Key highlights:

• • • The governance-implementation gap is alarming 鈥� 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 鈥� 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 鈥� 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 鈥� 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 鈥� 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 practice听鈥� 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 鈥� action that is likely driven by the European Union鈥檚 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 鈥� 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 鈥� 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 to听all听employees, 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 鈥�听Companies should seek to anticipate investors鈥� concerns by incorporating specific disclosures on AI oversight mechanisms, transparency measures (including environmental and risk assessments), and how they鈥檙e 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 鈥斕鼴y 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 鈥擟ompanies 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 鈥� 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 成人VR视频, here

Key highlights:

• • • Treat AI use as a material sustainability driver 鈥� 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 鈥� 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 鈥� 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鈥檚 also demanding changes in how sustainability systems are designed and governed. Indeed, much focus to date has been on the environmental impact of AI鈥檚 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 鈥� 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鈥檚 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 鈥� including energy demand, emissions, water use, and workforce effects 鈥� 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鈥檚 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鈥檚 financial position.

Early closures aligned with the European Union鈥檚 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 鈥� 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鈥檚 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鈥檚 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鈥檚 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:

• • • Elevate AI governance to the board 鈥� Companies should tie their AI deployment to enterprise risk management with explicit KPIs for energy intensity, water withdrawals and consumption, and supply鈥慶hain human rights.

    • Make transparency a competitive asset 鈥� Implement auditable disclosures on AI workload footprints, water stewardship, and supplier traceability, and then link executive compensation and vendor contracts to measurable efficiency and resiliency outcomes.

    • Demand transparency despite practical challenges 鈥� Although demanding transparency from suppliers may not be practical now due to current challenges, collectively asking for detailed information sends a notable requirement to AI infrastructure providers that the company is seeking to drive change and preserve trust in an AI-driven economy.

AI now sits at the center of corporate sustainability governance as it supercharges data gathering, analytics, and reporting. Indeed, there is is areas of energy optimization, emissions monitoring, land鈥憉se assessment, and climate scenario analysis.

At the same time, AI’s rise is colliding with sharply growing electricity and water demands from data centers and concerns over geopolitically exposed supply chains. The governance challenge for companies therefore is to manage risk at this intersection. This means treating AI as a capital鈥慽ntensive, cross鈥慴order infrastructure program whose environmental footprint and supply dependencies must be actively governed.

Why electricity and water are now board鈥憀evel AI risks

AI has turned electricity and water from background utilities into constraints that should be dealt with on the board level. Indeed, AI magnifies water risk across cooling, power generation, and chip manufacturing. This makes sourcing and efficiency choices strategic imperatives for many organizations.

Electricity demand 鈥� AI use and the data centers that power the tools already account for a significant and rising share of electricity use in the United States. The finds , a figure poised to grow as AI workloads scale. Forward鈥憀ooking projections from the U.S. Department of Energy indicate that by 2028 could be attributed to AI workloads.

If you translate those projections into , you can get an idea of the potential magnitude of the problem. Together, these sources suggest that the fastest鈥慻rowing part of AI’s energy appetite is not just for training models, but the steady, pervasive inference capabilities听required to power AI features in everyday products and operations.

Direct and indirect water use 鈥� Data centers powering AI also negatively impact local water footprints. It shows up in three places: i) data鈥慶enter cooling; ii) the electricity feeding those facilities, including thermoelectric and hydroelectric generation; and iii) AI’s own hardware supply chain. In regions already facing scarcity, these demands compound local stress. For example, the average per capita water withdrawal is 132 gallons per day; yet a large data center consumes water .

This makes data centers one of the听 in the country, which incidentally is home to . At the end of 2021, around听 from moderately to highly stressed watersheds in the western US. This is a common situation as well.

Geopolitical exposure 鈥� The hardware that powers AI includes advanced logic and memory chips, which depend on concentrated manufacturing nodes and supply chains with access to critical minerals. Extraction and processing of inputs, such as lithium and cobalt, are often clustered in jurisdictions with elevated levels of human鈥憆ights, environmental, or geopolitical risk. This potential amplifies exposure to export controls, sanctions, or resource nationalism, especially directly for companies鈥� supply chains and indirectly for those companies using AI.

