Responsible AI: Where Innovation Meets Oversight

Key Takeaways

• Responsible AI has increasingly become an investment issue, with governance shaping regulatory exposure, litigation risk, and long-term earnings.
• AI governance structures are no longer just a risk mitigant; they are emerging as a competitive differentiator.
• We believe engaging companies through a structured framework spanning risk, governance, and opportunity can help support responsible AI development.

Today, AI is no longer a distant technological frontier but a defining force reshaping industries, productivity, and global competition.

As investors, we aim to be active observers in this transformation. Through capital allocation and engagement, we seek to assess and, where appropriate, encourage practices that support sustainable long-term value creation and risk management.

At its core, responsible AI is not just simply a governance or ethical consideration; we believe it is a financially material investment consideration.

Why Responsible AI Matters for Long-Term Value

AI presents companies with clear opportunities for growth and efficiency, but it also introduces a complex risk landscape that spans factors such as regulatory scrutiny, litigation exposure, reputational damage, and even operational disruption. And these risks are increasingly tied to how companies govern AI, not just how they deploy it.

Recent research underscores the gap between ambition and execution. A Fortune/Deloitte CEO survey found that only 56% of CEOs report actively cultivating a culture of ethical AI. [1] At the same time, an Infosys Knowledge Institute survey of 1,500 senior executives revealed that while 78% view responsible AI as a driver of business growth, many organizations lack the infrastructure to deploy it safely. In fact, only 2% of companies surveyed met the full standards set in Infosys’ internal responsible AI capability benchmark. [2]

For investors, this disconnect signals potential volatility: companies that fail to establish robust governance frameworks may face heightened regulatory costs, weaker customer trust, and impaired long-term earnings potential.

Governance as a Competitive Differentiator

In addition, AI governance structures themselves are becoming a meaningful driver of differentiation. Unlike traditional technology companies, many leading AI developers are experimenting with unconventional governance models, combining nonprofit oversight, public benefit corporations, and independent trusts.

These structures, which are designed to balance commercial objectives with broader societal responsibilities, introduce new questions for investors:

• Who ultimately controls AI deployment decisions?
• What constraints exist on commercialization?
• Who bears the consequences when risks materialize?
• How should companies navigate potential conflicts between safety and revenue?

These factors can directly influence revenue visibility, regulatory exposure, and even terminal value assumptions.

Importantly, governance is not only a risk mitigant, but it can also be a source of competitive advantage. We believe companies that demonstrate strong oversight, transparency, and alignment between innovation and ethics may be better positioned to earn stakeholder trust, adapt to evolving regulation, and sustain potential long-term growth.

Responsible AI governance does not eliminate business, regulatory, operational, or investment risk, and there is no assurance that engagement efforts or governance enhancements will result in improved company performance, lower volatility, or superior investment returns.

A Rapidly Evolving Regulatory Landscape

The policy environment around AI is developing quickly across jurisdictions. Governments are increasingly treating AI as a matter of national security, economic competitiveness, and societal stability. In the United States, for instance, regulators are taking a more active role in overseeing frontier models, increased attention on AI-related risks, including national security, cybersecurity, transparency, and accountability considerations. Internationally, the European Union’s AI Act establishes one of the most comprehensive risk-based regulatory frameworks to date, while other jurisdictions are advancing their own rules on data governance, model transparency, and high-risk use cases.

We believe this evolving landscape creates both risk and opportunity. Companies that proactively align with emerging regulatory expectations are better positioned to avoid costly compliance disruptions, while laggards may face tightening constraints, fines, or loss of market access.

Our Engagement Framework: From Principles to Practice

To navigate the complexity of AI-related risks and opportunities, William Blair Investment Management applies a structured engagement framework that assesses how companies evaluate and manage AI.

Step One: Risk Identification

We begin by seeking to understand how AI is already influencing decisions—including informal or “shadow” use cases—and whether companies maintain a comprehensive view of AI-related risks supported by clear audit trails and explainability.

Step Two: Risk Analysis

We then focus on how companies evaluate the likelihood and impact of AI risks, test systems for bias and performance, and stress-test models before deployment, including escalation protocols for unexpected outcomes.

Step Three: Risk Mitigation

Next, we examine whether companies have real-time oversight of AI systems, clearly defined authority to pause or override deployment, and risk controls embedded throughout the product lifecycle. We also evaluate how firms are preparing their workforce for AI-driven changes and whether oversight processes are designed to operate at the pace of AI innovation.

Step Four: Governance and Accountability

Finally, we assess governance and accountability, including board-level oversight, clarity of roles and responsibilities, and alignment between AI strategies and long-term stakeholder outcomes. In particular, we focus on who has decision-making authority over high-risk AI applications and whether ethical considerations are integrated into core business strategy rather than siloed within compliance functions.

Step Five: Opportunities

In addition to risk management, we explore opportunities such as how AI can drive productivity, support environmental outcomes, and deliver shared value across employees and communities.

Engagement in Practice

Based on a sampling of recent company engagements, we have observed a growing recognition of the importance of responsible AI, particularly in enterprise applications where trust is essential.

Leading companies are:

• Establishing cross-functional AI governance committees
• Integrating fairness, privacy, and safety considerations into product design
• Enhancing transparency around model use and limitations
• Investing in internal monitoring, testing, and risk mitigation tools

Practices remain uneven, however, particularly in board-level oversight and escalation processes for high-risk AI use cases. But through our engagements, we encourage companies to strengthen these practices while maintaining room for innovation.

For example, we have urged portfolio companies in enterprise software and banking to move beyond broad responsible AI principles by improving factors such as: transparency, independent validation, evidence of governance effectiveness, and disclosures on risk identification, bias management, regulatory readiness, and human-rights impacts.

The Investor Imperative

From an investment perspective, the relevance of responsible AI extends across styles and geographies. For growth-oriented companies, strong AI governance can help support premium valuations by strengthening trust, reducing regulatory uncertainty, and improving the sustainability of AI-driven business models.

For value-oriented companies, effective oversight may help protect earnings quality and limit downside risks associated with litigation, compliance failures, or operational disruptions.

And in emerging markets, where regulatory frameworks, data infrastructure, and governance practices may be less mature, AI presents both leapfrog opportunities and heightened implementation risks, making oversight especially important.

We believe responsible AI is not about slowing innovation. Rather, it is about ensuring that innovation is sustainable, scalable, and aligned with long-term value creation.

As AI continues to transform industries, we believe the companies that succeed will be those that can balance technological advancement with robust governance and transparency. Investors, however, have a critical role to play in reinforcing this balance.

So, by engaging with companies, integrating governance considerations into investment decisions, and advocating for stronger standards, we aim to support the responsible development of AI—with the objective of better understanding and, where appropriate, encouraging practices that may help address risks and opportunities that this technology presents.

1. Source: Deloitte, Fortune/Deloitte CEO Survey, Fall 2025. ↩
2. Source: Infosys Knowledge Institute Survey, Responsible Enterprise AI in the Agentic Era, 2025. ↩