As sustainability reporting gains prominence around the world, it is also becoming increasingly complex. Technology is providing cutting-edge tools, and is spearheading a shift towards automated, real-time, and credible reporting. The shift comprises replacing cumbersome and scattered data gathering through connected systems with key technological drivers like Artificial Intelligence (AI) and machine learning. Today, AI analytics can track sustainability data patterns, model risks projections, and generate automated reports. Meanwhile Internet of Things (IoT) sensors and intelligent meters ensure continuous, precise carbon emissions, energy consumption, and water consumption measurements. Complementing these, blockchain use for data integrity and auditability through immutable records, fosters transparency and trust.

However, technology is not an unmixed boon. AI has the ability to disrupt entire industries by causing wide scale job losses. Protection of personal data and privacy are another concern.  Additionally, AI applications come with their own carbon footprint. Data centres, which power AI applications, and currently represent 2-3% of global greenhouse gas emissions, are set to see their power requirement jump by 160% by 2030. Therefore, the plan to harness technology for smarter sustainability disclosures must be both inclusive and thoughtful. More important is not what AI can do but how thoughtfully it gets deployed for the intended purpose.

Climate disclosures before the tech disruption

In order to understand how technology can transform sustainability reporting, it is important to first examine the limitations of traditional climate metrics and disclosure regimes. Until recent years, mandatory climate disclosure regimes were largely absent across the world. Most of the frameworks were voluntary and limited in scope which often led to inconsistencies. These disclosures rather complicated investment decisions and provided deficient metrics about accountability for environmental risk and impact.

Some businesses deliberately limit their climate disclosures to protect their reputation, engaging in greenwashing and cherry-picking to highlight favourable outcomes while concealing negative ones. The firms that seek to be transparent often face structural barriers, including insufficient expertise, data systems, and standardized methodologies. This hinders complete and comparable disclosures.

Environmental, Social, and Governance (ESG) data also presents a number of difficulties. The relevant data comes from various sources like annual filings, regulatory submissions, corporate sustainability reports, and can be both financial (like carbon pricing or operational costs) and non-financial (such as diversity, labour practices, or the intensity of emissions). Poor data quality affects investor valuations, as analysts may rely on incomplete or incomparable metrics when assessing a company’s risk profile or sustainability performance. For example, missing emissions data can lead to underestimating a firm’s possible exposure to carbon taxes or regulatory shifts, increasing transition risk within investment portfolios. Without current and accurate data, consultants find it more difficult to compare themselves to their peers and spot compliance gaps, which has an impact on the overall advice they provide clients. Furthermore, Scope 3 emissions remain the most difficult to quantify, accounting for over 70% of an average company’s overall carbon footprint. Accurately measuring such emissions requires careful information gathering from the many suppliers or service providers, many of whom might not have the knowledge, skills, or resources to manage such aspects. As a result, many businesses use estimates, which lowers comparability and affects reliability.

Inconsistency may be the most vexing obstacle in ESG reporting, though efforts toward consolidation are gradually reducing this fragmentation. In the past, firms reporting ESG data using a range of frameworks, each with distinct definitions and priorities. The Global Reporting Initiative (GRI), for example, focused on effects on a wide range of stakeholders, while the Sustainability Accounting Standards Board (SASB) focused on financial materiality. The Task Force on Climate-related Financial Disclosures (TCFD) focused on climate risk and governance.

However, much of this landscape has now converged. The International Sustainability Standards Board (ISSB) of the IFRS Foundation assumed responsibility of the SASB standards. The IFRS S1 and S2 standards also included the TCFD recommendations. At the same time, GRI continues to operate alongside ISSB, working toward interoperability. The Corporate Sustainability Reporting Directive (CSRD) in the European Union requires reporting that follows the European Sustainability Reporting Standards (ESRS). This pushes the field even more toward global comparability and mandatory disclosure.

Even with these advances, inconsistencies persist. Although the metrics may be similar, they are often presented in different formats, with different levels of detail, or with different timeframes. Because of this, making apples-to-apples comparisons is nearly impossible. In supply chains and emerging markets, where ESG disclosure regulations may be lax or nonexistent, the discrepancies are made worse. The lack of historical data can also make it difficult to track advancement over time. Inconsistent disclosures cause a longer analysis process, the need for extra assumptions, and a decline in confidence for consultants and investors alike.

Technology’s promise: from chaotic data to intelligent systems

Technology and intelligent systems will shape the future of strong sustainability disclosure regimes. Teams will be able to increase productivity and lessen the workload associated with reporting as a result. The following pillars will serve as the foundation for technology’s promise:

Centralized repository

Data silos can be broken by using a specialized data management platform as the only source of truth. In order to meet the data needs of numerous stakeholders, centralized repositories can also help with the “collect once, report many” strategy. A sustainability data management platform can be used to analyze data and present ESG insights. Once information is uploaded on to the system, it can be processed into customized and comprehensive business intelligence dashboards. These dashboards comprise visualization engines that include benchmarks and tables which can allow for detailed comparison among related companies.

