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      AI is increasing greenwashing risk by accelerating the creation, amplification and dissemination of environmental claims, often across multiple channels and faster than traditional review processes can keep up with.

      Greenwashing refers to deceptive or false practices that exaggerate, omit, conceal or otherwise misrepresent the environmental impact of a product, service, process or an organisation. It includes vague claims that cannot be confirmed, selective presentation of information, or the use of words, symbols and imagery that lead to an unearned sustainable impression. This risk is not confined to formal sustainability reports. AI-generated or AI-assisted claims appearing in advertisements, websites, product packaging, bid materials or investor communications may all attract scrutiny if they are inaccurate, vague or not properly substantiated. This practice can undermine credibility, expose organisations to regulatory action, and impair stakeholder trust.

      Annabel Reoch
      Annabel Reoch

      Global Head of Ethics and Compliance

      KPMG in the UK

      Why greenwashing risk is increasing

      The prominence of greenwashing risk and its implications are increasing with stringent regulatory focus, as well as ongoing focus on sustainability by investors and consumers. Rising enforcement pressure in some markets is also contributing to more cautious sustainability communications, so-called “greenhushing”, where certain organisations may selectively withhold or downplay information about their sustainable initiatives to avoid scrutiny.

      Some global greenwashing observations include:

      • A European Commission survey found that 53% of environmental claims provided vague or misleading information, while 40% of environmental claims were unsubstantiated¹.

      • Private companies represented 70% of greenwashing cases in Europe and North America², indicating a greater need to scrutinise sustainability claims made by private companies, according to a 2024 report.

      • Various sectors reported substantial increases in the number of organisations flagged for greenwashing risk from 2024 to 2025, for example a 24% increase in the Retail sector, a 19% increase in Banking and Financial services sector and a 21% increase in Food and Beverages sector³. These trends indicate that greenwashing is not isolated to specific industries but that it represents a pervasive, cross-sector challenge.

      • Regulators in the EU, UK, US, and APAC are increasingly issuing fines, penalties and advertisement bans. Disclosure requirements by organisations and investors are also becoming rigorous with enforcement intensity rising year‑on‑year. For example, legislation in the UK now calls out the associated risk of fraud with greenwashing⁴ and the EU’s Empowering Consumers for the Green Transition Directive, which EU member states had to transpose into national law by end of March 2026⁵. In Canada, Bill C-59 amended the Competition Act in June 2024 to explicitly address greenwashing by requiring certain environmental claims about products, businesses and business activities to be properly substantiated⁶.

      Greenwashing risk in the AI era

      The emergence of AI has added a new dimension to greenwashing risk. Agentic AI operates autonomously in areas such as drafting documents, data analysis and workflow execution, and is run with little human intervention. Its increasing deployment may therefore increase greenwashing risk in areas such as sustainability data analytics, reporting and marketing content, and this raises significant concerns where base data is incomplete or unreliable, audit trails are not clear or human review is limited or absent.

      Greenwashing risk and associated AI implications can be viewed through three lenses:

      The organisation’s communication of its environmental performance and targets is at potential risk of greenwashing, and breaches could lead to reputational damage, decreased market value, and regulatory investigations and penalties.

      • The dynamic regulatory environment increases the risk of regulatory non-compliance, especially for organisations operating across multiple jurisdictions and countries with varying greenwashing related regulations.

      • Organisations may be held liable for any significant gaps or control issues in their ESG governance framework and mechanisms to mitigate and monitor greenwashing risk.

      • The widespread adoption of AI has increased the organisation's need for appropriate controls over its AI models to ensure accurate and verifiable green claims.

      • AI algorithms which are difficult to audit and could be viewed as so-called “black box”, increase the risk of sustainability misstatements in annual reporting. 

      The products or services that an organisation sells are susceptible to greenwashing risk.

      • Greenwashing risks emerge from each step of a product/service cycle, starting from design and certification to production, marketing, sales, disposal and sustainability reporting.

      • These risks are amplified by complex supply chains spread across various geographies and reliance on the third-party data sources, which can limit an organisation’s ability to substantiate its environmental claims.

      • Increasing adoption of AI without adequate audit trail or authentication of underlying data could lead to false, vague or unverifiable green claims, for example in product packaging and marketing.

      • Claims by social-media influencers and claims on product packaging about product sustainability may amplify greenwashing risk if they lack verifiable data, making it difficult for consumers to confirm environmental claims.

      Each organisation has a variety of stakeholders, ranging from external, such as customers, suppliers or investors, to internal, such as employees, who may be impacted by or contribute to greenwashing risk.

      • Management could face regulatory scrutiny because of lack of appropriate oversight of internal processes, including supply chain due diligence, to mitigate and monitor greenwashing risk.

      • Biased AI-driven ESG disclosures can hamper investor and consumer trust, if the decision-making processes or underlying algorithms of AI are opaque.

      Instances of greenwashing

      Greenwashing can occur in many different forms. Illustrative examples of greenwashing and how it can be driven by AI include:

      • Advertising campaigns generated by AI agents overstating an organisation’s claims as a leader in renewable energy, while downplaying its other business segments in non-renewable energy.

      • Promoting an image of achieving net-zero emissions through AI-generated advertisements, while having significant exposure to businesses with adverse environmental impacts.

      • Vague carbon neutral claims made without adequate underlying data or evidence to substantiate these claims, particularly where this data has been driven by AI and there is a lack of audit trail for the data.

      • Product components or packaging tagged as sustainable or recyclable, where such items are not recyclable to the extent claimed.

      • An organisation’s suppliers are found to be in violation of anti-greenwashing regulations, contradicting the organisation’s sustainable image.

      • Funds raised or earmarked for sustainable purposes (e.g., green bonds, sustainability-linked loans or ESG-labelled capital) are reallocated to non-sustainable projects, thereby violating the purposes for which this funding was obtained.

      • Biased sustainability data selection and presentation could promote greenhushing, for example, where AI has been trained to find only the positive environmental data and highlight it, while ignoring or downplaying any negative information.

      What organisations should do to mitigate greenwashing risks

      To respond to greenwashing risks effectively, organisations should adopt a proactive approach towards ESG integrity and AI governance, and these might include:

      • Strengthen governance over sustainability claims, through periodic greenwashing risk assessments and proactive compliance reviews.

      • Conduct lifecycle‑based reviews to assess environmental impact and greenwashing risks of various products/services offered across geographies.

      • Implement tech-based monitoring mechanisms to identify and address potential greenwashing violations at an early stage.

      • Implement data integrity controls and AI governance frameworks to ensure transparency, documentation and necessary human oversight for AI‑generated ESG content.

      • Execute robust third‑party due diligence and monitoring where sustainability claims are based on information provided by suppliers, partners or certifiers.

      • Invest in training to ensure employees are aware of regulatory expectations and permissible sustainability claims.
         

      Sustainability as a value driver and key consideration for operational resilience is reshaping corporate strategy globally, driven by consumer demand, investor expectations and increasing scrutiny by regulatory bodies. With this shift, greenwashing has transformed into a material risk. At the same time, the rise of AI is changing how ESG narratives are being created, analysed and communicated, amplifying both the speed and scale at which misleading claims can propagate. In this evolving landscape where AI is becoming more prevalent, organisations should understand their sustainability risk profile, define their sustainability risk appetite, document where and how AI is being used to support sustainability initiatives and effectively navigate regulatory expectations, stakeholder expectations and shifting market dynamics.

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