AI Rising: Can Artificial Intelligence Truly Transform ESG Reporting (Without Creating New Risks)?

The Data Challenge in ESG

One of the biggest hurdles in ESG reporting has always been data—collecting it across complex supply chains, ensuring consistency, and presenting it in a way that investors and regulators trust. With the EU’s CSRD and similar regulations demanding detailed, auditable disclosures, the stakes have never been higher. This is where Artificial Intelligence (AI) has entered the ESG conversation.

AI as an Enabler

AI tools are now being deployed to automate data collection, detect anomalies, and even forecast environmental impacts. For instance, machine learning models can analyse satellite images to monitor biodiversity loss or predict emissions trends. Natural language processing can help firms scan thousands of supplier reports to flag potential compliance risks. By doing the heavy lifting, AI promises to make ESG reporting not only faster, but more reliable.

Risks in the Machine

Yet AI itself is not without ESG challenges. Its environmental footprint—from the energy-intensive nature of data

centres to electronic waste—is significant. Beyond the environmental, the “S” and “G” pillars also come into play: algorithmic bias, lack of transparency, and ethical concerns around data privacy all raise red flags. Regulators are increasingly aware of this dual role, with discussions emerging on how AI governance and ESG governance must go hand in hand.

Finding the Balance

For businesses, the message is clear: AI can be a powerful enabler of sustainability, but it cannot be adopted

uncritically. Companies must ensure that their AI use is ethical, transparent, and aligned with their broader

sustainability commitments. Those that strike this balance will not only meet disclosure requirements but also

build resilience and trust in an increasingly data-driven world.

Get in Touch:

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Matthew Aquilina

maquilina@quazar.mt / +356 2388 4600