October 2026 Newsletter

Executive Pay | 94% Of Senior Executives See Rising Career Mobility 

As India’s senior leadership landscape undergoes a rapid change, 94 per cent of executives are open to explore new career opportunities, driven by aspirations for faster career progression, a report said on Wednesday.

According to Page Executive India, part of Michael Page India, Executive Compensation and Talent Trends Report, India’s senior leadership landscape is becoming increasingly dynamic.

Executives are seeking faster career progression, broader responsibilities and greater opportunities for impact, even as organisations face growing pressure to keep pace, said the report based on a survey of over 1,200 C-level executives and senior leaders in the country.

The study by Page Executive India, an executive search and senior leadership recruitment company, showed that 94 per cent of senior leaders are open to new opportunities, while 63 per cent are actively looking for a new role and 41 per cent do not expect to remain in their current position for more than a year.

“India’s executive market is not constrained by a lack of talent, but by the pace at which organisations can develop and retain it. Senior leaders today are looking for faster progression, greater impact, and clearer alignment between performance and reward,” Page Executive India Managing Partner Jon Goldstein said.

AI is increasingly becoming integral to senior leadership roles in India, with 75 per cent of executives actively using the technology in their work, while an equal proportion reported higher productivity and 72 per cent saw an improvement in the quality of work, the report said.

“From an executive perspective, the conversation around AI is moving from adoption to accountability. As roles evolve and new skills become important, the ability to identify, develop and retain the talent that can work effectively alongside AI will become an increasingly important leadership priority,” Nilay Khandelwal, Senior Managing Director, Michael Page India and Singapore added.

Regarding compensation, 65 per cent of executives said they are satisfied with their remuneration while 87 per cent cited pay as a key driver of job satisfaction.

However, the survey found that mobility remains extremely high, and compensation alone is no longer enough to retain senior leadership, who are increasingly prioritising career progression and promotion opportunities.

Skills-based hiring is gaining ground in India, with organisations increasingly recognising its potential to widen access to talent and reduce bias, although its implementation remains uneven, it said. Read More.

AI in Governance | The Boardroom’s AI Governance Blind Spot 

Engineers have long relied on failure mode and effects analysis, or FMEA, to anticipate how complex systems can break down before they fail in the field. The method is useful because it does not treat risk as a single judgment. It separates the assessment of each possible failure mode into three questions:

How severe would the failure be?

How likely is it to occur?

How difficult would it be to detect before harm is done?

Corporate boards are coming up the learning curve on potential failure modes associated with AI. They understand AI applications across the business could lead to heightened data privacy issues, cybersecurity threats, intellectual property leakage, customer satisfaction concerns, regulatory risks and more. Directors are also starting to ask the first two types of questions called for by FMEA — on severity and occurrence. They ask how damaging these failure modes could be and how likely they are to happen. But most boards have not yet developed the same discipline or instinct around the third FMEA inspection vector: detection difficulty. In the context of AI governance, especially where AI intersects with human capital, this may be the most consequential question directors are not asking.

The resulting governance gap is real. ISS-Corporate reported in 2026 that only 22% of S&P 500 companies disclosed board oversight of AI and only 24% disclosed AI frameworks and policies. At the same time, The Conference Board reported that the share of S&P 500 companies disclosing AI as a risk jumped from 12% in 2023 to 83% by the end of 2025. In other words, companies are increasingly telling investors that AI matters, but few are showing how their boards are overseeing it.

That gap is not surprising given the speed and breadth of the technological transformation underway. At a recent NACD Master Class on Technology and Innovation Oversight, Bobby Mehta captured the scale of the moment facing corporate directors. Mehta, former president and CEO of TransUnion and a public company director who serves on the boards of The Allstate Corporation and Northern Trust Corporation, as well as nonexecutive chair of JLL, observed that the AI era is like “the Industrial Revolution, the Information Revolution and the Renaissance all happening at once.” AI is changing work, knowledge, productivity, competition and risk at the same time. Oversight must account not only for what AI can do, but also for how quietly it can fail.

That is what makes detection difficulty so important. A plant shutdown, a ransomware attack or a failed product launch announces itself. By contrast, a biased recruiting screen, an AI-assisted performance management system that disadvantages certain employees or unauthorized use of customer data in a public AI tool may persist for months without setting off a conventional alarm. Despite these lurking challenges, management dashboards may still show faster cycle times, lower costs and higher productivity. The organization may believe it has improved performance when, in reality, it has embedded a harder-to-see risk.

Consider shadow AI: Not long ago, bypassing corporate technology controls often required technical knowledge or privileged access. Today, an employee under pressure can paste confidential information into a public AI tool from a browser or a personal device. The employee may be trying to be productive, not malicious. But from a board oversight perspective, intent does not eliminate exposure. If the company cannot see which AI tools are being used, what data is being entered, whether outputs are being relied on and whether policies are being followed, its risk profile is fundamentally incomplete.

This concern is no longer a hypothetical one. In May 2026, CB Financial Services filed an Item 1.05 Form 8-K after its subsidiary, Community Bank, became aware of an internal incident involving nonpublic customer information ingested by an unauthorized AI-based software application. The company determined the incident was material because of the volume and sensitive nature of the information involved. For directors, the lesson is not merely that AI can create cybersecurity risk. The deeper lesson is that unauthorized AI use can become a disclosure issue, a customer trust issue, a regulatory issue and a board oversight issue before the board has a fully developed mechanism to detect it. In FMEA terms, when a serious issue is surfaced only after sensitive information has already moved into an unauthorized tool, the detection architecture deserves close scrutiny.

