Addressing roadblocks for the new ESG reporting paradigm
WHILE environmental, social and governance (ESG) has long been a point of interest in business and politics, recent global ESG standards – particularly the first International Sustainability Standards Board (ISSB) standards announced in June – mark a new era of sustainability-related disclosures in global capital markets. Singapore, for one, will require public companies to make climate-related financial disclosures starting 2025.
Meanwhile, private companies reporting annual revenues of at least S$1 billion need to start making disclosures in 2027.
Singapore’s new disclosure obligations underscore the growing need for transparency in financial reporting around carbon footprint as we collectively aspire to reach the 2050 net-zero emissions national target.
Amid the urgent call for climate action, organisations are under mounting pressure to enhance their ESG reporting, driven by heightened stakeholder expectations and intensified scrutiny of regulators and investors.
When good intentions clash with data reality
Many Singapore organisations are committed to ESG but have a limited grasp of the intricate regulatory framework underpinning ESG reporting. They also face challenges stemming from unreliable data collection and report-writing processes.
A new study from UiPath has found that 70 per cent of Singapore organisations still rely on spreadsheets to collect, analyse and report ESG-related data, and that 60 per cent use manual data entry and online forms to collect this information. Furthermore, 79 per cent of senior business leaders surveyed in Singapore found data collection to be the most challenging aspect of the entire ESG reporting process, with 56 per cent of respondents reporting inconsistent and irregular data collection procedures.
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While some organisations do it intentionally, corporate greenwashing can often result from inadequate or misinterpreted data from a company’s supply chain or the failure to source external verification of ESG-related data.
In the light of new ESG standards and regulations, organisations need to overhaul existing ESG processes and ensure that data is updated across supply chains to supplement ESG claims.
One innovation that has already been enhancing accuracy and compliance in ESG reporting is artificial intelligence (AI)-powered automation. HP, for instance, has integrated its automation tool with a report production tool to aid in the report-writing process. Such innovation seamlessly connects data points to corresponding statements in the report, enabling automatic updates. This process significantly reduces the need for manual work and improves accuracy and transparency in HP’s ESG reporting.
Today, software robots are being used to monitor performance against ESG key performance indicators (KPIs) such as carbon emissions reduction in real time. This allows organisations to effectively gauge their ESG progress at a particular point in time without needing to wait until the end of the business quarter. Meanwhile, AI can streamline the process of collecting extensive ESG data from diverse sources such as financial reports and sensor data. Organisations can then leverage machine-learning algorithms to analyse ESG data and identify patterns, anomalies and potential risks or opportunities.
Addressing roadblocks to drive implementation success
In spite of numerous benefits, organisations still wrestle with internal barriers when it comes to implementing automation effectively.
Beyond the challenge of insufficient data, securing buy-in from the board across all organisational levels and seamlessly integrating automation into existing legacy systems can pose significant hurdles. Moreover, we also have to acknowledge the limitations of automation. While automation holds the potential to mitigate human errors and enhance the precision of ESG reports and disclosures, teams still have to start with a foundation of accurate data.
Process, people and technology have to be aligned for organisations to fully harness the potential of automation for ESG reporting and compliance. To gain the support and commitment of employees, organisations must provide them with the required skills and knowledge to effectively use AI and automation to drive better ESG data accuracy and decision-making in ESG management processes.
Successfully navigating ESG use cases for automation hinges on nurturing a strong collaboration between sustainability and IT teams. While sustainability teams may possess critical ESG data, they may lack the necessary tools to analyse data quickly and efficiently. Conversely, IT teams with the right technology might not have direct access to relevant sustainability data.
The unique strengths of these teams – sustainability’s data and IT’s technological prowess – can be synergised to drive advancements in ESG reporting and decision-making. Bridging the communication gap between these teams through clearly defined goals and open dialogues sets the stage for a more comprehensive alignment of ESG objectives with technological solutions, fostering innovation throughout the organisation.
This approach not only propels companies toward their ESG goals but also positions them to thrive in a world where sustainability and technology intersect with increasing significance.
The writer is area vice-president, Asia at UiPath
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