Companies need to ensure their communication on legal and policy issues are pointing in the same direction in regard to these concerns. Indeed, companies need to deepen value鈥慶hain due diligence while navigating evolving supply鈥慶hain and AI鈥憇pecific regulatory regimes.

Recommended actions for companies

These intersections have clear implications for corporate governance. AI’s promise to accelerate decarbonization, improve transparency, and strengthen decision鈥憁aking will be realized only if leaders can properly manage the physical, political, and social realities underpinning the technology. Recommended actions to manage risk in areas in which AI and geopolitics converge include:

Demand transparency in electricity and water consumption of AI infrastructure 鈥� Companies building AI infrastructure need to conduct AI workload planning. Companies using AI can demand transparency of their suppliers鈥� 24- to 36-month forecast of training and inference by region with overlays in grid carbon and local water stress to better understand their indirect environmental impacts.

De鈥憆isk impact by incentivizing clarity in supply chains 鈥� Companies using AI can begin asking AI infrastructure companies to provide due diligence in tier 2, 3, and 4 suppliers, all the way down to smelters, refiners, and miners to make sure that companies are not indirectly contributing to environmental and social harms.

The bottom line

While these recommendations generally align with evolving corporate practices in sustainability and risk management, the challenge of implementation will vary based on the company’s size, influence over suppliers, and existing governance structures. The most challenging aspect will likely be achieving transparency and clarity in supply chains, which requires cooperation from suppliers and the investment of potentially significant resources.

At the same time, however, if more companies collectively ask for this level of detailed information from their AI infrastructure providers, it will send a notable demand signal. Indeed, AI is both a sustainability tool and a sustainability liability, but its benefits will be realized only if leaders confront the physical and geopolitical constraints that make AI possible.

Those companies that begin asking for this level of transparency can preserve the trust that underwrites their license to navigate successfully in an AI鈥慸riven economy.

You can find out more on the sustainability issues companies are facing around the environment here

Key takeaways:

• • • Smart resource efficiency and decarbonization are good differentiators 鈥擟oncrete 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 鈥� Companies should keep strengthening ESG data governance, involve finance and audit early, and consider voluntary alignment to maintain credibility with investors and supply鈥慶hain partners regardless of legal thresholds.

    • Ongoing monitoring of regulation is critical 鈥� 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鈥憆eaching reporting frameworks. CSRD seeks consistency and comparability in disclosures, while CSDDD extends human鈥憆ights and environmental responsibilities across value chains. The Omnibus would narrow which companies report, reduce data points, and limit due鈥慸iligence obligations mainly to tier鈥憃ne 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鈥憁aking 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 鈧�450 million from 鈧�50 million, which would significantly reduce the number of companies under its scope. Inside Parliament, center鈥憆ight groups prioritize deregulation and cost relief, while left鈥憀eaning 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鈥憇etter and the usefulness of ESG data for capital allocation, supply鈥慶hain management, and regulatory oversight.

Reactions remain split. Business groups welcome burden relief and a narrower due鈥慸iligence perimeter as pro鈥慶ompetitiveness measures. Civil鈥憇ociety 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鈥憈erm economic pressures and long鈥憈erm 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 鈥� 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 鈥� 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 鈥� 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 insights:

• • • Traditional board oversight models are outdated 鈥� Amid multiple crises threats, corporate boards that still rely on legacy governance approaches risk falling behind as today鈥檚 interconnected crises demand proactive and adaptive oversight.

    • Questioning assumptions about growth 鈥� Boards must continually challenge their assumptions about growth and risk utilizing four key strategies, including red team exercises, translating trends into strategic trade-offs, embedding sustainability anticipation, and linking culture with capital.

    • Sustainability is a central filter for all board decisions 鈥� Boards that proactively integrate sustainability into their culture, risk management, and strategic planning are better positioned to thrive amid regulatory pressures, climate risks, and stakeholder expectations in a volatile global environment.

Last year, corporate boards demonstrated greater readiness to address sustainability issues with significant financial implications, especially compared to their preparedness in 2018, according to the . For example, Environment, Social & Governance (ESG) board committees among Fortune 100听companies increased to 89 in 2024, compared to 22 in 2018.