The human capital implications are just as significant. According to John Bremen, senior managing director, chief innovation and acceleration officer, and board advisor at WTW, “the failure risks of human capital processes can rival those of technology and data systems, so diligence in detection is essential.” Employers are using automated tools for hiring and promotion decisions, performance management, productivity monitoring, learning and development, workforce planning and employee engagement. In FMEA terms, the severity of risks associated with any of these systems could be extremely high, because the consequences of failure may include discrimination claims, regulatory scrutiny, reputational harm, and loss of trust with employees and candidates. Occurrence may be moderate or high because these tools are becoming embedded in ordinary HR processes. But detection difficulty may be the variable that determines whether the risk is manageable or dangerously explosive.

A board applying an FMEA lens would ask management to identify AI failure modes explicitly. For each failure mode, management should be able to explain not only the severity and likelihood of occurrence, but also how the company would detect the problem early. What data would reveal the issue? Who reviews it? How often? What thresholds trigger escalation? Can the company audit vendors’ models or only the outputs of their algorithms? Are rejected applicants, employees or managers given a meaningful channel to challenge AI-influenced decisions?

Failure mode	Detection question	Relevant controls
An AI recruiting tool produces a biased candidate slate.	How would management know before a rejected applicant, regulator or plaintiff’s lawyer raises the issue?	Disparate impact testing, review of selection rates, periodic third-party audits, documented human review and appeal channels for candidates.
An employee uses a public AI tool to summarize confidential employee relations files or customer information.	Can the company see sensitive data flowing into unapproved AI tools?	Data loss prevention tagging, endpoint visibility, access controls, employee training, exception reporting and monitoring of unapproved AI usage.
A performance management algorithm amplifies historical bias.	Does the company test outcomes over time across roles, managers, demographic groups and business units?	Longitudinal drift analysis, human review of outlier decisions, adverse impact analysis, and reporting of overrides and exceptions.
An AI agent takes action in a workflow without appropriate authorization.	What prevents the agent from exceeding its approved authority, and who is alerted when it does?	Permission limits, transaction caps, audit logs, segregation of duties, kill switches and escalation thresholds.

What is made clear by just these few examples is where conventional AI policies are falling short. A policy that says employees may not enter confidential information into public AI tools is necessary, but it is not a control by itself. An annual compliance certification is useful, but it is not continuous visibility. A vendor representation that their tool has been tested is helpful, but it is not independent assurance. And “human in the loop” is not a control unless the human has the time, authority, training and information needed to detect and override a flawed recommendation.

This does not mean boards should slow AI adoption to a crawl. The governance challenge is to help management move with both urgency and discipline. As Mehta puts is, boards need to take “a considered risk/return approach and balance the risks of moving at pace versus the risks of being too slow,” a task that will require “a higher than typical level of engagement and collaboration with management.” For directors, that balance depends on timely evidence: what tools are being used, where sensitive data is flowing, how AI-enabled decisions are being tested and when exceptions are escalated. Without that visibility, boards are left choosing between speed and safety without enough information to know whether either is being achieved. 

Higher engagement, however, should not be confused with technical immersion. Directors do not need to become machine learning engineers, but they do need to insist that AI governance includes detection architecture. That means inventories of approved and unapproved AI use cases; logging and monitoring of sensitive data flows; periodic bias and drift testing; vendor audit rights; employee reporting channels; documented human review; exception reporting; and board-level visibility into incidents, near misses, overrides and unresolved limitations. The board should also ask whether management’s AI controls extend beyond enterprise-approved tools to the real behaviors of employees, managers, contractors and vendors.

FMEA’s enduring value is that it does not let optimism masquerade as control. A risk that is severe and likely to occur is serious. A risk that is severe, likely to occur and hard to detect is a governance crisis waiting to surface. As AI becomes embedded in human capital decisions and everyday corporate workflows, directors should move beyond asking whether management has an AI policy or a list of approved tools. The more consequential question is whether the organization has the visibility, evidence and escalation pathways to know when AI is producing outcomes the board would not be willing to defend — before someone outside the company tells them so. Read More.

Board Composition | Reevaluating Board Composition 

In an era marked by volatility, uncertainty, complexity, and ambiguity (VUCA), the effectiveness of a corporate board depends not only on the technical depth of its members but on the breadth of their strategic and leadership capabilities. This short article argues for a recalibration of how boards are composed, particularly in global corporations. It contends that the trend of appointing domain-specific experts to the board —a model likened here to a “Noah’s Ark” of paired expertise — is increasingly ineffective. Instead, the most resilient and high-functioning boards are those led by generalist leaders: former CEOs, senior executives, and operational general managers with a track record of strategic oversight and people leadership. I propose a hybrid model that favors generalist board composition, supplemented by specialist consultants as needed, thus maintaining the board’s strategic integrity while ensuring subject matter rigor.

