Key insights:

• • • Nature-related risks underreported — Companies’ nature-related interfaces are underreported across industries, despite being increasingly seen as decision-useful information for investors and regulators.

    • Stricter requirements for disclosure growing — Both voluntary and mandatory frameworks are increasing their requirements for nature-related disclosure.

    • Organizations should be proactive — Getting ahead of disclosure trends means that organizations should be measuring their nature-related interface as well as integrating nature-positive transition planning to their business strategy.

As the impacts of nature loss become more prevalent, companies are on business risk and performance. This is due to both physical nature-related impacts and increasing stakeholder pressure on organizations to integrate long-term nature-positive strategies. Managing nature-related impacts and dependencies is a framework-driven mandate for all boards of directors to consider.

Why nature matters

All businesses impact and depend on the four realms of nature: land, freshwater, ocean, and atmosphere to some extent, with the highest impact sectors being . These dependencies could include the provision of water supply to an organization, or services provided by nature to a business, such as flood mitigation. A could result in a $2.7 trillion GDP decline annually by 2030. In turn, most businesses also positively and negatively impact nature.

Financial flows that were determined to be harmful to biodiversity reached , including private investment in high impact sectors, with only $213.8 billion (€184.6 billion) invested in conservation and restoration. Despite this financing gap, less than 1% of publicly reporting companies currently disclose biodiversity impacts, indicating the need to align incentives and policies with nature-related outcomes.

Indeed, nature does not have a single indicator, like greenhouse gas (GHG) emissions; instead, its measurement involves multiple complex, location-specific factors. Despite this, disclosure of nature-related risks and impacts are increasingly being required by regulators.

Regulatory incentives to disclose

The disclosures being driven by regulatory frameworks include material information on all nature-related risks, particularly those requested by the International Sustainability Standards Board (ISSB) and European Sustainability Reporting Standards (ESRS). The ISSB Biodiversity Ecosystem and Ecosystem Services project (BEES) was initially considered a research workplan but was modified to a standard-setting approach.

Through its work, the ISSB due to: i) the deficiencies in the type of information on nature-related risks and opportunities reported by entities, which are identified as important in investor decision-making; and ii) the requirement of nature-related information that is not included in climate-related disclosures, including location-specific information on nature-related interface and nature-related transition planning.

On Jan. 28, all 12 ISSB members voted to , which included two important implications. One is that standard setting is to cover all material information on nature-related risks and opportunities that could be expected to affect an entity’s prospects. And two, it mandated that entities applying International Financial Reporting StandardsĚýS1 and S2 for climate-related disclosures supplement these with nature-related risks and opportunities disclosures as well.

Similar to the ISSB requirements to report material nature-related risks and opportunities, the ESRS also requires information to be disclosed for material impacts, risks, and opportunities found in an entity’s double-materiality assessment. The Task Force on Nature-related Financial Disclosures (TNFD) and its European counterparts have been in close collaboration since 2022, and all 14 TNFD recommendations have been incorporated throughout the ESRS environmental standards.

Companies that are required to comply with the EU’s sustainability reporting mandate also will be required to collect similar data for their future ESRS data points disclosure.

Alongside regulatory requirements, there are voluntary requirements and investor pressure to consider for many organizations. These include investor coordination initiatives on nature such as Nature Action 100 and considering which investors look at Carbon Disclosure Project (CDP) data.

To use the CDP as an example, 650 investors with $127 trillion in assets they needed in 2025. Further, the CDP is increasing its disclosure requirements for nature-related data in its questionnaire as it progresses to . This includes, for example, requiring disclosures on environmental impacts and dependencies for disclosers, enhancing commodities included in the forests questionnaire, and introducing oceans-related questions in 2026.

All of these heightened requirements underscore the need to measure a company’s nature-related impacts and proximity to its nature-related issues.

Implications for company boards

To align with these additional requirements and investor expectations, corporate decision-makers should consider the questions they are asking related to nature, as well as what data is being collected in relation to the organization’s impact on nature. The following steps can give leaders a starting point for how boards should consider this information:

Track relevant developments in regulatory and investor standards — Ensure there is a management-level understanding of how nature is considered in relevant standards for the company based on its current and anticipated locations of operation and specific industry.

Measure nature-related risks and opportunities — Given that identifying material nature-risks, with a particular focus on location specificity, is a common first step across current mandatory and voluntary regulatory frameworks, organizations should conduct a regularly updated, location-specific assessment on the company’s interface with nature, especially in instances in which these issues are material. Organizational leaders should also produce financial quantification of these risks within an overall materiality assessment and corporate risk register. For guidance, the best practice across these regulatory and disclosure frameworks is to utilize the .

Make further disclosure of any material nature-related information, including financial quantification — Frameworks such as the ESRS require further disclosure of any risks that are found to be material, including financial quantification and scale of the risk.

Integrate mitigation of nature-related risks in business strategies — Upcoming standards and research, such as that from the ISSB, indicates that missing disclosure includes company’s nature-positive transition planning. Consider how to integrate nature into long-term business strategies for full alignment with upcoming regulations and standards, including establishing nature-related governance.

Adopting these processes and integrating nature into corporate decision-making will provide corporations with a more future-proof and resilient business model. The increased adoption of nature within these frameworks is driven by the clear economic impact that our current loss of nature is having. This will only continue to become more of a priority as the impacts of nature loss are increasingly felt worldwide.

You can find out more about theĚýsustainability issues companies are facing around the environmentĚýhere

Key highlights:

• • • The governance-implementation gap is alarming — While nearly half of companies have AI strategies and 71% include ethical principles, a massive disconnect in execution persists.

    • AI governance is now a material investor risk — AI disclosure among S&P 500 companies jumped to 72% in 2025 from 12% in 2023, and investors are treating AI governance as a critical factor in overall corporate governance.

    • Regional disparities signal competitive risks — European, Middle Eastern, and African companies are leading in AI governance (driven by regulatory pressure), while only 38% of US companies have published AI policies despite being innovation leaders.

of 1,000 companies indicates a between the speed at which businesses are embracing AI and their preparedness to govern it effectively. These findings from , which offers a panoramic view across 13 sectors, are a wake-up call for every CEO, board member, and investor.

Indeed, nearly half (48%) of the companies sampled disclosed that they had AI strategies or guidelines in place, yet significant transparency gaps related to the environmental, social and governance (ESG) impacts of AI adoption remain.

When “ethical” principles lack substance

It is encouraging to see that 71% of companies with an AI strategy include principles around AI that include concepts such as ethical, safe, or trustworthy because this signals an awareness of the critical conversations happening around responsible AI. However, the AICDI data reveals a significant gap between stated principles and actual practice, more specifically:

• • • Environmental blind spots — A staggering 97% of companies failed to consider the environmental impact of their AI systems, such as energy consumption and carbon footprint, when making deployment decisions. As AI models grow in complexity and scale, their energy demands will only increase. In addition, investors are likely to adopt green AI as a non-negotiable concept in the future.