Blockchain technology

Blockchain technology creates transparent and secure audit trails for emission verification by securing an immutable record of emission data. The blockchain process involves data collection, its recording on the blockchain, validation of the transaction through consensus algorithm, creation of the audit trail, final verification, and the eventual reporting. Some of the advantages of this technology are trustworthy data, streamlines verification, increased transparency, prevention of frauds, and creation of efficient marketplaces.

Artificial Intelligence

AI is proving to be a gamechanger in the field of climate reporting. There are many clear advantages of this technology – automated mapping of specific metrics, rapid and intelligent data extraction from documents, and contextual data analysis and output generation. AI tools can independently find and extract relevant ESG-related data from thousands of documents, including public filings and sustainability reports. These tools can also be used to normalize these data points across multiple reporting frameworks. This brings about consistency and comparability across industries and geographies.

AI also has the potential to allow the sustainability teams to refocus their efforts by automating time-consuming tasks. They can spend their time on activities that have a more tangible impact, such as reducing emissions and promoting strategic change.

Large multinationals, with operations across various jurisdictions, often struggle with navigating the demands of different reporting frameworks. AI functions as an effective facilitator by pinpointing necessary data points in accordance with diverse standards and regulations. It may also aid in the creation of customized reports that meet specific jurisdictional criteria.

Another area where AI can play an important role is Scope 3 emissions’ estimation and disclosure. These emissions constitute a significant portion of a company’s overall carbon footprint. However, they are very difficult to measure due to the fact that a company’s products may pass through many suppliers. AI algorithms can help automating tasks such as calculating product-level carbon footprints and enabling real-time data monitoring across the supply chain.

Beyond reporting, AI is also seen to be increasingly used to construct climate risk management forecasting tools. AI has been successful in forecasting climate-related events. For example, by training on massive datasets of weather, hydrological, and satellite imagery, AI tools can detect deforestation and forecast climate-related events. Firms may acquire faster and more accurate data which can help them undertake forward-looking risk assessments. This can help them make more proactive climate-related decisions.

There are some obvious advantages of the adoption of technology. While data traceability across jurisdictions helps keep the companies audit and compliance ready, enhanced financial materiality lifts the firms’ ability to finetune their core business strategies. A case in point is Microsoft Cloud for Sustainability, a unified platform, connects disparate data sources, automates emission calculations, and a seamlessly integrates with the existing Microsoft ecosystems. Similarly, IBM offers ‘Envizi’ as a platform for ESG data management, and ‘Persefoni’ for focused audit-grade carbon accounting.

Conventional vs. tech-enabled disclosure systems

The dark side of “smart” sustainability

According to recent research, sustainable transitions are not always beneficial processes and might not benefit everyone equally. While using AI, IoT, machine learning, and other technologies to monitor and disclose climate change has been heralded by many as a cure-all, there is a negative aspect to their use that raises serious environmental, ethical, and social issues.

Technology has an excessive environmental cost. Rare earth minerals are the building blocks of smart devices. Their mining destroys ecosystems and contaminates water sources. Data centres are energy-guzzlers that raise concerns about the viability of AI and IoT.

Ethical and social concerns around “sustainable” technology abound. For instance, smart solutions, such as smart grids, benefit wealthy countries or corporations unequally, they create a “green divide” and contribute to social inequality.

In order to ensure that ESG reporting is ethical, transparent, and aligned with sustainability goals, it is important to integrate human oversight with algorithm accountability. Human oversight could be entrenched through oversight committees, regular monitoring, capacity building trainings, and a robust mechanism of feedback loops. On the other hand, algorithmic accountability requires transparent designs, thorough audit trails, and regulatory compliance. The integration of the two are possible in an environment of hybrid decision-making, automated alerts seeking human review, effective shareholder engagement, and regular audits and their public reporting.

It will be fascinating to see how key frameworks like the EU AI Act (adopted in 2024, with phased implementation), which is currently the world’s most comprehensive AI regulation, and OECD AI Principles fare in addressing these concerns. While the domains like privacy, social inequality, ethical and human-centric focus, and innovation with safeguards bear the stamp of impact and partial success, these frameworks provide limited attention to environmental impacts.

A smarter, not just faster, future

In an era where the greatest challenge facing humanity is climate change, responsible, and robust technological innovations carry the promise of sculpting a more sustainable future. Yet, as we stand at the crossroads of technological innovation and climate urgency, the path to sustainability demands more than just speed—it requires smarter, human-centred solutions. The allure for speed is so palpable, but the rush for automating risk may sabotage sustainability from its loftier purpose: pushing accountability, equity, and sustainable stewardship of our planet. Let technology enable communities, not just corporations, by democratizing access to climate and ESG data, ensuring transparency becomes a shared public good.

The future of ESG reporting lies in technology-driven sustainability. This means designing systems that embed ethical priorities such as justice, fairness, equity, and planetary health into how data is collected, analyzed, and disclosed. It involves using AI and digital tools to make ESG data more accurate, comparable, and timely, at the same time ensuring that humans remain the arbiters of its meaning and context. In doing so, technology becomes not a substitute for human judgment, but a catalyst for more accountable, transparent, and trustworthy sustainability reporting.