1. Introduction: The “Noah’s Ark” problem in boardrooms

Across many global boardrooms today, a familiar pattern has taken hold — a structure that mirrors the biblical Noah’s Ark. For every critical domain, boards are stacking two-by-two: two cybersecurity experts, two marketing authorities, two finance veterans, two talent gurus, et cetera. The intent is risk mitigation and representation, ensuring every discipline has a voice. Yet this Noah’s Ark strategy, while symbolically complete, is strategically flawed.

Rather than charting a bold course, these boards often resemble floating zoos of expertise, where directors are isolated by often outdated specialty and overly deferential to their functional peers. As each pair narrows its focus to its specific discipline, the board risks losing the cross-functional integration and strategic oversight essential to corporate governance. This leads to fragmented accountability, outdated expertise, and authority bias – quite often to the advantage and/or burden of/on the chairperson.

Sydney (2020) critiques this model explicitly: “Generalists — rather than specialists—make for great board directors… to be better prepared to govern in times of uncertainty.” The problem is not that specialists lack value; it’s that the permanence of their seat on the board can create intellectual silos and stagnation.

The academic literature supports this observation. Nili and Shapira (2024) note in the Yale Journal on Regulation that appointing specialists may in fact reduce the diversity and quality of strategic debate. “Authority bias leads to suppression of diverse viewpoints,” they argue, “particularly when the specialist has been recruited under the premise of exclusivity of knowledge.”

The alternative is to rethink the Ark: not as a static collection of experts, but as a vessel guided by navigators — generalist leaders who can synthesise, question, and direct. These are individuals who have operated companies, not just departments. Who have balanced growth and risk, not just analysed it. Who bring perspective, not just credentials.

In this paper, I argue that the future of corporate governance lies not in Noah’s Ark duplication of expertise, but in empowering generalist captains who can integrate functional insights and steer with strategic clarity. Functional experts should remain part of the picture—as consultants, advisory panellists, or rotating guest participants—but not permanent fixtures at the helm.

2. The limitations of specialist-dominated boards

2.1 Obsolescence of expertise

Expertise, particularly in rapidly evolving fields such as cybersecurity or digital marketing, has a half-life. A director whose reputation is grounded in their achievements from a decade ago may no longer be equipped to handle contemporary challenges in that domain. As Sydney (2020) remarks, “Expertise earned in the past can easily become obsolete when not continually tested in real-time environments.”

Nili and Shapira (2024) found that directors labelled as specialists often experience a depreciation of influence over time, especially when their technical knowledge fails to align with emerging trends or technologies. In effect, these directors may inadvertently become liabilities rather than assets.

2.2 Authority bias and groupthink

When boards rely heavily on domain specialists, they risk developing a cognitive dependency on those individuals, leading to authority bias. This creates a boardroom dynamic where certain directors dominate conversations within their area of specialty, and other members hesitate to challenge or even question their inputs.

As Nili and Shapira (2024) note, “Authority bias leads to suppression of diverse viewpoints, particularly when the specialist has been recruited under the premise of exclusivity of knowledge.”

This contributes to groupthink which might hinder the board’s ability to critically evaluate, discuss and challenge strategic decisions from a multi-dimensional perspective.

2.3 Fragmented oversight and responsibility silos

A board composed of function-specific experts risks devolving into a confederation of silos. Each director may focus narrowly on their area, resulting in an aggregation of perspectives rather than an integrated strategic vision. This is antithetical to the purpose of a board, which is to provide overarching governance and align on long-term value creation.

Moreover, these silos can lead to poor communication and accountability. For instance, cybersecurity may be deemed “handled” because a former CISO is on the board, but this individual may not be aligned with current best practices or may fail to integrate the issue into a broader risk framework.

2.4 Firms exemplifying the “Noah’s Ark-like” board composition

According to my framework evaluation, the following companies have (had) boards predominantly composed of domain-specific experts, which may lead to fragmented oversight and a lack of cohesive strategic direction:

1. Credit Suisse Group AG
   • Prior to its acquisition by UBS in 2023, Credit Suisse’s board was heavily populated with specialists in risk management, compliance, and technology.
   • The lack of generalist leadership contributed to challenges in strategic oversight and cohesive decision-making. We all know what happened here.
2. Synopsys Inc.
   • The board includes individuals with deep expertise in software, semiconductors, and related technical fields.
   • While this brings valuable insights, in my view, the board lacks a sufficient number of generalist leaders with broad operational experience.
3. Ansys Inc.
   • Ansys’ board comprises individuals with substantial experience in engineering and technology sectors.
   • The composition leans heavily towards technical expertise, potentially limiting broader strategic perspectives.
4. Dell Technologies
   • The board is composed of members with extensive backgrounds in technology and engineering.
   • This concentration of technical expertise may result in a narrower focus on operational and strategic issues.
5. NVIDIA Corporation
   • NVIDIA’s board includes several members with strong technical backgrounds in graphics processing and computing.
   • While beneficial for product development, this may limit diverse strategic viewpoints at the board level.

3. The strategic value of generalist leadership

3.1 Systems thinking and integration

General managers bring a systems-oriented perspective, honed by years of operational leadership, cross-functional collaboration, and enterprise accountability. Unlike specialists, they are not confined by functional dogma and are more adept at evaluating trade-offs, interdependencies, and strategic timing.

Generalists also tend to excel in scenario planning, a crucial skill in the VUCA landscape. Their exposure to multiple business cycles, regulatory environments, and stakeholder contexts makes them capable of contextualising issues that transcend functional boundaries.