• • • Narrow social lens could open up reputational issues — More than two-thirds (68%) of companies with AI strategies did not adequately assess the broader societal implications of their AI technologies. Failure to understand and mitigate potential negative impacts on communities, vulnerable populations, or democratic processes is a recipe for reputational damage and legal challenges on the full spectrum of the human side of AI. Indeed, investors are growing more sophisticated in their understanding of these systemic risks.
    • Governance on paper and not in practiceĚý— While 76% of companies with an AI strategy reported management-level oversight, only 41% made their AI policies accessible to employees or required their acknowledgement. That means these policies are just words on paper if they are not understood, embraced, and actively practiced by those on the front lines of AI development and deployment. This gap in governance can lead to inconsistencies, unforeseen risks, and a fundamental breakdown in trust, both internally and externally.

Gaps in AI governance exist across regions and sectors

The AICDI data reveals fascinating regional and sectoral differences as well. For instance, companies in Europe, the Middle East, and Africa are generally ahead in publishing AI policies and establishing dedicated AI governance teams — action that is likely driven by the European Union’s looming AI Act. This highlights the proactive stance some regions are taking and offers a glimpse into what might become a global standard.

Despite the United States being a hub for AI innovation, only 38% of companies in the Americas published an AI policy. This discrepancy suggests a potential future competitive disadvantage for those lagging in governance.

Not surprisingly, sectors also varied in corporate oversight of AI initiatives. Financial, communication services, and information technology firms were more likely to have responsible AI teams than companies in energy and materials. This makes sense given their direct engagement with data and often consumer-facing AI applications, but it again points to a broader need for cross-sectoral AI governance best practices.

How companies can meet investor expectations

AI has rapidly become a mainstream enterprise risk. Fully 72% of S&P 500 companies disclosed at least one material AI risk in 2025, up from just 12% in 2023, according to the Harvard Law School Forum on Corporate Governance.

To attract and retain investor confidence, companies need to take concrete steps, including:

1. 1. 1. Conducting a comprehensive AI audit — Companies need a thorough understanding of where AI is currently deployed across their products, operations, and services. The AICDI offers a to help with this, which allows companies to evaluate current AI governance maturity and benchmark themselves against peers.
      2. Establishing robust, transparent, and accessible AI governance frameworks — Companies need to move beyond vague principles by developing clear, actionable policies that address environmental impact, societal implications, data privacy, fairness, and accountability. Critically, these policies must be accessible toĚýallĚýemployees, and their acknowledgement should be a requirement. Training and continuous education are paramount in order to embed these principles into daily operations.
      3. Proactively disclosing AI governance practices —ĚýCompanies should seek to anticipate investors’ concerns by incorporating specific disclosures on AI oversight mechanisms, transparency measures (including environmental and risk assessments), and how they’re preparing for evolving regulatory landscapes. Companies that showcase their commitment to responsible A as a strategic advantage will gain stakeholder trust.
      4. Embracing industry standards and collaboration —ĚýBy using global frameworks, such as the (which grounds the AICDI’s work), companies can strengthen standardization efforts. They should also participate in collaborative efforts and industry forums to share best practices and collectively raise the bar for responsible AI.
      5. Comparing your performance with peers —Companies can benchmark their responses against sector and regional peers. Also, they need to identify leaders and laggards to understand where a company stands and where it needs to improve. AI is an evolving field, and therefore, corporate AI governance frameworks must evolve as well — and the key ingredient for this is responsible innovation.

By any measure, AI is transforming our world; however, its benefits will only be fully realized if companies prioritize their responsible governance. For investors, AI governance is fast becoming a material risk and opportunity. And for companies, it’s no longer an option but rather a strategic imperative that can go a long way toward building trust, mitigating risks, and securing a sustainable future.

You can learn more about the , the corporate foundation of ¶¶ŇőłÉÄę, here

Key takeaways:

• • • Legacy data architectures can’t keep up with modern demands — Traditional, centralized data cores were designed for stable, predictable environments and are now bottlenecks under continuous regulatory change, rapid M&A, and AI-driven business needs.

    • AXTent aims to unify modern data principles for regulated enterprises — The modern AXTent framework integrates data mesh, data fabric, and composable architecture to create a data core built for distributed ownership, embedded governance, and adaptability.

    • A mindset shift is required for lasting success — Organizations must move from project-based data initiatives to perpetual data development, focusing on reusable data products and decision-aligned outcomes rather than one-off integrations or platform refreshes.

This article is the second in a 3-part blog series exploring how organizations can reset and empower their data core.

For more than a decade, enterprises have invested heavily in data modernization — new platforms, cloud migrations, analytics tools, and now AI. Yet, for many organizations, especially in regulated industries, the results remain underwhelming. Data integration is still slow because regulatory reporting still requires manual remediation, M&A still exposes hidden data liabilities, and AI initiatives struggle to move beyond pilots because trust and reuse in the underlying data remains fragile.

The problem is not effort, it is architecture. Since 2022, the buildup around AI has been something out of science fiction — self learning, easy to install, displace workers, autonomous, even Terminator-like. Moreover, while AI may indeed revolutionize research, processes, and profits, the fundamental challenge is not the advancing technology, rather it is the data used to train and cross-connect these exploding capabilities.

Most data cores in use today were designed for an earlier operating reality — one in which data was centralized, reporting cycles were predictable, and governance could be applied after the fact. That model breaks down under the modern pressures of continuous regulation, compressed deal timelines, ecosystem-based business models, and AI systems that consume data directly rather than waiting for curated outputs.

So, why is the AI hype not living up to the anticipated benefits? Why is the data that underpinned process systems for decades failing to scale across interconnected AI solutions? The solution requires not another platform refresh, but rather, a structural reset of the data core itself.

That reset uses data meshes, data fabrics, and modern composable architecture as a single, integrated system, and aligns it to the AXTent architectural framework, which is designed explicitly for regulated, data-intensive enterprises.

Why the traditional data core no longer holds

Legacy data cores were built to optimize control and consistency. Data flowed inward from operational systems into centralized repositories, where meaning, quality, and governance were imposed downstream. That approach assumed there were stable data producers, limited use cases, human-paced analytics, and periodic regulatory reporting.

Unfortunately, none of those assumptions hold today. Regulatory expectations now demand traceability, lineage, and auditability at all times (not just at quarter-end). M&A activity requires rapid integration without disrupting ongoing operations. And AI introduces probabilistic decision-making into environments built for deterministic reporting, with business leaders expecting insights in days, not months.

The result is a growing mismatch between how data is structured and how it is used. Centralized teams become bottlenecks, pipelines become brittle, and semantics drift. Compliance then becomes reactive, and the cost of change increases with every new initiative.

