International ESG Reporting Standards: Navigating the Challenges

Environmental, Social, and Governance (ESG) reporting has become a critical aspect of corporate transparency, enabling businesses to showcase their commitment to sustainability, social responsibility, and ethical governance. In today’s sustainability-driven market, investors, consumers, and regulators demand accurate, transparent, and verifiable ESG disclosures. ESG reporting is now a strategic necessity for businesses aiming to stay competitive, compliant, and sustainable.

However, the global landscape of ESG reporting has fundamentally changed. Unlike traditional financial reporting, which historically required around 200 data points, ESG reporting under CSRD necessitates over 1,100 data points, and that number is growing exponentially as companies consider complexities across subsidiaries, divisions, locations, and third-party relationships.

The Absence of a Universal Framework

ESG (Environmental, Social, and Governance) reporting has become an essential part of corporate disclosure, with investors, regulators, and consumers demanding greater transparency in sustainability efforts. This absence of a universal ESG reporting framework creates confusion for companies trying to align their sustainability disclosures with multiple reporting guidelines and regulations. Organizations looking to develop an effective ESG reporting strategy must navigate multiple frameworks that cater to different aspects of sustainability disclosures.

Regulatory Changes and Compliance

Governments and regulators frequently update ESG disclosure requirements. Several major regulatory changes have been introduced to enforce ESG reporting compliance.

While the USA is going in different directions, and the EU considers streamlining and integrating requirements later this month, the global landscape of Environmental, Social, and Governance (ESG) reporting has fundamentally changed with the European Union’s Corporate Sustainability Reporting Directive (EU CSRD) first wave of corporate reports being published in 2025.

The Risk of Greenwashing

One of the biggest threats to ESG transparency is greenwashing-the practice of misleading stakeholders by exaggerating or fabricating sustainability claims.

Last week was intense and enlightening in my journeys across Europe, engaging with nearly 60 organizations across multiple ESG and CSRD discussions. But there are challenges . . .

Strategic Approaches to ESG Ownership

One striking observation from my recent workshops across London, Utrecht, and Stockholm is the variation in how companies structure ESG ownership. Some firms have designated ESG controllers or sustainability officers, while others distribute ESG responsibilities across finance, compliance, risk management, legal, audit, and internal control teams.

With the first CSRD-aligned reports already being released, it is evident that ESG reporting is more than a regulatory requirement-it is a fundamental shift in how businesses operate and disclose their impact. Leading companies are integrating ESG into their core business strategy, governance frameworks, and risk management processes. As ESG and EU CSRD continue to evolve, organizations must focus on smarter, data-driven approaches that align ESG reporting with broader business objectives.

The Role of AI in ESG Reporting

January 6 - Artificial intelligence is appearing across every part of the sustainability landscape as a growing number of companies use AI-powered solutions for reporting, risk management and decision-making. The question is whether these new tools are strengthening sustainability information - or simply adding new layers of complexity.

Companies often hold relevant information across multiple systems, making consistency difficult. Tools such as Sweep’s carbon management platform use machine learning models to identify inconsistencies in supplier data and flag them for review, helping companies improve accuracy. This is particularly useful for double materiality assessments, where gaps in supplier data and inconsistent reporting formats remain the norm.

Filling those gaps remains one of the hardest parts of reporting. Firms like Watershed have developed proxy-data approaches for Scope 3 emissions that draw on broad datasets to estimate emissions factors where supplier information is missing.

These estimates do not replace direct data, and using them brings challenges: different models can produce materially different results, and proxy-based approaches remain sensitive to incorrect assumptions. However, when used well, they help companies move from broad averages toward more specific, decision-relevant estimates.

AI can also make internal data more accessible. Large organisations often struggle to retrieve information needed for sustainability disclosures, especially for governance, risk processes and supply-chain due diligence. AI-powered search and tagging tools can help locate evidence and match it to disclosure requirements. But these systems can also introduce new risks if they surface outdated or incorrect information.

The usefulness of sustainability data also depends on whether it informs financial decisions, supervision and strategy. The NGO Ceres, for example, used AI to analyse climate disclosures from hundreds of insurers. By applying consistent tags across TCFD pillars, it identified patterns and gaps that would have taken analysts months. The approach improved comparability and provided clarity on where firms aligned with reporting expectations.

These capabilities are also valuable for supervisors. The Bank for International Settlements has explored how AI can help prudential authorities move beyond firm-by-firm analysis to understand system-wide risks. AI can compare disclosures, highlight missing metrics and identify patterns in reports. This gives supervisors a clearer view of systemic risks and practices.

Investors face similar challenges. The first cycle of CSRD reporting provided much richer information, but many investors struggled to review and compare these reports. Tools like those developed by Climate Aligned use natural-language analysis to benchmark companies’ transition plans and climate reporting. They allow investors and other financial actors to understand whether commitments connect with capital expenditures, and activities align with taxonomies.