3.2 Leadership and people management acumen

Boards are not merely technical advisory bodies; they are fiduciary stewards responsible for setting tone, culture, and long-term direction. As such, directors need more than technical knowledge—they require leadership. Generalists who have led large teams and managed large P&Ls bring firsthand knowledge of how strategic decisions impact people, performance, and profit.

As Roberta Sydney (2020) puts it: “Great board members are not those with the narrowest expertise but those with the broadest capacity to lead, challenge, and support from a holistic standpoint.”

3.3 Enhanced strategic dialogue and decision-making

Strategic oversight requires directors to ask the right questions, not just provide the right answers. Generalists, with their cross-functional experience, are often better positioned to identify gaps in strategy and explore unintended consequences. They can bridge the knowledge of specialists without being trapped in it.

The National Association of Corporate Directors (NACD) emphasises that effective boards engage in strategic conversations that go beyond operational details. This necessitates board members who can traverse diverse domains and synthesise insights.

3.4 Seven global firms with best-in-class generalist boards

Here are seven “best-in-class” global firms whose board compositions reflect a strong commitment to generalist leadership, strategic breadth, and cross-functional oversight. These boards embody the antithesis of the “Noah’s Ark” model by prioritising operational experience, enterprise leadership, and integrative thinking over siloed technical specialisation.

1. Best Buy Co., Inc.

• Why it stands out: Includes seasoned CEOs (Corie Barry, Hubert Joly) and CFOs (Karen McLoughlin), blending operational, digital, and financial acumen.
• Governance strength: The board is involved in long-range planning and organisational culture, not just functional compliance.

2. Nestlé S.A.

• Why it stands out: Features former CEOs (Paul Bulcke), global executives, and experts in nutrition, marketing, and ESG.
• Governance strength: Diversity of leadership backgrounds contributes to long-term strategic alignment across global markets. PS: Not a single Swiss in the board although it is Swiss based.

3. Microsoft Corporation

• Why it stands out: Strong mix of tech innovators (Satya Nadella, Reid Hoffman), policy leaders (Penny Pritzker), and investors (Hugh Johnston).
• Governance strength: The board’s composition enables foresight in innovation and adaptability to policy and market shifts.

4. Unilever PLC

• Why it stands out: Board members have held leadership positions across consumer goods, sustainability, and emerging markets.
• Governance strength: Emphasises purpose-driven strategy with operational execution.

5. Procter & Gamble Co.

• Why it stands out: Broad operational experience across marketing, international business, and corporate strategy.
• Governance strength: The board is known for supporting long-term innovation while managing scale and complexity globally.

6. ABB Ltd.

• Why it stands out: Chaired by Peter Voser (former Shell CEO) with board members including industrial CEOs, CFOs, and operational leaders (e.g., Atlas Copco, Caterpillar).
• Governance strength: Industrial and engineering complexity is matched by real-world general management experience across sectors and geographies.

7. UBS Group AG

• Why it stands out: Though historically more specialised, the current board reflects a shift toward generalist leadership: banking CEOs (Gail Kelly), macroeconomists (William Dudley), policy advisors, and digital leaders. This board has learned from the CS debacle and is ensuring that is moves toward a more generalist approach.

Governance strength: Increasing emphasis on governance, geopolitical awareness, and technology strategy with global integration.

4. The hybrid model: generalists with consultative experts

A growing number of governance experts advocate for a hybrid model in which boards are composed primarily of generalist leaders while subject matter experts are brought in on an ad hoc or consultative basis. This model preserves the strategic bandwidth of the board while still incorporating the latest expertise in fast-moving domains.

The Harvard Law School Forum on Corporate Governance (2024) writes, “Adding a director with a narrow range of expertise may reduce the quality of board discussions on other, more prevalent topics on the agenda. A better approach is to access specialist knowledge via external advisors or advisory boards.”

This approach is not merely theoretical. Many high-performing boards have established external advisory panels or rotate in technical experts for particular strategic reviews or quarterly deep dives. These consultants provide real-time insights without permanently altering the board’s structure or diluting its strategic cohesion.

5. Global governance implications

Global organisations require directors who understand international markets, regulatory systems, and geopolitical dynamics. Generalists who have managed operations in multiple regions bring nuanced perspectives that specialists often lack. Their broader worldview is essential in aligning global strategy with local execution.

General managers are more likely to bring experience from multiple sectors, enabling boards to cross-pollinate ideas and practices. In contrast, specialists often have deep but narrow experience, which can limit innovation or relevance across different contexts.

Generalists tend to be better crisis managers. Having led through downturns, restructurings, and transformations, they are equipped to make swift, principled decisions under pressure. Their presence on a board strengthens institutional resilience.

6. Recommendations for board composition policy

• Prioritise Leadership Track Record in Board Recruitment

Search committees and nominating boards should place greater emphasis on operational leadership experience rather than recent technical expertise. Candidates should be evaluated on their ability to synthesise, challenge constructively, and lead across functions.

• Establish Standing Advisory Councils

Rather than embedding all needed expertise within the board, organisations should institutionalise external advisory councils composed of domain experts who can be called upon for in-depth consultations.