The AXTent framework starts from a different premise: The data core must be designed for continuous change, distributed ownership, and machine consumption from the outset. Indeed, AXTent is best understood not as a product or a platform, but as an architectural framework for reinventing the data core. It combines three design principles into a coherent operating model:

1. 1. 1. Data mesh — Domain-owned data products
      2. Data fabric — Policy- and metadata-driven connectivity
      3. Data foundry — Composable, evolvable data architecture

Individually, none of these ideas are new. What is different — and necessary — is treating them as a single system, rather than independent initiatives as conceptually illustrated below:

Fig. 1: The AXTent model of operation

The 3 operating principles of AXTent

Let’s look at each of these three design principles individually and how they interact with each other.

Data mesh: Reassigning accountability where it belongs

In regulated enterprises, data problems are rarely technical failures. Instead, they are accountability failures. When ownership of data meaning, quality, and timeliness sits far from the domain that produces it, errors propagate silently until they surface in regulatory filings, audit findings, or failed integrations.

A structured framework applies data mesh principles to address this directly. Data is treated as a product, owned by business-aligned domains that are then accountable for semantic clarity, quality thresholds, regulatory relevance, and consumer usability.

This is not decentralization without guardrails, however. AXTent enforces shared standards for interoperability, security, and governance, ensuring that domain autonomy does not fragment the enterprise. For executives, the benefit is practical: faster integration, fewer semantic disputes, and clearer accountability when things go wrong.

Data fabric: Embedding control without re-centralization

However, distributed ownership alone does not solve enterprise-scale problems. Without a unifying layer, decentralization simply recreates silos in new places.

A proper framework addresses this through a data fabric that operates as a control plane across the data estate. Rather than moving data into a single repository, the fabric connects data products through shared metadata, lineage, and policy enforcement.

This allows the organization to answer critical questions continuously, such as:

• • • Where did this data come from?
    • Who owns it?
    • How has it changed?
    • Who is allowed to use it — and for what purpose?

In this way, governance is no longer a downstream reporting activity; rather, it is embedded into how data is produced, shared, and consumed. Compliance becomes a property of the architecture, not a periodic remediation effort.

And in M&A scenarios, the fabric enables incremental integration, which allows acquired data domains to remain operational, while being progressively aligned rather than forcing immediate and costly consolidation.

Composable architecture: Designing for evolution, not stability

The third pillar of the AXTent model is a modern data architecture that’s designed to absorb change rather than resist it. Traditional architectures usually rely heavily on rigid pipelines and tightly coupled schemas. While these work when requirements are stable, but they may collapse under regulatory change, new analytics demands, or AI-driven consumption.

AXTent replaces pipeline-centric thinking with composable services, including event-driven ingestion and processing, API-first access patterns, versioned data contracts, and separation of storage, computation, and governance.

This approach supports both human analytics and machine users, including AI agents that require direct, trusted access to data. The result is a data core that evolves without constant re-engineering, which is critical for organizations operating under continuous regulatory scrutiny or frequent structural change. AXTent allows acquired entities to plug into the enterprise architecture as domains while preserving context and enabling progressive harmonization.

The architectural compass

This framework exists for one purpose: to provide a practical, business-oriented methodology for building a reusable, decision-aligned, compliance-ready data core. It is not a product nor a platform. It is a vocabulary that’s backed by building blocks, patterns, and repeatable workflows — and it’s one that executives can use to organize data around outcomes instead of systems.

Overall, the AXTent model prioritizes data clarity over system modernization, decision alignment over model sophistication, continuous compliance over intermittent remediation, reusable data products over disconnected pipelines, and enterprise knowledge codification over one-off integration work.

In essence, organizations should move away from project thinking and toward perpetual data development, in which every output contributes to a compound knowledge base. This is the mindset shift the industry has been missing as it prioritizes AI engineering over business purpose.

In the final post in this series, the author will explain how to shift from “build and operate” to “build and evolve” via a data foundry. You can find more blog postsĚýby this author here

Key highlights:

• • • Principles need a repeatable process —ĚýResponsible AI commitments become real only when companies systematize human rights due diligence to guide decisions from concept through deployment.

    • Policy and engineering teams should co-own safeguards — Ongoing collaboration between policy and technical teams can help translate ideals like fairness into concrete requirements, risk-based approaches, and other critical decisions.

    • Engage, anticipate, document, and improve continuously —ĚýInvolving impacted communities, running regular foresight exercises (such as scenario workshops), and building strong documentation and feedback loops make human rights accountability durable, instead of a one-time check-the-box exercise.

More and more companies are adopting responsible AI principles that promise fairness, transparency, and respect for human rights, but these commitments are difficult to put into practice when it comes to writing code and making product decisions.

, a human rights and responsible AI advisor at Article One Advisors, works with companies to help turn human rights commitments into concrete steps that are followed across the AI product lifecycle. He says that the key to bridging the gap between principles and practice is embedding human rights due diligence into the framework that guides product development from concept to deployment.

Operationalizing human rights

Human rights due diligence involves a structured process that begins with immersion in the process of building the product and identifying its potential use cases, whether it is an early concept, prototype, or an existing product. This is followed by an exercise to map the stakeholders who could be impacted by the product, along with the salient human rights risks associated with its use.

From there, the internal teams collectively create a human rights impact assessment, which examines any unintended consequences and potential misuse. They then test existing safeguards in design, development, and how and to whom the product is sold. “Typically, a new product will have many positive use cases,” explains Natour. “The purpose of a is to find the ways in which the product can be used or misused to cause harm.” In Natour’s experience, the outcome is rarely a simple go or no-go decision. Instead, the range of decisions often includes options such as go with safeguards or go but be prepared to pull back.

The use of human rights due diligence in the AI product lifecycle is relatively new (less than a decade old) and as Natour explains, there are five essential actions that can work together as a system:

1. Encourage collaboration between policy and engineering teams

Inside most companies, responsible AI is split between policy teams, which may own the principles, and the engineering teams, which own the systems that bring those principles to life. Working with companies, Natour brings these two functions together through a series of workshops to create structured, ongoing collaboration between human rights and responsible AI experts and the technical teams to better co-develop responsible AI requirements.

In the early stages of the collective teams’ work, the challenges of turning principles into practice emerge quickly. For example, the scale of applications and use cases for an AI product can make it difficult to zero in on those uses that . Not all products or use cases need to be treated equally, says Natour, and companies should identify those that could potentially cause the most harm. Indeed, these most-harmful uses may involve a “consequential decision” such as in the legal, employment, or criminal justice fields, he says, adding that those products should be selected for deeper due diligence.

2. Consider the principles at each stage of the development process

Broad principles and values, such as fairness and human rights, should be considered at each stage of the lifecycle. For the principle of fairness, for example, teams may assess which communities will use this product and who will be impacted by those use cases. Then, teams should consider whether these communities are represented on the design and development teams working on the product, and if not, they need to develop a plan for ensuring their input.

