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"AI is not a magic wand. If your data is poor, AI just gets you to bad results faster — in a nicer format."
WHERE AI CREATES REAL VALUE
The opportunities are real — if you use them right
AI can bring greater structure and efficiency to sustainability work while also supporting analysis and research.
The following benefits are genuine:
Data structuring: For many sustainability managers, data is the most pressing challenge: thousands of data points from supply chains, invoices, and disparate source systems — with no clear picture. AI can genuinely help here. Not as a substitute for human judgment or missing data, but as a tool that structures, filters, and recognises patterns. AI can also replace many manual steps when extracting data from documents and invoices.
Materiality Assessment: Double Materiality Assessments that once required weeks of research, benchmarks, and workshops can now be prepared and documented far more efficiently with AI support.
Drafting support: AI is also a genuine asset when it comes to generating and formulating text. A first draft, an outline, an opening paragraph — it comes together quickly and lowers the barrier to getting started. AI is also strong at rewriting and adjusting tone. Keep in mind: the better your templates and guidance, the better the output.
Market screening: Sustainability requirements and regulations change constantly. AI can help monitor and contextualise new EU regulations, requirements, and supply chain risks on an ongoing basis. That said, when it comes to interpretation and nuance, AI makes a significant number of errors.
THE OTHER SIDE OF THE COIN
Where the risks are underestimated
Companies that deploy AI uncritically in ESG reporting take on significant risks. It starts with data quality: garbage in, garbage out applies here just as anywhere else. A language model that hallucinates Scope 2 emissions or misrepresents a regulatory requirements catalogue is not just useless — it is dangerous.
Companies should pay particular attention to the following risk areas:
Hallucinations. AI models fabricate convincingly worded but incorrect facts, figures, source references, or regulations — without any warning. There have already been several
Voice and authenticity.AI-generated text often sounds polished but generic. Companies with a recognisable brand voice or personal style need to actively shape the output — otherwise everything sounds like no one in particular.
Adoption without review. The biggest mistake is copy-paste without reading. AI-generated text frequently contains logical jumps, repetition, or phrasing that sounds correct but is devoid of substance.
Data protection. Sensitive company data fed into external AI services may violate compliance requirements.
Intellectual property and compliance. For mandatory publications (annual reports, ESG reporting), the company bears responsibility for the content — regardless of who or what produced it.
Greenwashing risk. Automatically generated, optimistically worded reports without a solid data foundation promote greenwashing, whether intentionally or not.
Regulatory grey areas: AI-assisted reports exist in a legal grey zone: who is liable for AI-generated errors in mandatory reporting?
AN HONEST ASSESSMENT
What AI can — and cannot — do
AI can…
✓ Aggregate and structure data
✓ Identify patterns in large datasets
✓ Generate report text from templates
✓ Monitor regulatory changes
✓ Prepare Materiality Assessments
✓ Produce translations, transcripts, and summaries
AI cannot…
✗ Credibly replace missing data
✗ Guarantee data protection & intellectual property compliance
✗ Make strategic ESG decisions
✗ Guarantee legally sound compliance
✗ Replace authenticity and credibility
✗ Be held liable for errors in mandatory reports
✗ Take on human accountability
PRACTICAL RECOMMENDATIONS
The pragmatic path forward
Neither blind enthusiasm nor reflexive rejection will get you anywhere. Companies that successfully use AI in ESG follow a clear pattern: they start small, with well-defined use cases and a solid data foundation. They keep human review and accountability in the process. And they treat AI as an efficiency tool — not as a strategic substitute for expertise.
In practice, that means: AI for preparing and condensing data, for creating first drafts, for monitoring regulatory developments. Not for final quality assurance, not to substitute for missing data, and not for the strategic decisions about what truly makes a company sustainable.
The difference between hype and hysteria lies not in the technology — it lies in the governance you build around it.
Conclusion: Neither Hype nor Hysteria — Just Clarity
AI is not a silver bullet for ESG challenges. But it is not a risk to be ignored either. The technology is mature enough for responsible use — provided companies know what they want from it and what they do not.
Those who lay the right foundations now — clean data, clear processes, defined accountabilities — will benefit from AI. Everyone else will find that getting to the wrong answer faster is no improvement at all.