At the same time, it is hard to know if this progress is adequate. As climate shocks become more severe, AI transforms industries, and stakeholder expectations evolve, corporate boards of directors are encountering a dynamic business environment that contains multilayered risks.

Boards operating in the traditional oversight models may soon find themselves struggling as the governance tactics of the past prove inadequate in the face of these newer changes.听Furthermore, the future operating environment for companies is becoming increasingly complex, with a heightened risk of polycrises, in which multiple, interconnected crises converge to create unprecedented challenges.

Moreover, boards of directors as fiduciary stewards of companies鈥� strategies are now expected, by regulators, investors, and stakeholders, to demonstrate fluency in climate and sustainability issues. In fact, more than 50 jurisdictions 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 in which every operating assumption is questioned.

In this context, sustainability is no longer a peripheral concern, but rather a central filter through which every decision must pass, as companies must navigate the intricate relationships between environmental, social, and economic factors to ensure long-term resilience and success. This reality means that boards must take proactive and integrated approaches to effective governance and oversight. Indeed, those that prioritize sustainability, risk management, and strategic adaptability are more likely to thrive in a world characterized by uncertainty, interdependence, and accelerating change.

Embracing re-evaluation strategies

To meet these new expectations in an ever-changing business landscape fraught with multi-faceted risks, boards must also question their assumptions about growth and the lens through which they are examining systemic risks. A board also needs to understand where it is prioritizing short-term wins at the expense of long-term viability.

These four key strategies can help directors prompt a critical re-evaluation of their growth assumptions and framework they use for assessing systemic risks 鈥� they can also help directors determine whether the board is prioritizing short-term gains over long-term sustainability:

1. Execute 鈥渞ed team鈥� exercises

Boards often find themselves surrounded by confirmation bias because they rely on trusted advisors and management teams who often share familiar viewpoints. This environment can stifle innovation and obscure systemic risks. A red team exercise can help break this cycle by inviting a diverse group of external experts and internal challengers to pressure-test assumptions about growth, systemic risks, supply resilience, reputation, and the company鈥檚 license to operate. Such exercises encourage directors to confront uncomfortable truths and explore alternative scenarios.

Too many organizations still operate as if ESG and value-creation are in conflict when, in fact, they are not. Running red team exercises in the board room can better align their strategies with sustainable goals to better spur innovation while maintaining operational resilience as priorities.

2. Translate trends into strategic trade-offs

Boards must be adept at discerning emerging trends to better inform the difficult strategic听decisions about what to pursue and what to forego. Asking tough questions that frame trends as choices is an effective mechanism to analyze trade-offs. For example, 鈥淒o we invest in short-term returns with high-carbon lock-in, or reallocate capital toward regenerative business models that preserve long-term viability?鈥� is a common trade-off question that many companies across industries are asking. By engaging in debates about real dilemmas rather than passive updates, directors can make informed decisions that balance immediate gains with future sustainability.

3. Build 鈥渟ustainability anticipation鈥� into board culture

To lead effectively in an uncertain future, boards must build sustainability foresight into their culture. An effective means of doing so is embedding sustainability anticipation into every board committee鈥檚 mandate. Tools such as dynamic scenario planning, transition-readiness metrics, and real-time materiality assessments that address emerging risks can help boards to anticipate and adapt to future challenges.

4. Link culture and capital

Most companies view sustainability as just a function rather than a filter for every business decision. This is why linking culture and capital at the board level is an essential step in making boards genuine hubs of foresight. Indeed, pulse surveys, stakeholder feedback, and behavioral data are necessary sources boards can use to make sure that sustainability is a foundational principle across all business decisions and used as a lens for value creation.

Looking ahead

The time for passive governance is over. By adopting these strategies, boards can navigate the complexities of today’s business environment for long-term viability for tomorrow. As the risks of interconnected crises 鈥� polycrises 鈥� intensify, making sustainability a fundamental criterion for every business choice is crucial for companies and can provide a profitable operating path in the years to come.