3. Engage with impacted communities and rightsholders

Natour advocates for companies to actively engage with impacted communities and stakeholders, including those who are potential users or who may be affected by the product’s use. This could be the company’s own employees, for example, especially if the company is developing productivity tools to use internally in their workplace. Special consideration should be given to vulnerable and marginalized groups whose human rights might be at greatest risk.

External experts, such as Natour and his colleagues, hold focus groups with such stakeholders as . The feedback from focus groups can then be used to influence model design, product development, as well as risk mitigation and remediation measures. “In the end, knowing how users and others are impacted by your products usually helps you make a better product,” he states.

4. Establish responsible foresight mechanisms

To prevent responsible AI from becoming a one-time check-the-box exercise, Natour says he uses responsible foresight workshops and other mechanisms as a “way to create space for developers to pause, identify, and consider potential risks, and collaborate on risk mitigations.”

The workshops use personas and hypothetical scenarios to help teams identify and prioritize risks, then design concrete mitigations with follow-on sessions to review progress. Another approach includes developing simple, structured question sets that push product teams to pause and think about harm. For example, Natour explains how one of his clients includes the question: What would a super villain do with this product? in order to help product teams identify and safeguard against potential misuse.

5. Create documentation and feedback loops for accountability

As expectations around assurance rise from regulators, customers, and civil society, strong documentation and meaningful, accessible transparency are essential, says Natour.ĚýClear, succinct, and accessible user-facing information about what a model does and does not do, about data privacy, and other key aspects can help users understand “what happens with their data, as well as the capabilities and the limitations of the tool they are using,” he adds.

Further, transparency should enable two-way communication, and companies should set up feedback loops to enable continuous improvement in the ways they seek to mitigate potential human rights risks.

The hardwired future

Effectively embedding human rights into the AI product lifecycle starts with a shared governance model between a company’s policy and engineering teams. Together they can collectively hardwire human rights into the way AI systems are imagined, built, and brought to market.

You can find more about human rights considerations around AI in our here

Key highlights:

• • • Treat AI use as a material sustainability driver — Bring AI explicitly into financial materiality and impact assessments so you can see where AI changes the scale or severity of existing issues or introduces new risks or opportunities.

    • Map, measure, and baseline AI demand to make it governable — Create an inventory of in which situations or how often AI is used and establish utilization metrics over time so you can spot growth, redundancy, and hotspots.

    • Control AI impact through policy, oversight, and supplier expectations — Set rules for appropriate AI use and triggers for extra review before scaling AI, and manage impacts whether AI is in-house or provided by vendors.

While AI clearly already is changing how companies operate and deliver, it’s also demanding changes in how sustainability systems are designed and governed. Indeed, much focus to date has been on the environmental impact of AI’s energy use, water consumption, and supply chain challenges.

Yet, there is another side to consider that involves examining how AI itself is used within organizations. It is important to understand where AI is applied throughout an organization, how often it is used, and whether those uses are necessary — and most crucially, how and when the review processes is applied. By including AI in materiality assessments, a clear track is set for its deployment, with systems in place to address any environmental and social impacts and risks that arise before they become problems.

To ensure effective value creation of AI use, organizational leaders need to focus beyond the footprint, by mapping their AI use, defining control and review processes, developing systems for ongoing quantification, and reporting transparently. The goal is to manage AI’s impact from the inside out, making sure the benefits are worth the risks and that sustainability remains a priority.

Bringing AI use into materiality and impact assessments

Financial materiality and impact assessments provide a practical basis for governing AI through the structured process of identifying and prioritizing significant impacts. Many sustainability topics influenced by AI use — including energy demand, emissions, water use, and workforce effects — are already assessed in existing materiality exercises. What is often missing is an explicit examination of how AI alters the drivers of those impacts.

The International Sustainability Standards Board’s centers on financial materiality, which is defined by whether a topic could reasonably be expected to influence the decisions of investors or other users of financial statements. How AI is used within companies undoubtedly influences the risks and opportunities the company faces and certainly can affect a company’s financial position.

Early closures aligned with the European Union’s Corporate Sustainability Reporting Directive (CSRD) suggest that AI is typically addressed within broader topics such as workforce impacts, digital governance, or business conduct rather than identified as a standalone source of dependencies, impacts, risks, and opportunities. This reflects the difficulty of assessing impacts that are indirect, cumulative, and demand-driven, and topics in which regulations and best practices are still evolving.

Bringing AI into materiality assessments requires assessing whether its use alters the scale or severity of existing impacts, introduces new risks and opportunities, or creates dependencies that warrant prioritization.

In practice, determination of the materiality of AI hinges on understanding scale and concentration — such as in which situations it is used or embedded in critical workflows and the scale of applications across functions, tools, and systems. Mapping AI use across use cases and delivery models can help provide the basis for determining in which instances AI meaningfully alters environmental, social, or financial exposure.

Once these priority areas are identified, organizations then can move from qualitative assessment to structured oversight by establishing a baseline for AI utilization and its associated potential impacts.

Governing AI demand through policy

Once a basis of materiality for AI is determined, the next governance step is to shift towards control, primarily through policy that’s supported by proportionate measurement of demand.

As access to AI expands, it can become a default tool for routine tasks, increasing demand through duplication and persistent use cases without sufficient oversight or challenge. Policies then can set expectations for appropriate application, conditions to assess depth relative to task value, and crucially, what conditions should trigger additional review before AI is scaled or embedded into core work processes.

Quantification underscores these policies by making AI use visible over time and by tracking its impact. For most organizations, the starting point for measuring AI impact is obtaining a consistent view of utilization and its evolution. This determination of scale will then later support the precise attribution of energy or emissions. Comparing precise indicators to utilization will enable leaders to establish a baseline and then support effective identification of growth, potential redundancy, and overall impact.

Managing AI’s impact

Where organizations own or operate their own AI infrastructure, management responsibility will sit within established operational controls, including decarbonization of electricity supply, managing cooling water use, and overseeing hardware lifecycles, such as refurbishment, reuse, and recycling. Governance also explicitly needs to cover model training and retraining, especially in areas in which concentrated energy and water demand can arise. In fact, it should be subject to planning and review rather than treated as a purely technical decision.

Where AI capability is accessed through external or third-party providers, these same impact areas must be addressed through policy and a rollout of supplier engagement practices that link disclosure with procurement decision-making. Management without direct control necessitates setting expectations and engaging external providers on energy sourcing, water stewardship, hardware management, and transparency around model training practices and associated impacts.

Governing AI as an impact on sustainability

AI’s sustainability effects depend on infrastructure efficiency, energy sources, and governance of its use in organizations. That means that effective management must include assessing material impacts, setting policies for demand and monitoring, measuring results, and making transparent reporting.

