Investors Matter for the Future of Trustworthy AI. Are they ready?

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This blog post draws on insights from the Global Partnership on AI (GPAI) Expert Group on Trustworthy AI Investment meeting which took place on 11 June 2026.

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Every day, investors influence which AI companies are funded, which technologies scale and which business models succeed. In 2025, 61% of all venture capital funding was directed towards AI, reflecting an extraordinary concentration of investment flowing into firms offering AI products and services.

Policymakers have spent years developing principles and frameworks for trustworthy AI. The OECD AI Principles, the first intergovernmental standard on AI adopted in 2019 and revised in 2024, provide a widely recognised framework for trustworthy AI. Yet investors still lack a common way to assess whether AI companies are living up to these standards and principles.

This creates a growing disconnect. Policymakers increasingly view trustworthy AI as essential for driving AI adoption, innovation and ultimately economic growth. Investors broadly agree. But when investment committees sit down to make decisions, they often lack the common language, data, tools, experience, and signals needed to translate trustworthiness, as reflected in the OECD AI Principles, into something they can evaluate and act on.

The good news is that this is changing. Across public markets, private equity and venture capital, investors are beginning to treat trustworthy AI not as a niche governance issue but as a material business factor. The challenge now is turning this initial interest into practical and standardised advice to help guide capital flows towards firms developing and deploying trustworthy AI.

Trust drives adoption. Adoption drives investment.

There is a simple logic behind investing in trustworthy AI. When people trust AI systems, they are more likely to use them. Greater adoption creates larger markets and stronger incentives for innovation. Innovation attracts investment. In turn, investment can reinforce trust by rewarding companies that build safer, more accountable, more efficient and more transparent AI systems, contributing to a virtuous cycle.

Today, however, this cycle remains incomplete. Investors increasingly recognise that AI governance, risk management and accountability matter. Yet many still struggle to identify which companies are genuinely managing AI risks well and which are merely making promises. Around the world, there is no common language, disclosure framework or set of comparable indicators that allows investors to consistently evaluate trustworthiness. As a result, trustworthiness in an investor context is often discussed in principle but rarely assessed in practice.

The GPAI Expert Group on Trustworthy AI Investment wants to help bridge this gap by examining how investors can help advance trustworthy AI by integrating governance, accountability and risk management considerations into investment decisions. The group provides a forum for dialogue between policymakers, investors and AI experts to identify practical ways to align investment incentives with global standards and tools like the OECD AI Principles, the Hiroshima AI Process Reporting Framework and the OECD Due Diligence Guidance for Responsible AI, which provides practical guidance to enterprises for implementing OECD standards on responsible business conduct and the OECD AI Principles.

Investors see AI trustworthiness as financially material

At the GPAI Expert Group on Trustworthy AI Investment meeting, held on 11 June 2026, one message emerged clearly from discussions across public and private markets: investors no longer view trustworthy AI as a purely ethical consideration. The central question for many investors is whether a factor affects long-term value creation. Increasingly, many believe AI trustworthiness does.

For public market investors, responsible AI practices are increasingly seen as material to investment decisions through the impact on enterprise customer demand, regulatory exposure, insurability, liability risk, compute cost, reputational risk, AI incidents and cybersecurity failures. At the same time, companies that demonstrate strong governance and robust AI risk management may enjoy competitive advantages as customers become more selective about the AI systems they adopt, as shown in recent research by the Oxford Martin AI Governance Initiative on The Role of Investors in AI Governance.

Private equity investors are reaching similar conclusions. Responsible AI is becoming part of investment due diligence, Board engagement, portfolio management and preparation for future public listings. AI governance is increasingly viewed as a component of business quality and performance, alongside earnings growth, operational efficiency, financial transparency, digital value creation, climate governance, strategic talent management, and other established governance practices.

Venture capital investors also recognise the importance of AI trustworthiness, although many are still early in developing practical approaches. Recent investor surveys suggest that most venture capital firms see commercial opportunities in trustworthy AI, but many have yet to establish a shared understanding of what it means in practice.