• Conduct Regular Composition Audits

Boards should assess their composition annually to ensure alignment with strategic needs, not just compliance checklists. This includes identifying whether a board has become too narrow in its functional expertise and whether it retains integrative thinkers.

• Educate on Governance Over Expertise

Board onboarding programs should stress fiduciary responsibility, enterprise leadership, and strategic oversight rather than domain mastery. General governance capability should be cultivated and prioritised.

7. Conclusion

The composition of a board is one of the most powerful levers for corporate performance. In a globalized, fast-changing environment, boards must be able to operate above the fray of specialist silos. The evidence increasingly supports a model that privileges generalist leadership, enriched by specialist insight when needed but not dominated by it.

Don’t fill the Ark—staff the Bridge: Boards need navigators, not more passengers.

By adopting a generalist-first philosophy in board appointments, global corporations can foster more integrated thinking, sharper strategic oversight, and greater institutional resilience. The Noah’s Ark model of expert duplication is outdated; what boards need today are strategic navigators who can steer through complexity, not passengers who specialise in reading one part of the map. Read More.

Board Effectiveness | A Survey Of The C-Suite 

What’s in this report from PwC:

Executive confidence in board effectiveness has reached an all-time high, with 41% of more than 500 surveyed C-suite executives rating their boards as excellent or good, up from 29% in 2021. But that confidence is concentrated among executives with the closest board access; among those who rarely interact with the board, just 17% give the same rating, exposing a perception gap that may not surface until boards are under real pressure.

Some other key findings:

• Director bandwidth and pace of change are the leading constraints on board effectiveness, with executives citing overboarding (47%), slow response to emerging risks (35%) and difficulty keeping pace with digital transformation (34%).
• Nearly all executives (99%) believe boards should be using AI for oversight, but only 35% say their boards currently do so.
• Nine in 10 executives see room to improve how board assessments are used, particularly to inform succession planning and drive follow-through on board input. Read More.

Corporate Governance | Corporate Oversight And Shareholder Engagement 

Growing corporate adoption of Artificial Intelligence (AI) has increased the importance of AI governance to both companies and shareholders. U.S. technology companies have been expanding their AI governance and disclosure frameworks. However, investor views on the adequacy of these practices continue to evolve, and AI-related shareholder proposals have increased over the past five years, despite a general decline in environmental and social proposals. Investors may well continue to engage on AI-related issues in the future, whether through proposals or other mechanisms.

Investment Stewardship in the AI Era is a publication series exploring the opportunities, risks, and responsibilities emerging from AI. Every edition focuses on a specific topic, offering concise analysis, proprietary insights, and practical observations for investors seeking to understand the opportunities and challenges ahead.

This report examines the different ways companies, shareholders, and others have been approaching AI oversight.

AI governance is moving from a specialist technology issue to a mainstream board, strategy, capital allocation, and stewardship topic.

Key Takeaways

1. AI governance is increasingly a core investment and stewardship issue. As AI becomes increasingly embedded in corporate strategy, capital allocation, and infrastructure planning, investors are evaluating not only growth opportunities but also boards’ ability to oversee associated risks and externalities.
2. AI-related investor concerns extend beyond technology execution. Shareholder engagement increasingly focuses on issues such as data privacy, misinformation, human rights, workforce impacts, energy consumption, water usage, emissions, and broader societal risks arising from AI deployment and infrastructure expansion.
3. Large technology companies have expanded governance and disclosure frameworks around AI. The companies reviewed generally disclose board-level oversight mechanisms, AI-related risk factors, responsible AI commitments, and sustainability-related practices, suggesting that AI governance is becoming more formalized within existing corporate governance structures.
4. Despite these developments, shareholder scrutiny remains elevated. The persistence of AI-related shareholder proposals indicates that some investors continue to question whether current oversight, disclosure, and risk-management practices are sufficient to address the long-term implications of AI.
5. The debate is increasingly shifting from AI oversight to AI governance adequacy. With potential changes to Rule 14a-8 and growing discussion of alternative governance models, investors, policymakers, and companies are increasingly focused on whether existing governance frameworks are capable of managing AI-related risks and opportunities over the long term.

About This Report

This report examines various aspects of how shareholders and companies are addressing AI governance. These aspects include how shareholder proponents are framing AI-related risks, patterns of AI governance disclosures among certain U.S. technology companies, trends in AI-related shareholder proposals, and different alternative models of AI governance.

Ongoing investment in AI data centers is likely to continue, but investors are concerned not only with the potential returns on the massive investments but also with AI-related risks such as water and energy use, data privacy, and workforce impacts. The U.S. technology companies reviewed in the report respond to these concerns to some degree. They identify AI as a material risk factor and publicly disclose commitments to responsible AI principles, and many also disclose information on AI-relevant environmental and social risks.

AI-related shareholder proposals on U.S. ballots have grown in recent years, a trend that suggests a persistent investor focus on AI governance. However, evolving regulation may lead to investors pursuing other means of engaging on AI concerns, such as direct dialogue or proxy voting.

Amid heightened investor and company concern about AI, policymakers, academics, and others are considering alternative approaches to AI governance and accountability. Proposed options include closer public oversight of technology companies, federal equity stakes in technology companies, and mission-guardian structures, although some of these approaches have also attracted criticism. These discussions of alternatives relate to the larger question of whether conventional corporate governance is sufficient for AI-related risk.