Treating AI as a source of managed sustainability can better help mitigate risks and ensure that the environmental and social effects of AI use are aligned with value creation.

You can find out more about here

Key insights:

• • • Old laundering patterns have modern wrappers— Nefarious actors now cooperate to move value through mirror-trade commodity flows and sometimes crypto, blending legal transactions with illicit proceeds.

    • FinTech expands laundering options— Peer-to-peer apps, reloadable cards, kiosks, and virtual assets allow for the execution of many small conversion transactions that break up funds and blur clean-to-dirty movement.

    • Fraud scales cheaply in an AI era— As cash use drops, scams and extortion become lower-risk and easier to industrialize — sometimes through forced-labor scam operations — making verification and policy adaptation urgent.

When incentives align, strangers can become business partners. In the 21st century, traditional finance, banking, and cash payments have been disrupted by a watershed of technological advances for which we are all unprepared. This time of crisis and opportunity has created an unexpected alliance between FinTech firms and traditional banking institutions.

To fight financial crime, however, it is important to deal with the ever-evolving ways for currency to change forms and change hands across vast distances. This new way of moving money mirrors ancient systems of debt ledgers & interpersonal trust, often known as Hawala or Fei Chien. Criminals continue to innovate with both methods, creating unsettling partnerships.

The cartel-business partnership

Cartels, underground banking networks, and legitimate businesses now collaborate — sometimes unwittingly — to launder money by moving value through mirror-trade commodity flows and cryptocurrency, merging legal trade with illegal profits. Near-cash-style FinTech methods — such as peer-to-peer apps, reloadable cards, kiosks, and virtual assets — can expand laundering opportunities by enabling numerous small conversion transactions that fragment funds and obscure the movement of illicit money. As cash use declines, fraud, including scams and extortion (sometimes executed through forced-labor scam operations) becomes less risky and easier to scale in the AI era, underscoring the urgent need for verification and policy adaptation.

The flow of illicit cash also extends to digital assets. Some of the cash money that gets stuffed into bitcoin ATM-style kiosks is from the drug trade. Indeed, the U.S. Treasury Department’s Financial Crimes Enforcement Network (FinCEN) issued an alert on this topic as well and, while the two schemes seem distinct, we can speculate that some of the resulting Bitcoin, crypto, or other virtual assets went to underground bankers facilitating a mirror trade for a countryman.

What is old is new again

In the world of finance, the dawning of a new era of digital, on-demand, borderless transactions provides access to an exciting frontier of possibility. New coins, new blockchain tokenization uses, and new FinTech tools with cool names are all rising and falling faster than the price of bitcoin.

The players in this intersection have figured out that trade is profitable, and legal trade leading to illicit substance trade is even more profitable. Underground shipping, sanctions evasion, and dark web services for money laundering are all profitable by themselves, and when combined, they represent an illicit economic blitzkrieg.

Cartels, underground banking networks, and legitimate businesses now collaborate — sometimes unwittingly — to launder money by moving value through mirror-trade commodity flows and cryptocurrency.

Crypto is the new Hawala or Fei Chien because, with no bank or government involved, people can keep common copies of a ledger instead of relying on a hawaladar or Chinese underground banker to keep records. Virtual assets could facilitate the currency side of mirror trades, refilling a person’s coffers via digital transfer which can then be moved to an exchange and on to a local bank.

Commodities are the new cash because mirror trades are physically settled in commodities. For example, investment in source chemicals for drugs, negotiated at a discount, helps expand the illicit cartel business. Similarly, one-off items can be used for large-cash replacement transactions.

FinTech is the new money service business (MSB). We know that they are regulated the same but often serve different market segments, and many now exchange government fiat currency for one or more forms of cryptocurrency. Money laundering thrives on breaking up funds into smaller amounts to avoid reporting; therefore, a multitude of near-cash options like peer-to-peer payment apps, reloadable cards, and virtual assets help the launderer with this problem.

One might imagine that lower-tier street dealers could have several peer-to-peer payment app accounts for ease of use, because although the criminal is running an illicit business, it’s a business, nonetheless. Industry experts call these small payments conversion transactions because they usually come from a clean, legitimate payroll source but are converted to dirty funds when spent on an illicit substance or activity.

Fraud is low risk and AI fuels the fire

In this rapid-fire digital transaction world, fraud is the new mugging, complete with racketeering and slave labor farms. The profit margin on physical intimidation has gone down because people use cash less often, and many seldom carry it at all.

Due to digital innovation, communication technology, and AI, however, the barrier to entry for fraudulent theft, extortion, or scamming has gone down dramatically as well. Presumably, the margins are high because the ability to fraudulently communicate has become exponentially enabled by these tech advances. Fraud and scams are ubiquitous to the point of impeding legitimate business from communicating with customers effectively.

The players in this intersection have figured out that trade is profitable, and legal trade leading to illicit substance trade is even more profitable.

Further, slave labor has reared its ugly head in yet another strange intersection among these many things. Fraudsters in Southeast Asia build warehouses filled with tech and then force local people to operate scams and fraud schemes at scale. Aggregated funds from these efforts are sometimes moved via commodity or artifact, but often these funds are gathered from kiosks or peer-to-peer apps and then moved through cryptocurrency transactions until they become increasingly arduous to track.

Looking to the new dawn

It seems every few minutes brings us a new tool, a new opportunity, a new way to move money, and a new way to get scammed out of it all. This expanding capability is fueled by GenAI and even more advanced forms of AI. Business expands, productivity expands, and resources are consumed faster. Fraud is enabled, scaled, and seems to hang in the very air.

With the proliferation of digital, borderless, and AI-enabled everything, the human touch is more important than ever. Business owners note that requests for memorabilia and other tokens of physical value continue to rise. Cash will not go away, but its share of transactions is already diminished with the advent of crypto, new intersections in commodity exchange, and other person-to-person ways to settle accounts.

For the financial institutions, government agencies, and fintech firms that populate this world, creating informed best-practices and sensible policy documents is critical at this phase of innovation. Without a proactive approach we cannot hope to stay ahead of criminals and keep legitimate markets secure.

You can find out more about how organizations are using new methods to detect and prevent financial fraud here

Key takeaways:

• • • Smart resource efficiency and decarbonization are good differentiators —Concrete gains in decarbonization, smarter resource efficiency, and rigorous human-rights due diligence increasingly distinguish market leaders from the rest.

    • Consideration for voluntary reporting is required — Companies should keep strengthening ESG data governance, involve finance and audit early, and consider voluntary alignment to maintain credibility with investors and supply‑chain partners regardless of legal thresholds.

    • Ongoing monitoring of regulation is critical — Legal uncertainty will continue, likely even up to the final decision. Companies should expect ongoing uncertainty and legal risk throughout the rest of this year.