AI is also being applied to manage risk in globalised supply chains, which are complex and vulnerable to disruption. During the early stages of the COVID-19 pandemic, satellite data showing reduced pollution in parts of China, which provided an early signal of factory shutdowns. Similar dynamics can be seen in microchip shortages, or reductions in nuclear power output during periods of low river flow.

The amount of Earth observation data has also grown rapidly thanks to satellite imagery, climate models and sensor networks, generating terabytes of data and trillions of observations.

The non-profit ClimateTRACE uses nearly one billion datapoints to track emissions of methane and other greenhouse gas emissions around the world. That kind of analysis would be well beyond the capacity of individual analysts without AI. This not only boosts transparency but also exposes risks that might otherwise go undetected.

Google’s Deepmind just released WeatherNext 2, which uses AI to significantly boost the accuracy of storm forecasts. This can provide extra days of warning, enabling earlier evacuations and reducing harm. Better predictions of drought or crop failure can help identify famine risks before market signals appear. With more lead time, governments and humanitarian organisations can act to reduce impacts.

Challenges and Risks Associated with AI in ESG Reporting

Importantly, AI-generated signals require human judgment to properly assess their implications and be integrated into decision-making processes.

That goes for all AI applications in sustainability. One concern is the rise of low-cost AI-generated reports. These tools can produce polished documents quickly, but with limited quality control. Outputs may not reflect a company’s strategy or specific challenges, and without expert oversight, the reports pose significant reputational or regulatory risks.

Another challenge is information overload. AI reporting and synthetic datasets are creating vast amounts of new data, but much of it may not be useful for decision-making. This just creates noise for analysts, investors and supervisors to sift through.

Data security is a further risk. Poorly governed AI tools can create vulnerabilities that expose sensitive client or personal information. The data security threat comes both from AI-enabled cyberattacks and from organisations failing to properly manage their own data when using AI.

A final issue is the potential for systemic correlations. If many market participants rely on similar AI models, the diversity of judgment may decline. This increases the volatility of bubbles and market crashes, according to research by the LSE. Markets depend on differentiated views, and overreliance on similar approaches can undermine that stability.

The bottom line is that AI has an important role to play in managing sustainability risks in an increasingly volatile world, but only if it is used thoughtfully, and within a well-governed environment. Most importantly, humans need to stay in the driving seat.

The State of ESG & Sustainability Reporting: Challenges, Tools and Outlook

BARC has published a new study titled The State of ESG & Sustainability Reporting: Challenges, Tools and Outlook, offering insights into the challenges companies face in their ESG (Environmental, Social and Governance) reporting processes and the tools available to overcome these challenges. ESG reporting has become increasingly important as it is seen as a vital instrument to maintain a positive image with customers, employees and business partners.

According to co-author FH-Prof. Dr. Susanne Leitner-Hanetseder, “ESG reporting is a disruptive process, but necessary to gain the trust of stakeholders." Stefan Sexl, co-author of the study, added: “The technical implementation of ESG reporting is a significant challenge and there is no market standard yet. Companies need to invest in the right tools and solutions to integrate their data and add ESG data to their reporting. Sustainable customer branding is the main driver for ESG reporting.

ESG reporting is a data-intensive task. The challenge of collecting data, especially for environmental KPIs, is often underestimated. ERP and CPM systems, Word, Excel, and BI tools are used in combination with specialized solutions, often developed by start-ups, for the technical implementation of ESG reporting.

The study concludes that ESG reporting is a complex and challenging process, but it is essential for companies to demonstrate their commitment to sustainability and social responsibility. BARC is a leading analyst firm for data & analytics and enterprise software with a reputation for unbiased and trusted advice.

Even with adjustments to Europe’s new sustainability rules, companies face increasing pressure. The Corporate Sustainability Reporting Directive and the Corporate Sustainability Due Diligence Directive require firms to work with larger volumes of data from more sources, often under tight timelines.

AI’s ability to interpret unstructured data, compare disclosures at scale and identify patterns means that it can help organisations manage these pressures, but its use also demands caution.

Data Inconsistencies and Third-Party Assurance

But there are challenges . . . unlike traditional financial reporting, which historically required around 200 data points, ESG reporting under CSRD necessitates over 1,100 data points, and that number is growing exponentially as companies consider complexities across subsidiaries, divisions, locations, and third-party relationships.

One of the most pressing challenges of EU CSRD is the requirement for third-party assurance on ESG reports. Organizations are already experiencing a one-third increase in audit fees due to limited assurance requirements, and these costs will escalate significantly once reasonable assurance becomes mandatory.

Internal vs. External Data

During my workshops in Utrecht and Stockholm, I facilitated discussions on what keeps organizations up at night regarding ESG and CSRD compliance. Understanding the role of internal vs.

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