Artificial Intelligence (AI) is rapidly reshaping economies and societies alike, presenting both significant opportunities and efficiencies, as well as profound governance challenges. The onus falls on institutional investors not only to understand these changes, but to craft stewardship frameworks that ensure responsible, sustainable value creation through AI. TD Asset Management Inc. (TDAM) has undertaken a comprehensive analysis of the available literature on AI governance from a stewardship standpoint. Our goal is to distill actionable insights and build a robust framework tailored to our stewardship program, providing clarity for both internal alignment and external engagement.

Governance of AI and Financial Performance

Early and proactive governance at the board level has emerged as a key differentiator for companies integrating AI. Some research reveals that organizations which began addressing AI governance early - especially at the highest levels - demonstrate outperformance against major benchmarks¹. The drivers of this success are multifold:

• Dedicated Technology Oversight: The integration of science, technology and innovation committees within the board structure enables targeted oversight of AI strategy and risk. These committees act as conduits for expertise, keeping boards closely attuned to technological trends and their implications for the business.
• Strategic and Governance Alignment: Early adopters of robust governance frameworks are better equipped to identify strategic and governance gaps, anticipate the impact of technology on products and services, and remain agile in adapting to market changes.
• Board Talent Acquisition: Companies prioritizing AI governance attract director talent with sector-specific knowledge and a nuanced understanding of AI’s transformative potential. This in turn fosters enterprise-wide alignment across commercial, talent, privacy/cyber/data security, and operational strategies.
• Enterprise Alignment: The most successful organizations treat AI not as a siloed function, but as an enterprise-wide imperative, ensuring that innovation aligns with budgetary priorities, operational improvements, talent strategy and data security.

Interestingly, we have observed that successful technology firms—where technology forms the core of their business models—tend to diffuse AI governance responsibilities across multiple committees. This distributed approach reflects the pervasiveness of AI in all aspects of their business. For other sectors, however, focused oversight is perhaps the best practice.

Risk Management

The risks inherent in AI adoption are multifaceted and extend far beyond the headline-grabbing incidents of data breaches or regulatory fines. TDAM’s stewardship perspective emphasizes the following dimensions:

• Technical Risks: AI systems are inherently complex and opaque, raising challenges such as hallucinations (the generation of false or misleading outputs), algorithmic bias, unreliable performance and unintended consequences, such as technology glitches.
• Regulatory Risks: The regulatory landscape for AI is rapidly evolving. Companies must remain vigilant about compliance with emerging standards, data privacy laws, labour laws and sector-specific regulations.
• Organizational Risks: The speed of AI adoption can outpace an organization’s ability to adapt. Without the right governance structures, talent and risk oversight, companies risk strategic misalignment, operational inefficiencies and reputational harm.
• Systemic Risks: The interconnectedness of AI systems means that failures in one area can rapidly cascade. Systemic risks include market disruptions, widespread misinformation and market volatility introduced by poorly governed AI deployment.
• Environmental Risks: With the rise of AI, the infrastructure needed to meet the demand has environmental implications, particularly the increase in the number of data centres. These centres consume a substantial amount of energy and require significant amounts of water for cooling. Risks derive from their locations, the stress they put on the surrounding environment, and the methods used to acquire these necessary resources.

Effective risk management demands a forward-looking, cross-disciplinary approach which integrates technical, legal, ethical and operational perspectives within the governance framework.

Systemic Issues and Opportunities

AI’s integration into the financial system and wider economy brings systemic considerations—both risks and opportunities that need to be actively managed:

• Market Stability: AI-driven trading and decision-making can both stabilize and destabilize markets. Oversight is needed to ensure algorithms act prudently and transparently.
• Societal Impact: AI can foster inclusion and efficiency but may also exacerbate inequality or automate away entire job categories. Responsible stewardship must account for the broader human capital impacts.
• Ethics and Trust: Firms that prioritize ethical AI gain stakeholder trust, which can be a durable source of competitive advantage.
• Innovation: AI opens the door for new business models, data-driven insights and operational excellence—but only if risks are prudently managed and opportunities are seized.

