Putting AI to Work for Better ESG Data

Environment, social, and governance (ESG) reporting has never been easy—and with pressures rising from investors, corporate leaders, and the government, organizations are struggling to meet the needs of all stakeholders.

Facility managers must grapple with reaching and documenting progress toward ESG goals that align with the desires of sustainability-minded investors while boards and corporate leaders argue that ESG is expensive, ineffectual, and not worth the effort.

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All the while, the Securities and Exchange Commission (SEC) is working harder than ever to promote more transparency in ESG reporting, with new rules that require at least 80% of assets in funds labeled for ESG to be used for that purpose.

The Problem with ESG Reporting

Despite the controversy ESG reporting begets, it is an important way for stakeholders to hold organizations accountable to the sustainability goals they set, especially as the climate crisis continues to worsen. The World Economic Forum explains that ESG reporting “provides evidence of the commitment to ESG targets and values by documenting carbon footprint, energy efficiency, pollution, human rights, and diversity and inclusion, within the organization and across the supply chain.” However, ESG data tends to fall short of what it promises due to roadblocks that prevent companies from producing precise, on-target ESG reports.

A significant issue with ESG reporting is the inconsistent nature of ESG frameworks across organizations since terms like sustainability and diversity can mean something different to every company. The wide variety of frameworks within each industry complicates the process of comparing progress and setting industry-wide standards.

Apart from incompatible frameworks, ESG reporting is often hindered by unreliable data collection methods. Even though a power plant might be making a concerted effort to meet their sustainability goals, messy data classification can cause those efforts to be underreported. Avoiding this problem is especially critical at a time when all eyes are focused on the importance of green power generation and decarbonization efforts.

Considering the barriers to effective ESG reporting, many utilities are searching for better ways to capture their progress, suggesting a need for new solutions. Fortunately, those solutions exist and have been optimized to help companies determine the best ways to utilize them to help meet organizational goals. Every facility manager can implement Internet of Things (IoT) and artificial intelligence (AI) technologies to improve their ESG goal-setting and measurement. With better ESG data, organizations can have a clear picture of what they need to do to achieve net-zero goals and stay aligned with the needs of stakeholders.

Where AI Can Help

Commercial and industrial (C&I) entities are growing increasingly familiar with the digitization of their operations, but it doesn’t stop there. Utilities can digitize their ESG reporting efforts by tapping into the vast functions of artificial intelligence to set the right goals, achieve them, and share their progress with stakeholders.

There are many ways that AI can facilitate ESG reporting, but they all have one thing in common: data. The colossal increase in the amount of data over the past decade, from 6.5 zettabytes in 2012 to 97 zettabytes in 2022, has given way to the modern applications of AI technology that now exist. Today, AI technology uses data to provide intelligent suggestions through automated data management and analysis. As the trend of exponential data growth continues, the opportunity for AI to take over time-consuming tasks in even the most manual industries widens. Rather than having humans comb through huge swaths of data, AI can sort through and pick out important pieces of information in a fraction of the time it would take a human to do the same thing.

In line with technological advancements in AI, C&I entities are accumulating more data than ever from IoT devices, like sensors, which constantly transmit information. Power plants and other utilities that equip their factories with these devices are facing a unique opportunity to take advantage of the data that comes from them. However, this opportunity is often wasted, with facility operators facing an overwhelming number of alerts that flood their attention with useless information. AI can solve this problem by filtering out irrelevant alerts, keeping operators aware of only the information that is important for them to know.

This is especially useful for keeping facility managers informed in real-time about matters related to ESG efforts, including whether there is a water leak hiking up the facility’s resource consumption. Using AI for facility monitoring is a helpful way for managers to make progress on their ESG goals, but AI can also be used to set those goals and accurately report ESG outcomes.

Setting and Reporting on ESG Goals With AI

Designing environment, social, and governance goals for an organization is a daunting task. There are many things to consider, including past performance, current technologies and market changes. Businesses often struggle to set goals that are “SMART” — Specific, Measurable, Achievable, Relevant, and Time-bound. When setting goals, AI can help by analyzing relevant company data and offering recommendations on what metrics are SMART and realistic. Hybrid AI technology is particularly useful for this task as it combines traditional, data-driven automation with cutting-edge, human-like reasoning. This technology can review both data and historical knowledge — like ESG goal frameworks used by other organizations within the industry — to generate accurate predictions on what a power plant can achieve. By functioning as an automated ESG advisor, hybrid AI software gives organizations perspective and relevant information to guide the creation of SMART metrics.

AI can also improve data collection methods by helping utilities maintain a document trail of all ESG activities. Keeping a ledger of all documents related to ESG efforts is the best practice for ensuring that the organization can prove its compliance with its own standards and any other legal requirements when investors and other entities investigate. However, sorting through hundreds of pages of documents and only saving those that relate to ESG reporting is difficult for any organization to manage without the help of technology. AI embedded with metadata capabilities can read through documents, analyze key words, and save data from relevant files, keeping them available when the time comes to report results. The automation of this process saves both time and money while preserving the accuracy of reported ESG data.

The Future of ESG Measurement

Technologies like AI, IoT devices, and machine learning can alter the future of ESG reporting by augmenting tedious tasks and helping businesses stay in compliance with stakeholders and the SEC without undue financial burden. Problems with unstandardized frameworks and unsatisfactory data-gathering can be remedied with artificially intelligent historical analysis and document tracking. Best practices for creating goals, measuring results, and sharing ESG progress are made simple with the combination of AI and on-hand organization data. Utilities that let AI do the grunt work for their ESG measurements are improving industry-wide ESG data and are in a better position to understand where they stand on their journey to sustainable operations.

—Richard Martin is senior vice president at Beyond Limits, an AI software company. Martin has more than 30 years’ experience growing software companies through value creation within the process industry. His broad experience in sales, business development, marketing, company and solution strategy, partner programs, and operations stem from his roles at top companies like Texas Instruments, AspenTech, and Aveva. He has a degree in chemical engineering from the University of Mississippi.