolocate shipments.
Meanwhile Sinay, a French start-up that uses artificial intelligence to analyze ocean data — on shipping movements, weather patterns, and air and water pollution — can help the maritime industry streamline its operations and reduce its environmental impact.
This is part of a wider trend in which a constellation of startups are collectively providing comprehensive solutions to help businesses minimize climate change contributions with AI.
Applications such as emissions monitoring, recycling management, and predictive infrastructure maintenance are attracting significant interest from big companies along with investors, highlighting the potential for entrepreneurs globally.
Validating ESG data with AI
Unless ESG data can be verified as reliable, organizations will struggle to deliver on their sustainable targets. AI is helping to deliver automated engines that allows companies to validate data across any number of checkpoints against their own standard operating procedures.
By automating the validation process, innovative AI solutions can ensure the highest data quality, significantly reduce manual efforts, and enhance compliance with regulatory standards.
Finally, innovative startups are creating a constellation of solutions that address external data gaps. This advanced technology positions companies to confidently meet stakeholder expectations and contribute to a more sustainable future.
Parth Patil is the Co-Founder of Credibl ESG
This article includes a client of an Espacio portfolio company