The European Union’s effort to streamline its sustainability rulebook has entered a decisive stage. Through the Omnibus initiative, the European Commission aims to align and simplify overlapping environmental, social, and governance (ESG) regulations, particularly its Corporate Sustainability Reporting Directive (CSRD) and the Corporate Sustainability Due Diligence Directive (CSDDD). Framed as a push to enhance competitiveness, the Omnibus package reflects a broader recalibration that seeks to balance economic pragmatism with the EU’s sustainability ambition. The current goal is to finalize the legislative process by the end of 2025.

Over the past three years, the EU has assembled one of the world’s most far‑reaching reporting frameworks. CSRD seeks consistency and comparability in disclosures, while CSDDD extends human‑rights and environmental responsibilities across value chains. The Omnibus would narrow which companies report, reduce data points, and limit due‑diligence obligations mainly to tier‑one suppliers.

Proponents argue this will ease compliance and focus effort where it matters most. Critics fear that fewer reporters and fewer metrics could dilute accountability and the CSRD’s role as a global benchmark.

Debating the details

Decision‑making now shifts to the European Parliament and the Council, followed by a trilogue, in which the institutions converge on a compromise text. The Council has already staked out a position to raise the CSRD turnover threshold to €450 million from €50 million, which would significantly reduce the number of companies under its scope. Inside Parliament, center‑right groups prioritize deregulation and cost relief, while left‑leaning groups press to maintain or strengthen standards and comparability.

What happens next will determine scope and granularity. If thresholds rise and data points drop, complexity and audit costs decline, especially for smaller and midsize companies. Yet comparability could suffer if disclosures become thinner or less standardized.

The central question is whether simplification improves usability or merely softens obligations. Striking the right balance will shape the EU’s standing as a standard‑setter and the usefulness of ESG data for capital allocation, supply‑chain management, and regulatory oversight.

Reactions remain split. Business groups welcome burden relief and a narrower due‑diligence perimeter as pro‑competitiveness measures. Civil‑society organizations and some investors, on the other hand, warn that scaling back disclosures could undermine transparency, reduce comparability across sectors and borders, and weaken incentives for meaningful action on environmental and social issues. The debate underscores the persistent tension between short‑term economic pressures and long‑term sustainability objectives at the heart of the Omnibus process.

What companies should do now

For companies preparing for CSRD, the Omnibus adds uncertainty. While some smaller organizations may fall outside scope, larger enterprises must continue under a simplified regime. Practical steps include maintaining strong ESG data governance, engaging finance and audit teams early, and focusing on material topics that drive performance and risk management. Companies also should track institutional positions through 2025 and adjust their programs, targets, and controls as the final contours emerge.

Regardless of their position under the current or future framework, several strategic actions can help businesses stay prepared and maintain credibility with investors and regulators alike, including:

• • • Continue to strengthen sustainability data and governance — Even if the reporting scope narrows, robust ESG data management remains essential. Companies should ensure that internal processes, data systems, and oversight structures can deliver consistent and verifiable information. This will reduce compliance risks and position those companies well for any future expansion of requirements.
    • Consider voluntary alignment with simplified frameworks — Some firms potentially falling outside CSRD scope may still benefit from voluntary reporting under frameworks such as those for small and midsize entities (SMEs). This supports transparency with lenders, investors, and supply-chain partners that increasingly may expect sustainability disclosures, regardless of legal thresholds.
    • Focus on decarbonization and risk mitigation — Beyond reporting, tangible progress on decarbonization, resource efficiency, and human-rights due diligence remains a critical differentiator. Companies that integrate these areas into strategic risk management will be better equipped to respond to global sustainability standards and maintain market access in Europe.

The Omnibus represents more than a technical adjustment to EU sustainability rules. Indeed, it is a test of how effectively the bloc can balance economic pragmatism with ambitious climate and social objectives.

While the Omnibus may lead to political compromise, it does not fully close the door on legal risk. that certain proposed changes could conflict with EU principles of proportionality and the Charter of Fundamental Rights, particularly in the absence of comprehensive impact assessments.

For companies in Europe, the key takeaway is that even after legislative adoption, the regulatory landscape may continue to evolve, which will make ongoing monitoring essential.

You can find out more about the challenges corporations face with regulatory enforcement here

Key highlights:

• • • “Nothing says strategic priority like funding” — Ioannou’s most powerful insight is that directing capital through a sustainability lens is what enables companies to build lasting strategic resilience.

    • Progress typically involves tension — Acknowledging trade-offs across cost, timing, and stakeholder impact allows organizations to navigate complexity with greater clarity, reinforce internal alignment, and demonstrate that sustainability is being pursued through deliberate, not decorative, choices.

    • Preparing for difficult obstacles — The challenge is no longer whether sustainability matters, but what we are willing to do when it becomes inconvenient. Ioannou’s perspective challenges leaders to build resilient structures and processes that can withstand hostile terrain rather than fair-weather sustainability programs.Ěý

In recent years, Environment, Social & Governance (ESG) issues have shifted from a period of mainstream momentum to an era marked by skepticism and backlash. For Prof. Ioannis Ioannou of the London Business School, the question is no longer whether sustainability matters. “The real challenge,” he writes in , “is what we are willing to do when it becomes inconvenient to say so.”

Unlike some typical ESG toolkits that focus on messaging or compliance, this playbook calls for deeper strategic reflection. It is designed for leaders who remain committed, even as external validation fades.

Here are three actions from the playbook that can help organizations move from performative commitments to those initiatives that can have a more enduring impact.

Action #1: Treat capital allocation as the litmus test of strategic intent

“Nothing says strategic priority like funding,” says Prof. Ioannou, noting that capital allocation is where strategic commitment becomes visible. When sustainability priorities shape where capital flows — what gets funded, delayed, or redesigned — they move from rhetorical statements to structural choices.

This goes beyond simply adding ESG metrics to project evaluations. Indeed, sustainability must be embedded into the logic and architecture of investment decisions, Ioannou emphasizes. “It needs to be present from the start — at the first gate — not treated as a reputational check once everything else is locked in.” That includes integrating environmental and social criteria into how initiatives are assessed, which risks are priced in, and how long-term returns are understood.

“If ESG appears in reporting but doesn’t shape executive compensation, capital approvals, or promotion decisions, it’s a signal that the organization hasn’t yet internalized it,” Ioannou explains, adding that financial and non-financial outcomes should be tied together across both individual and institutional metrics.

For many organizations, this shift requires challenging a deeply ingrained capital allocation mindset. “We’ve trained generations of business leaders… to default to short-term financial returns,” Ioannou says. “That logic often crowds out longer-term investments in resilience, innovation, and systemic adaptation.” Overcoming this legacy of short-termism means rethinking how value is defined, especially under conditions of ecological, social, and geopolitical disruption.

Action #2: Make trade-offs visible and treat them as part of serious strategy

“Sustainability work that avoids trade-offs isn’t strategy — it’s storytelling,” says Ioannou. Indeed, a defining mark of credible ESG leadership is the willingness to address the inherent tensions involving costs, timelines, stakeholder impacts, and business models and to engage those conflicts directly, rather than trying to smooth them away.