In short: the question is no longer whether trustworthiness matters. The question is how to measure it in a standardised and concrete way that makes sense for investors.  

The biggest gap is not motivation. It is information.

Investors frequently say the same thing: they want to do more, but they do not know exactly how. Across different investment communities, three needs arise repeatedly:

First, investors need clearer frameworks that translate high-level principles into practical tools for assessing responsible business practices. Existing resources such as the OECD AI Principles, the OECD Due Diligence Guidance for Responsible AI and other international standards provide important foundations. But investors often need simpler and more actionable guidance tailored to individual business models, investment decisions and aligned with investor-specific language and needs.  

Second, investors need comparable data. They want to understand how companies govern AI systems, manage risks, conduct testing, oversee third-party providers and address incidents. Much of this information is currently unavailable, inconsistent or difficult to compare across companies and jurisdictions. The Hiroshima AI Process (HAIP) Reporting Framework, the first global voluntary framework for organisations to report on their AI governance and risk management practices in a structured and comparable manner, could help lay the foundation for more consistent assessments of trustworthy AI by investors and other stakeholders.

Third, investors need credible verification. Independent audits, third-party assessments and assurance mechanisms can help bridge the trust gap between company claims and investor confidence. This mirrors developments seen in cybersecurity, sustainability reporting and financial auditing. Without these building blocks, investment teams will continue to struggle to integrate trustworthy AI as a material factor in standard investment processes.

Trustworthy AI is about opportunity, not only risk

One theme generated remarkable agreement across investor groups. Too much of the current conversation focuses exclusively on risk. Indeed, risk matters: investors need to understand potential harms, liabilities, governance failures and operational vulnerabilities. But risk alone rarely drives investment decisions. Investors also need a compelling view of the opportunity.

Companies that demonstrate strong AI governance may gain customer trust more quickly. They may win contracts in highly regulated sectors. They may face fewer operational disruptions and lower compliance and liability costs. Most importantly, they may be better positioned to scale sustainably as AI becomes embedded across the economy.

In this sense, trust can become a competitive advantage. Several companies are already treating AI governance as a business enabler rather than simply a compliance requirement. They use governance frameworks to strengthen customer relationships, support product development and differentiate themselves in increasingly crowded markets.

For investors, the opportunity lies not only in identifying risks to avoid but also in identifying business models that are likely to succeed because they build trust from the outset.

What role can policymakers play?

Policymakers cannot decide where private capital flows. But they can make it easier for markets to reward trustworthiness. Three areas stand out where public policy could make a difference:

First, policymakers can help create common signals in support of AI trustworthiness that reduce fragmentation across jurisdictions. Investors operate globally and benefit from consistency, in particular when it comes to capital markets and the policy frameworks enabling this.

Second, policymakers can help identify and communicate demand signals. Investors pay close attention to what governments, enterprises and citizens want from AI systems. As one example, clear procurement expectations from government departments regarding accountability, transparency, explainability and human agency can create stronger incentives for investment in trustworthy AI. Similarly, integrating trustworthy AI into state-led AI investment funding pools is another important market signal.

Third, governments can support greater transparency and disclosure, helping close the gap between what investors need and what companies currently provide. Existing initiatives, including voluntary reporting frameworks – such as the Hiroshima AI Process Reporting Framework – may provide useful starting points.

The OECD, GPAI and related international initiatives are well positioned to convene these conversations and help build a shared understanding between investors, companies and policymakers.

Shaping AI investment towards trustworthy outcomes

AI investment is no longer a niche issue for technology specialists. It is becoming a core question for economic policy. Investors are already shaping the future of AI through the capital they allocate. The challenge is ensuring that trustworthiness becomes as visible and measurable as growth, revenue or market share.

The foundations already exist: international principles, emerging standards, reporting initiatives and growing investor interest. What remains is translating these into practical and standardised tools that investment committees around the world can use to guide their every-day decisions.

If that gap can be closed, trustworthy AI will no longer be treated as an abstract aspiration. It will become an unquestionable material factor that markets recognise, reward and scale. And that may be one of the most powerful levers available for advancing trustworthy AI worldwide.