Governing AI is likely to remain a concern in the future. For institutional investors, a major concern may be whether corporate AI governance frameworks can foster value creation while effectively managing risks.

ISS STOXX clients can download an expanded version of this report by logging into ProxyExchange, then selecting the Knowledge Center and its Library tab. The expanded report features additional detailed information on the selected technology companies and their AI governance profiles, along with a comprehensive listing of AI-related shareholder proposals, including the issuer, proposal description, proponent, and voting support levels.

Introduction

For institutional investors, the AI infrastructure buildout is increasingly viewed through two lenses:

• As a capital expenditure supercycle with the potential to support a broader secular shift in the global economy
• As an emerging governance and stewardship priority that may introduce material operational, financial, social, environmental, and policy risks.

The investment thesis remains compelling, but it is also being tested by questions around return on invested capital, free cash flow compression, financing requirements, energy and water constraints, and the durability of current spending levels.

This report examines how those issues surfaced during the 2026 U.S. proxy season. It focuses on four related areas.

First, the report assesses how shareholder proponents are framing AI-related risks, including privacy, misinformation, workforce impacts, discrimination, human rights, child safety, surveillance, water and energy use, and emissions.

Second, it reviews AI governance disclosures among a select group of large U.S. technology companies, including board and committee oversight, risk-factor disclosure, responsible AI commitments, sustainability reporting, and capital-structure considerations.

Third, it reviews AI-related shareholder proposal activity in the 2026 U.S. proxy season, including how proposal volume and support levels compare with the broader decline in environmental & social (E&S) proposals.

Finally, it considers alternative governance models being debated in policy and academic circles, including proposals for enhanced public oversight, government equity participation, frontier-model review pathways, and mission-guardian structures.

The central takeaway is that AI governance is moving from a specialist technology issue to a mainstream board, strategy, capital allocation, and stewardship topic. Large technology companies are expanding disclosures around AI oversight and responsible AI principles, but shareholder proponents continue to argue that disclosure alone does not resolve the underlying risks. As ongoing changes to Rule 14a-8 reshape the shareholder proposal process, institutional investors may increasingly rely on a broader engagement toolkit to assess whether boards are providing effective oversight of AI-related risks and opportunities.

AI governance is moving from a specialist technology issue to a mainstream board, strategy, capital allocation, and stewardship topic.

AI Infrastructure as an Investment and Governance Theme

The rapid expansion of AI infrastructure has intensified scrutiny from a wide range of stakeholders. Local opposition to data center development; competition for energy, water, and critical mineral resources; privacy concerns; misinformation; workforce disruption; and evolving local, state, federal, and geopolitical regulations are all becoming part of the investment debate. Some observers further contend that systemic risks associated with AI adoption, deployment, and infrastructure expansion may not yet be fully reflected in market valuations.

At the same time, geopolitical competition, national security considerations, and the strategic importance of resilient digital infrastructure are likely to continue supporting investment across primary, secondary, and tertiary industries linked to the AI data center buildout. For investors, the issue is therefore not simply whether AI infrastructure spending continues, but whether companies can demonstrate disciplined capital allocation, credible risk oversight, and governance frameworks that are proportionate to the scale of the opportunity and the risks. These governance questions have become increasingly prominent in investor stewardship discussions, with shareholder proponents arguing that a range of AI-related risks warrant greater corporate attention and oversight.

Shareholder Proponents and the Debate Over Material AI-Related Risks

One lens through which these governance concerns have become visible is the shareholder proposal process. Many environmental and social shareholder proposals have been submitted repeatedly at large U.S. technology companies over multiple proxy seasons. Before the current AI transformation, proponents were already raising issues such as emissions, water use, data privacy, discrimination, misinformation, child safety, workforce impacts, surveillance, and operations in conflict-affected and high-risk areas. Many of these longstanding digital-platform concerns are now being discussed through the lens of AI, but the underlying risk themes remain familiar.

For shareholder proponents, the persistence of these concerns suggests that material risks may remain insufficiently addressed, notwithstanding expanded governance and risk disclosures. Their argument is not that companies have failed to disclose governance structures altogether, but that their disclosures may not demonstrate sufficient remediation, accountability, or operational change when controversies recur. In that view, AI governance could become disclosure-heavy without necessarily becoming outcome-oriented.

Andrew Behar, CEO of As You Sow and a shareholder proposal proponent, has stated that shareholder proposals can serve as early warning systems that some companies ignore. Sanford Lewis, Director and General Counsel of the Shareholder Rights Group, has characterized issues raised in shareholder proposals as neglected material risks. In this framing, proponents argue that systemic risks and negative externalities may be material to long-term value yet are not fully captured in conventional financial statements or balance-sheet analysis.

At the same time, the shareholder proposal landscape has become more polarized. Support for environmental and social proposals has declined in recent years, reflecting broader shifts in investor voting behavior, changing interpretations of materiality, and political scrutiny of stewardship practices. AI-related proposals therefore sit at the intersection of two competing trends: rising investor attention to AI-related risks and falling support for many environmental and social shareholder proposals overall. As these debates continue, a key question is whether existing corporate governance frameworks are sufficient to address the risks and opportunities associated with AI, or whether boards are already putting appropriate oversight mechanisms in place.