Organizations frequently frame sustainability as universally beneficial. While that instinct may serve communications goals, it does little to strengthen strategic capacity. “Real progress almost always introduces tension,” Ioannou explains, adding that confronting these trade-offs should be made routine. “Leaders should ask: What shifts as a result of this decision? Who carries the burden? What timelines change, and what expectations must be reset?”

These answers could help bring clarity into operations by translating difficult decisions into language that invites accountability. “If a supplier shift increases costs by 8% but reduces water usage by 30%, that’s not a dilemma to hide. This is a strategic choice to make transparently,” he explains.

Organizations need to normalize this mindset through scenario planning, making ESG-informed business cases, and promoting cross-functional alignment, Ioannou recommends. When sustainability decisions live only in specialist teams, they remain abstract; but when they’re interrogated through operational, financial, and reputational lenses, these trade-offs become manageable.

“It’s easy to achieve consensus when the work stays abstract,” he adds. “The question is what happens when hard choices emerge, such as when costs surface, when values compete, and when speed slows down? Navigating these tensions openly is what makes sustainability real — it’s how leadership moves from messaging to meaning.”

Action #3: Distribute ownership and build governance depth across the business

“Resilience doesn’t come from the brilliance of one ESG leader — it comes from what remains when the spotlight moves on,” says Ioannou.

This means that boards of directors must develop the fluency to govern sustainability not as an adjacent risk, but as a core strategic focus. “Directors don’t need to master every metric, but they need to understand how climate, inequality, and systemic disruption affect the business over time,” he says, adding that boards need to treat ESG competence as a prerequisite for their directors in order to offer meaningful oversight. And this needs to be supported by tailored training, engagement with scenarios, and deepened dialogue around risk and resilience.

However, governance doesn’t stop at the boardroom. “Sustainability can’t thrive as a silo,” Ioannou explains. “It must be integrated into how the organization plans, executes, and adapts” This includes embedding ESG considerations into stakeholder engagement, procurement processes, product development, capital budgeting, and performance management.

Other key elements of this, he notes, is identifying internal champions and the importance of succession. “Look beyond the sustainability team. Who in finance, HR, or operations has the influence and insight to make sustainability actionable? …If the work vanishes the moment someone leaves, then it was never embedded. The question isn’t just what you’ve achieved — it’s what you’ve institutionalized,” he says.

As organizations seek to build governance structures that enable sustainability and continuity they also need to create lasting initiatives to support this strategy — such as ESG committees with cross-functional mandates, internal working groups linked to business planning cycles, and incentive systems that reward collaborative delivery — and foster the conditions under which the work can scale and endure.

“When the political noise fades, what matters is what you’ve built — structures, practices, and decisions that hold shape under pressure,” Ioannou concludes. “That’s the difference between performative ESG and resilient leadership.”

You can find more information in ourĚýSustainability Resource CenterĚýhere

Key highlights:

• • • Corporate governance has become more critical but for different reasons — The importance of corporate governance has increased significantly in 2025, driven by unexpected factors like AI adoption uncertainty, geopolitical complexity, and tariff impacts rather than just traditional ESG concerns.

    • ESG integration into core business strategy remains limited — The prediction that most companies would fully integrate ESG into their core business strategies proved overly optimistic, with only 21% of CFOs now saying their companies are working toward full integration.

    • Relying solely on non-profits and research institutions to solve the climate change problem is unrealistic — The fragmented and localized regulatory landscape necessitates increased resources from private capital and innovative business models. Large-scale impact requires building business models that can sustain and expand on these solutions.

The Thomson Reuters Institute made several predictions in early 2025 around sustainability. And while the corporate landscape in this space continues to evolve, significant adaptations in the way companies approach environmental, social, and governance (ESG) initiatives in 2025 were initially anticipated. Early outlooks suggested an increasing importance of corporate governance and that companies were narrowing the scope of their ESG activities and giving more prioritization to embedding sustainability into their corporate business strategies.

Yet, by mid-2025, certain forecasts have altered. While companies are still moving forward with their sustainability strategies, few are taking advantage of the opportunity to use sustainability as a strategic lens for competitive advantage. In addition, many are narrowing their material impacts, risks, and opportunities to only those that are core to their business strategy and operations. In other words, they are maximizing material opportunities and mitigating material risks even as their traditional governance responsibilities are unlikely to change.

What we got right

Prediction: Material risks, opportunities & impact endure while the term “ESG” fades

The acronym ESG was always a framework to identify corporate risks and opportunities; but unfortunately, the backlash to the term has forced companies to change the language used around their sustainability strategies. This was already underway at the beginning of 2025 and is still true now.

For example, many speakers at the recent , sponsored by Reuters Events, highlighted their success in moving sustainability strategies forward by focusing on the material issues that can be a way to future-proof financial success. Other key takeaways included spotlighting corporate actions that are now being taken and aligning these actions with the company’s purpose.

Prediction: Corporate governance more critical in 2025

This prediction about corporate governance increasing in importance remains factual, but the reasons are different than anticipated. Uncertainty around AI adoption, additional geopolitical complexity, and the impact of tariffs are the key factors driving the importance of corporate governance in mid-2025.

That said, however, other drivers are keeping corporate governance, in particular for corporate boards, elevated in importance as well. As Helle Bank Jorgensen, CEO of Competent Boards, : “As climate shocks intensify, artificial intelligence reshapes industries, regulations shift and stakeholder expectations evolve, directors face a new reality — that traditional oversight models are no longer sufficient.”

In addition, Jorgensen points out it’s expected by regulators, investors, and stakeholders that boards of directors will demonstrate fluency in climate and sustainability issues as they act as fiduciary stewards of companies’ strategies. She also cites more than 50 jurisdictions that have introduced requirements or expectations for directors to possess climate-related competence. This profound shift requires boards to take a much more aggressive, forward-looking orientation — one in which every operating assumption is questioned.

Prediction: Reverse of federal ESG-related regulations & rules accelerates

Six months into the year, the federal government’s efforts to roll back environmental tax credits as part of the Inflation Reduction Act from 2022 became a reality in the enactment of the One Big Beautiful Bill Act. Meanwhile, the U.S. Securities and Exchange Commission voted to in court in late March.

These actions show, as we predicted, that federal agencies are pulling back on ESG-related rules, especially around climate change. To fill this gap, pro-sustainability regulations and rules at the state and local levels may be needed. , aĚýformer White House climate advisor, told attendees of the recent RB USA conference that the real momentum and focus on climate needs to be on states, local governments, and communities as these local efforts are crucial and deserve international attention and investment.