Future-Proofing AI Strategies

TDAM believes that future-proofing is not merely about technology adoption, but about building resilient, adaptable organizations. This entails:

• Dynamic Governance: Governance frameworks must evolve in lockstep with technological developments. Regular reviews, scenario planning and horizon scanning are essential.
• Continuous Talent Development: Building in-house expertise and upskilling the workforce safeguards against obsolescence and positions firms to capture AI’s potential.
• Stakeholder Engagement: Open dialogue with regulators, clients and communities builds trust and allows for shared learning as AI matures.
• Robust Data Governance: Data is the lifeblood of AI. Organizations must invest in secure, ethical and transparent data management practices to support trustworthy AI systems.

TDAM’s Stewardship Approach to AI: Two Pillars

Recognizing the breadth of AI’s impact, TDAM divides its stewardship approach into two primary focus areas: AI creators and AI users.
 

1. Stewardship Focus for AI Developers

AI developers comprise those entities responsible for innovation, infrastructure and foundational tools—developers of algorithms, infrastructure providers and data centers. Our stewardship priorities for this cohort include:

• Transparency and Explainability: Developers must prioritize making AI models and decisions explainable, especially in high-impact sectors such as finance, healthcare, transportation and defense.
• Security and Privacy: Protecting the integrity and privacy of data is paramount. Best practices include robust cybersecurity protocols, regular penetration testing, adherence to client consent agreements and copyright laws and compliance with global standards.
• Ethical Design and Bias Mitigation: Actively addressing ethical considerations and bias in models is critical. This includes diverse data sets, bias testing and transparent reporting.
• Collaboration with Regulators: Proactive engagement with policymakers may help shape pragmatic regulatory environments and may ensure broad societal benefit.
• Sustainable Infrastructure: As developers build the infrastructure needed to support their AI tools and their broader use, they should ensure that these tools are created to fit their own environmental goals and they should be cognizant that users also aim to meet environmental goals.
• Lifecycle Oversight: Governance must extend across the entire lifecycle—from data sourcing to model deployment and decommissioning.

2. Stewardship Focus for AI Users

AI users span a vast array of sectors—pharmaceuticals, finance, media, engineering and beyond. TDAM’s stewardship priorities for AI users emphasize:

• Risk Assessment and Mitigation: Users must conduct comprehensive risk assessments before deploying AI and evaluating technical, ethical and organizational factors.
• Board and Management Oversight: Ensure that board members and senior management possess adequate AI literacy and that governance structures match the pace of adoption.
• Alignment with Business Strategy: AI initiatives should clearly support the company’s long-term strategic objectives instead of being pursued as isolated projects.
• Impact on Stakeholders: Evaluate and report on the impact of AI deployment on customers, employees and broader society, addressing fairness, transparency and accountability.
• Operational Readiness: Users must invest in staff training, change management and monitoring systems to ensure AI is integrated safely and effectively.
• Environmental Due Diligence: Ensure that the supply chain necessary for AI use mitigates impacts to the environment and aligns with broader corporate environmental goals.

Conclusion

AI governance is not a static exercise but an ongoing journey that requires vigilance, adaptability and stewardship excellence. The rewards for early and robust governance are clear: improved financial performance, strategic clarity and operational resilience. Conversely, the risks of neglect are significant—not only for individual companies, but for the financial system and society.

Our stewardship framework reflects a belief in responsible innovation. We advocate for transparent, ethical, and future-focused AI governance, supporting both creators and users as they navigate this dynamic landscape. Through collaboration, continuous learning, and steadfast attention to risk and opportunity, we aim to safeguard long-term value of our investments.

For Canadian institutional investors only.

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¹https://corpgov.law.harvard.edu/2021/08/31/a-special-board-committee-can-help-drive-corporate-and-transformational-success/