Last year, at a mid-sized manufacturing company in Southeast Asia, the head of sustainability found herself drowning in spreadsheets. It was ESG reporting season, and gathering non-financial data across environmental impact, workforce diversity and ethical governance was turning into a chaotic three-month saga. Emails flew back and forth between departments. Half the data came in PDFs, the rest in inconsistent formats. Despite the team’s diligence, errors slipped through, and stakeholders questioned the credibility of the final report.
Unfortunately, this isn’t an isolated story.
The ESG reporting boom and data dilemma
In 2024, over 90% of S&P 500 companies published ESG reports, and 86% of investors now consider ESG metrics before making decisions (source: Key ESG, 2024). But the road to reliable ESG reporting is filled with potholes. Data fragmentation, lack of standardisation and inconsistent methodologies plague even large enterprises.
What’s worse, 88% of executives rank ESG data quality among their top three concerns, according to Veridion 2024. In such a scenario, organisations must ask: Is continuing with manual data collation and validation sustainable?
The AI advantage
Artificial Intelligence (AI) is emerging as a transformative force in ESG reporting. Whether it’s parsing emissions data, scanning supplier disclosures or validating diversity, equity and inclusion (DEI) benchmarks, AI can reduce human error, improve consistency and drastically speed up the reporting process.
According to Fintech Futures, companies that use AI-driven ESG tools see up to 40% faster data processing and 30% fewer errors. AI doesn’t just automate processes; it ‘learns’ how to flag inconsistencies and adapts to changing frameworks like GRI, SASB and CSRD.
Organisations are turning wise – one example is MUFG, one of Japan’s largest banks, which recently invested in sustainability reporting automation and AI to streamline its ESG efforts.
Challenges: Why manual validation still persists?
Despite AI’s appeal, many organisations are still stuck in manual mode. Why?
Firstly, trust. ESG is closely tied to a company’s brand and ethical standing. Many sustainability officers and auditors still prefer the perceived ‘control‘ of manual validation. Secondly, AI adoption requires investment – in tools, training and internal buy-in. Smaller firms or those new to ESG compliance may find it difficult to opt in.
And finally, there’s the ‘data in, data out’ challenge. If foundational ESG data is flawed or incomplete, AI alone can’t fix it. It can validate, analyse and standardise – but it cannot manufacture quality data.
Regulations are shifting the game
Globally, ESG regulations are evolving. The European Union’s Corporate Sustainability Reporting Directive (CSRD) will soon require over 50,000 companies to report in a standardised format. In the US, the Securities and Exchange Commission’s (SEC) Climate Disclosure Rule mandates certain firms to disclose Scope 1 and 2 emissions.
These shifts are not optional. AI is uniquely equipped to ensure compliance across jurisdictions, quickly adapting to regulatory changes and minimising the risk of non-compliance.
In Vietnam, even central bankers have endorsed the use of AI to improve the quality of ESG disclosures in the banking sector.
Hybrid approaches: The middle path forward
The ideal path may not be ‘AI vs manual’, but ‘AI with oversight’. Think of AI as the engine and manual validation as the final-check system. AI can process bulk data, run predictive analytics and align with global frameworks. But final checks, contextual interpretation and stakeholder narratives still benefit from human judgement. Such hybrid models are already in motion in firms experimenting with AI copilots and ESG data lakes that flag outliers for human review.
The Road ahead
Going forward, organisations must ask themselves questions like: Are we collecting the right data at the source? Are we future-proofing our processes with scalable technology? Is our reporting framework agile enough to adapt to shifting stakeholder expectations? Answering these questions requires cross-functional collaboration between sustainability, finance, IT and compliance teams. AI can be the glue connecting these points.
To conclude, the future of ESG reporting is not about choosing between AI and manual validation. It’s about intelligent automation with wise team-play between both. Organisations must understand that ESG reporting is no longer a box-ticking exercise. It’s a strategic imperative for companies to grow responsibly.
The author is founder and director, World of Circular Economy (WOCE). Views are personal.