AI Governance Disclosures among Select U.S. Technology Companies

To assess how companies are responding to these concerns, this report takes a closer look at selected U.S. technology companies[1] during the 2026 proxy season, with a focus on corporate governance disclosures relevant to AI oversight. The review considers proxy statement disclosures, board skills matrices, committee charters, Form 10-K risk factors, responsible AI statements, sustainability reporting, and capital-structure features.

As discussed in a December 2025 ISS STOXX article, a growing number of institutional investors view effective oversight of AI-related issues as relevant to fiduciary duty, investment stewardship, and corporate engagement. UNESCO and the Thomson Reuters Foundation’s Responsible AI in practice report, which leveraged data from the AI Company Data Initiative (AICDI) and was grounded in UNESCO’s Recommendation on the Ethics of AI, examined responsible AI disclosures from approximately 3,000 companies. The report identified significant gaps in responsible AI disclosure, particularly in board oversight and implementation due diligence mechanisms. AICDI data is also shared with an investor signatory group representing approximately USD 1.2 trillion in assets under management.

Against this evolving landscape, the governance disclosures of large technology companies provide one indication of how boards are approaching AI oversight, risk management, and accountability. For many asset owners and asset managers, the core governance question is whether companies and boards are aligning AI-related strategy with long-term financial value creation, responsible risk management, and sustainable capital allocation. AI governance effectiveness is, therefore, not limited to whether companies disclose responsible AI principles; it also depends on whether those principles are embedded in oversight structures, risk management processes, product governance, infrastructure planning, and stakeholder impact assessments.

Figure 1: Considerations in AI Long-Term Financial Value Creation

The U.S technology companies reviewed generally exhibit robust baseline governance practices, including board independence, committee independence, meeting attendance, and overboarding policies (the data is from Form DEF 14A proxy statement disclosures).

However, AI is not explicitly identified in most of the selected companies’ board skills disclosures, suggesting that boards may be treating AI as part of broader technology, cybersecurity, product, strategy, or risk oversight rather than as a standalone expertise category.

In Form 10-K Item 1A “Risk Factor” disclosures, all the companies reviewed explicitly identify AI as a material risk factor. These companies also identify climate change as a material risk factor and discuss material social risks and harms, including issues such as human rights, privacy, and employment, although the level of specificity varies by issuer.

Committee charters and other governance documents are generally available through company investor relations websites. In their committee charters, Meta and Microsoft explicitly include AI within a board committee’s oversight responsibilities. The other companies do not explicitly include AI in this way, perhaps because they reasonably view AI as warranting full-board oversight, particularly given its connection to corporate strategy, capital allocation, product development, enterprise risk management, cybersecurity, regulatory compliance, and reputation.

It is also common for audit committees, nominating and governance committees, and other standing committees to have overlapping responsibilities for enterprise risk management and corporate governance risk exposure. Because AI can expose companies to a wide range of environmental and social risks, the companies reviewed also delegate oversight of material environmental and social matters through formal committee charters.

All companies reviewed publicly disclose commitments to responsible AI principles on their corporate websites. Many also provide recurring updates on responsible AI initiatives, with Alphabet and Microsoft publishing formal reports on an annual cadence. The OECD has also published Due Diligence Guidance for Responsible AI, reflecting the growing focus on implementation mechanisms rather than principles alone.

In sustainability reporting, virtually all companies reviewed maintain Net Zero targets and disclose information on water management, waste management, and selected social risk topics. These disclosures are increasingly relevant to AI governance because the infrastructure supporting AI development and deployment can have significant implications for energy demand, water use, emissions trajectories, supply chains, data privacy, workforce impacts, and community relations.

Finally, only two companies in the selected peer group, Alphabet and Meta, have multi-class capital structures. For investors evaluating AI governance, capital structure remains relevant because it can affect shareholder influence, board accountability, and the practical impact of investor engagement or escalation.

While many companies have expanded their baseline governance and disclosure practices in recent years, they have not eliminated investor concerns. The 2026 proxy season illustrates the extent to which shareholders continue to seek greater AI-related oversight, accountability, transparency, and board engagement in AI-related risks and opportunities.

AI-Related Shareholder Proposals during the 2026 U.S. Proxy Season

Against this backdrop, AI-related shareholder proposals on U.S. ballots increased slightly in 1H2026 compared with the prior year, even as the total number of environmental and social (E&S) proposals declined sharply.[2] Overall U.S. E&S shareholder proposals reached an all-time high of almost 417 proposals on ballots in 1H2024, fell to approximately 253 in 1H2025, and declined further to approximately 162 in 1H2026, representing a decline of more than 60 percent compared with 1H2024. AI-related shareholder proposals at U.S. companies increased to 29 in 2026, up from 26 in 2025 and 24 in 2024.

This divergence suggests that AI-related social, environmental, and governance risks have been a focus of investor attention and engagement, even as broader E&S proposal activity continues to contract.

Figure 2. AI-Related Shareholder Proposal Volume at U.S. Companies (2022 to 2026)

However, proposal volume and investor support tell different stories. While AI-related proposals continued to increase in number (Figure 2), support for AI-related proposals broadly mirrored the wider trend of declining support for the E&S proposal landscape.