Prediction: Growth in greenwashing litigation and industry collaboration continues

Our prediction about the growth in greenwashing litigation continuing into 2025 turned out to be accurate. Looking at greenwashing trends in 2025, we can see that the risk of greenwashing has never been higher because of increased complexity and the expansion of groups to include non-governmental organizations, employees, consumer class actions, and investors. Key areas under scrutiny include allegations of contaminants in consumer products, net zero statements, and forced labor in supply chains.

What we got wrong

Prediction: ESG integration into core business strategy would go mainstream

The prediction that the majority of companies would be fully integrating ESG into their core business strategy was a little aggressive, in retrospect. Indeed, omnibus proposals to simplify the European Union’s Corporate Sustainability Reporting Directive and the Corporate Sustainability Due Diligence Directive took shape in early 2025 and derailed the accuracy of this prediction. In fact, only 21% of CFOs now say their companies are working to , according to aĚýsurveyĚýconducted by accounting and advisory firm BDO. However, in that survey, ESG risk was cited among the top three concerns in financial planning with 45% of CFOs ranking it among their most pressing business risks.

In mid-2025, most companies may not be embracing ESG as a strategic lens to fundamentally transform the way they operate, despite our prediction. However, companies are developing the ability to anticipate and adapt their operational strategies to uncertain futures in response to AI and geopolitical and economic instability, and for many, climate change remains a major area of risk exposure.

To underscore that point, , CEO and co-founder of Voyacy Ventures, a blue-tech company that’s tackling the urgent global problem of coral reef ecosystem collapse and its severe consequences, told attendees of the recent RB USA conference: “It is unfair for us to expect that non-profits and research institutions can solve this problem [by themselves]. We need to develop a business model to develop this solution on a large scale.”

Cousteau’s comments ring true across the board on climate risk and other areas of sustainability risk. Private capital and large-scale corporate initiatives are necessary components for funding these solutions.

You can find more aboutĚýthe challenges around Sustainability here

Key insights:

• • Litigation risk is evolving — Greenwashing litigation is rapidly increasing and evolving worldwide, with different drivers and enforcement priorities across regions, including NGOs in the EU and consumer class actions in the US.

  • Compliance becoming more challenging — Companies, especially multinationals, face heightened legal risks and complexity due to the lack of global alignment on sustainability regulations and standards, making compliance more challenging.

  • Proactive legal consultation is essential — Early legal guidance and comprehensive training for corporate teams are essential to help businesses navigate this shifting landscape, ensure compliance, and mitigate the risk of costly greenwashing claims.

Navigating the nuanced world of corporate sustainability claims has become more treacherous in 2025, as businesses face a proliferation of greenwashing lawsuits driven by distinct regional pressures and shifting enforcement priorities.

Indeed, the greenwashing trends are multifaceted with changes across regions and industries, as well as the expansion of which parties are bringing litigation, according to legal experts from Morgan, Lewis & Bockius. For example, private consumer-based litigation remained robust in the United States, despite changes in federal enforcement priorities, according to , a Partner in International Transactions, Finance and Trade at the firm.

In the European Union, non-governmental organizations (NGOs) are driving litigation because of new EU regulations regarding green claims and consumer protection, according to , Partner & Co-Leader of the ESG & Sustainability Advisory Group at Morgan Lewis. “We see an increase in NGOs being very active because there is a pushback on sustainability as a whole, and the different governments around the world push now even harder in relation to greenwashing or even climate change litigation,” Apetz-Dreier explains.

In addition, greenwashing claims continue to proliferate across several key industries, according to , a Morgan Lewis litigation partner and his peers. Oil and gas, consumer products, and transportation sectors, in particular, are facing added scrutiny, Corrado adds.

Consumer class actions and NGOs filling void

In the US, litigation activity by class action attorneys, NGOs, and state attorneys general has picked up in 2025 during a time in which federal enforcement has pulled back, according to Valenstein. More specifically, Corrado says he continues to see NGO-driven consumer litigation and NGOs taking advantage of consumer protection laws that permit derivative claims on behalf of consumers to seek injunctive relief to stop false advertising practices. “Consumer class actions are targeting companies for the presence of contaminants like microplastics and PFAS” in a range of products from food packaging to apparel have proliferated, he adds.

In the area of supply chain integrity, Valenstein notes an increase in the US of civil litigation under the Victims of Trafficking and Violence Protection Act of 2000 to attack forced labor in the supply chain, which has led some companies to settle before such lawsuits are filed to sidestep negative publicity. In addition, there is more consumer protection class-action litigation challenging the reasonableness of reliance by companies on third-party supply chain audit firms, he says.

Further, companies are exercising caution in their diversity, equity & inclusion (DEI) public statements to avoid drawing negative attention. “I think that the DEI space is unique in terms of complexity,” Valenstein says. “Companies are worried about action from the federal government, but they’re also worried about litigation from stakeholders who feel disappointed that companies are walking back and no longer honoring commitments.”

Greenwashing claims around statements regarding net zero commitments is still a key area of litigation as well. For example, companies in the airlines industry are being targeted for “emission statements and carbon neutrality goals,” in which the legal theory is that “these statements induced consumers to… pay a premium” under false pretenses, Corrado explains.

Outside assistance necessary to manage legal risk

Multinational companies are particularly challenged by the varying regulations and legal risks across different countries because of the lack of global alignment around sustainability laws and standards. “The big challenge for multinationals is that in the past, there was an alignment globally by most administrations and governments,” says Apetz-Dreier. “But this is now changing. It is a big challenge to comply with the different national approaches and regulations.”

As companies seek to align their practices with new areas of legal risk exposure, Corrado, Valenstein, and Apetz-Dreier advise companies to consult proactively with legal counsel for guidance and training. For example, they suggest that legal counsel from several multidisciplinary areas review the company’s sustainability reports, website copy, product labels, and marketing messages before any product launch to better mitigate the murky multi-layered divergence in the legal landscape. Indeed, Valenstein describes the holistic approach he and his peers use with clients: Companies approaching outside counsel with the expertise in litigation, regulatory & compliance, and antitrust is a best practice, he explains.

They also recommend hiring external experts to conduct training sessions for corporate marketing department teams to better equip them with the necessary awareness and skills to craft messages that are both appealing to consumers and legally sound. By doing so, companies can balance their marketing strategies with compliance requirements and reduce the risk of facing costly legal disputes. Corrado describes how bringing in external experts to review “real world examples of claims and statements that seemed innocuous but led to litigation” helps to “recalibrate the marketing department’s view” on how their words can open up the company to risk exposure.

As the landscape of environmental claims continues to shift, companies must brace themselves for anticipated trends in both greenwashing legal claims and regulatory changes. “We are living in a very quickly evolving legal environment and it’s important for our clients to be prepared for the unknown because there is a lot of insecurity about what is going to happen,” says Apetz-Dreier. “We prepare our clients for the different opportunities and challenges — that’s a big part of our work.”

You can learn more about the concept of greenwashing here