You can find out more about how companies are addressing the challenge of sustainability here

This , which came out February 12, reflects a return to a more traditional approach regarding the shareholder proposal process. In addition, it is also a shift back to previous principles-based rulemaking that focus primarily on financial materiality, according to , Special Counsel at Sullivan & Cromwell. The guidance also signals a potential reversal of previous rules passed in 2021, which had allowed stockholder proposals with 鈥渂road societal impact.鈥�

This move by the SEC has important implications for shareholder proposals that raise social and ethical issues because a company could choose to exclude a proposal based on economic relevance of the stockholders who are supporting a proposal, according to Hu. Indeed, this new guidance essentially reverses the SEC鈥檚 2021 action that allowed proposals touching on “broad societal significance” to bypass the ordinary business exclusion.

The 2021 action allowed such shareholder proposals to go forward based on two considerations: i) whether the proposal addresses issues essential to the management’s daily operation of the company and which makes it impractical for shareholders to directly oversee these matters; or ii) whether the proposal excessively controls or interferes with the company’s management processes.

Impact of this new SEC guidance on ESG

The effect of the ordinary business exclusion and the evaluation of shareholders鈥� economic relevance is expected to lead to , including those related to environmental, social & governance (ESG) and anti-ESG issues, according to analysis by Sullivan & Cromwell. In particular, the ordinary business exclusion emphasizes the need for a company-specific materiality analysis when determining whether shareholder proposals can be excluded from proxy materials. As a result, this shift is widely expected to make it easier for companies to exclude shareholder proposals from their proxy statements, particularly those related to ESG and political policies.

In addition, the return to principles-based and mandated reporting on financially material matters has two important implications for companies and their ESG reporting:

Strengthened separation of financial and sustainability data 鈥� This SEC guidance strengthens the likelihood that financial material information will be the sole focus of SEC filings and that non-financially significant information, like specific ESG disclosures, might be relocated to sustainability reports rather than being included in SEC filings, according to Hu. This distinction could help streamline SEC documents and makes sure that they remain focused on financial data relevant to investors.

Distinction between financial and sustainability reporting timelines 鈥� Before this new guidance, some companies were moving toward the simultaneous release of their annual financial reports and sustainability reports. This was a challenge for companies because 鈥渢he reliance on third-party data, especially for Scope 3 emissions, presents hurdles in timely and accurate reporting,鈥� Hu states. However, the focus now on principles-based reporting of financially impactful information ensures that the timelines are likely to remain different.

What should companies do now?

To navigate this murky environment, Hu advises companies to seek legal counsel to ensure compliance and strategic alignment with the evolving regulatory environment. In addition, companies should:

Monitor legal requirements 鈥� Make sure legal requirements are the foundation for their disclosures and decision-making processes. A company-specific materiality assessment is crucial in determining what issues are financially material and significant to the company’s business model, and thus, to shareholders.

Focus on principles in disclosures 鈥� Ensure that all filings align with the SEC鈥檚 principles-based approach to disclosure. Companies should focus only on financial information in their SEC filings and reserve other information for sustainability reports or other documents that cater to a wider stakeholder base.

Balance risks and benefits regarding what information to include in SEC filings 鈥� Hu also recommends that companies should conduct a cost-benefit analysis of disclosure placement and consistency. This means considering the potential risks and benefits of including certain information in their SEC filings rather than in other reports. By taking a thoughtful and company-specific approach to disclosure, companies can navigate the evolving regulatory landscape and make strategic decisions that align with their mission and the expectations of their stakeholders.

Caution is warranted on the horizon

Once fully staffed, the SEC will likely consider changes to existing rules around shareholder engagement. Likewise, Hu said she also expects the SEC to scrutinize those actions recommended by proxy advisors as signals for the proxy season’s voting patterns, particularly on proposals related to diversity, equity, and inclusion (DEI), especially as some companies narrow their activities in this area. However, it is a little early to know the impacts, she adds.

Either way, companies must proceed with caution and strike a balance between investor expectations and regulatory requirements, while primarily focusing on issues that are significant to their specific business model and bottom line.

You can find out more about how the Securities and Exchange Commission is managing the current regulatory environment here