As of June 30, 2026, U.S. E&S shareholder proposals received average support of 9.7 percent, down from 10.6 percent during the same period in 2025 and 15.5 percent in 2024. A similar pattern emerged among AI-related proposals, where average support declined to 10.3 percent, compared with 13 percent in 2025 and 19.2 percent in 2024 (Figure 3).

Figure 3. AI-Related Shareholder Proposal Support Levels at U.S. Companies (2022 to 2026)

The persistence of AI-related shareholder proposals suggests that many investors continue to view AI governance as an important engagement topic. However, future engagement may not rely as heavily on the shareholder proposal process.

Regulatory developments may further reshape engagement channels. On November 17, 2025, the U.S. Securities and Exchange Commission (SEC) announced that it would no longer respond to no-action requests under Exchange Act Rule 14a-8. On August 14, 2026, the SEC announced that it had determined to discontinue responding to all Rule 14a-8 no-action requests and would no longer provide “no-objection” letters, effectively withdrawing from the shareholder proposal review process. The SEC has also indicated on several occasions that it may pursue rulemaking to rescind Rule 14a-8. Consistent with that position, on August 28, 2026, the SEC submitted a proposed rule to rescind Rule 14a-8 to the Office of Management and Budget.

If the shareholder proposal process becomes less available or less predictable, investors may need to rely more heavily on other engagement mechanisms, including direct dialogue, proxy voting, escalation strategies, investor coalitions, public policy engagement, and litigation-related or campaign-based approaches, where appropriate.

As investors continue to identify and engage on material AI-related risks, a related question is whether existing governance structures are sufficient to oversee a technology with potentially significant economic, social, and national security implications. This question has prompted growing discussion among policymakers, academics, and market participants about alternative approaches to AI governance and accountability.

The persistence of AI-related shareholder proposals suggests that many investors continue to view AI governance as an important engagement topic.

Emerging AI Governance Models

Proposed alternatives to current AI governance span a wide range of approaches. Some focus on increasing public oversight of private AI companies, while others contemplate a more direct government role in strategically important firms or the creation of governance mechanisms tailored specifically to frontier AI systems.

These debates have contributed to a broader discussion over whether conventional corporate governance arrangements remain adequate for AI-related risks. The speed and scale of AI development have prompted debate over whether prevailing corporate governance norms are sufficient for novel AI technologies. Some commentators have proposed alternative governance models intended to address risks that may extend beyond the traditional shareholder-company relationship.

Public Oversight Mechanisms

In a Financial Times opinion piece, Council on Foreign Relations (CFR) Senior Fellow Richard A. Falkenrath argued for a presidentially nominated, Senate-confirmed director to sit on certain technology company boards. Falkenrath contended that such a board seat would not amount to nationalization but would instead provide a mechanism for minimum accountability, given the national security and civilizational implications of AI.

Frontier-model review and vetting pathways also already exist in certain forms, including through the U.S. Department of Commerce’s Center for AI Standards and Innovation (CAISI), Executive Order 14409, and the Bureau of Industry and Security (BIS)’s export-control regime. These mechanisms reflect a policy environment in which AI governance is increasingly linked to national security, competitiveness, advanced computing infrastructure, and cross-border technology controls. Finally, some have proposed a FINRA-style self-regulatory organization that would report to the SEC on matters related to frontier AI governance.

Public Capital and Ownership Models

In addition to enhanced oversight, policy discussions have focused on industrial policy, federal government equity stakes, and the possibility of an AI sovereign wealth fund. The CFR and the Center for Strategic and International Studies (CSIS) have tracked recent federal equity stakes. CSIS commentary has highlighted the potential governance and political economy risks that can arise when public capital and strategic private-sector industries become closely intertwined.

Mission-Driven Governance Structures

Academic debate has also addressed mission-guardian structures. In AI Corporate Governance and Ben & Jerry’s Risk, Harvard Law School professor Jesse M. Fried and S.J.D. candidate Idan Reiter examined risks presented by ambitious mission guardians at Ben & Jerry’s and OpenAI. They argue that such structures can harm both investors and corporate missions. The authors suggested that many companies may follow Anthropic’s example by instituting a kill switch, under which a super-majority of investors can remove guardians.

Conclusion

AI governance has become an increasingly material issue for institutional investors because it connects large-scale capital deployment with a broad set of operational, financial, social, environmental, and policy risks. The 2026 U.S. proxy season suggests that large technology companies are expanding disclosures around AI oversight, responsible AI principles, risk factors, and related sustainability considerations. However, shareholder proponents continue to argue that disclosure alone may not adequately address the risks associated with AI development, deployment, and infrastructure expansion.

The modest increase in AI-related shareholder proposals during 1H2026, against a broader decline in environmental and social proposal activity, indicates that AI remains a meaningful stewardship topic. As the shareholder proposal process faces potential regulatory change, investors may increasingly rely on a broader toolkit, including direct engagement, proxy voting, escalation strategies, and policy dialogue, to evaluate whether companies are effectively governing AI-related risks and opportunities.

For boards and management teams, the governance challenge is likely to become more complex as AI becomes further embedded in long-term strategy, capital allocation, enterprise risk management, sustainability commitments, and public policy debates. For institutional investors, the key question is whether corporate AI governance frameworks are sufficiently specific, accountable, and operationally grounded to support durable value creation while managing the externalities that could affect both portfolio companies and the broader market. Read More.