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Investors Need Better Information About How Companies are Using AI
PAI’s recommendations help companies use existing disclosure standards to report material information on AI
The IPOs of companies like SpaceX, OpenAI, and Anthropic promise to upend financial markets and generate eye-popping returns for many investors. However, the volatility and uncertainty surrounding these companies underscore another challenge: investors are struggling to value the AI transformation, and to account for how its impacts, risks, and opportunities will appear in a company’s bottom line.
As AI is transforming industries, from manufacturing to services, investors are looking beyond these major cases to assess how it will reshape business and society, what the major risks are, and how they should account for these unprecedented changes in portfolio management. But the effects of AI are complicated and difficult to predict. They also vary widely across sectors, companies, and regions.
According to the 2026 Stanford University Human-Centered AI Index Report, global corporate investment in AI more than doubled in 2025, with productivity gains ranging from 15% in customer support to 50% in marketing output. At the same time, the risks are broadening, from workforce disruption to environmental footprint, along with an ever rising count of documented AI incidents.
In conditions of greater certainty, investors and providers of capital can evaluate a company’s prospects and price its value using financial filings and independent research. But to evaluate how companies are responding to complex, far-reaching developments such as AI, they need information beyond what’s included in traditional financial filings.
Investors need to understand how risks and opportunities show up in a company’s operations, products, and value chain. This includes risks related to nebulous issues like public accountability, fast-moving regulation, liability, and workforce transformation. Investors are looking to evaluate how these issues are overseen and managed in a company’s governance structure. They also need to understand a company’s overall strategy, where it sees challenges or competitive advantages, and how its approach compares to competitors.
Having this information will help investors price in risks and opportunities that don’t appear on a company’s balance sheet. And it can give them better insight into which companies have the leadership, strategy, and guardrails to navigate the uncertainty of AI responsibly—to generate financial returns without potentially catastrophic organizational or societal disruptions.
Better AI disclosures also benefit other stakeholders. Governments, insurers, civil society organizations, and safety researchers all need high-quality information from companies to make informed decisions. Some of this information is already included in financial and sustainability reports, but much deeper insights are needed than are currently disclosed.
Reporting Standards
Over the past 25 years, several interoperable standards have emerged to define how companies should report on this broader set of impacts, risks, and opportunities. Most prominent are the SASB Standards, the ISSB Standards (IFRS S1 and S2), the European Sustainability Reporting Standards (ESRS), and the Global Reporting Initiative (GRI) Standards.
These standards are increasingly mandated by law, integrated into stock exchange listing requirements, or accepted as best practice. According to KPMG, 96% of the world’s largest 250 companies report on their sustainability impacts, with SASB standards particularly dominant among companies in the Americas, and a growing number of companies preparing to adopt the ESRS.
Companies do not need a new reporting standard to improve their AI-related disclosures. They do need enhanced guidance on how to apply existing reporting standards in the context of AI.
Draft Disclosure Recommendations
Today, PAI is publishing a draft of Disclosure Recommendations to help companies identify material information about the impacts, risks, and opportunities of AI to include in their financial and public reporting. These Disclosure Recommendations are:
- Industry-agnostic, but designed to capture nuances from domains in which AI is deployed, such as healthcare, financial services, retail, infrastructure, energy, social media, and the public sector.
- Intended to improve the quality of corporate-level information that informs decision-making, such as capital allocation decisions.
- Structured to support, rather than add to, the requirements of existing reporting standards.
- Designed to be used alongside the Corporate AI Risk Assessment Framework that we published earlier this year. That framework defines what good corporate governance and management look like, while these recommendations help companies report externally.
Investor-focused reporting is one element in the larger field of AI transparency. System cards, regulator-required reports, and incident reports are other tools, aimed at different audiences, that all support the goal of increased trust and transparency.
To develop these recommendations, PAI consulted with a mix of investors, companies, civil society organizations, and other experts. We plan to refine them over time based on feedback and as reporting practices evolve.
We welcome feedback on these draft Disclosure Recommendations and the opportunity to work with companies, investors, and other stakeholders interested in testing them. For more, please get in touch with Sam Wallace.
Facts Only
* Global corporate investment in AI more than doubled in 2025, with productivity gains ranging from 15% in customer support to 50% in marketing output.
* Risks associated with AI are broadening, including workforce disruption and environmental footprint.
* Investors require information beyond traditional financial filings to evaluate company responses to complex developments like AI.
* Investors need insight into how risks and opportunities appear in operations, products, and value chains.
* This includes risks related to public accountability, fast-moving regulation, liability, and workforce transformation.
* Companies need guidance on applying existing reporting standards within the context of AI.
* PAI is publishing draft Disclosure Recommendations to help companies report material AI impacts.
* The recommendations are industry-agnostic and designed to support, rather than add to, existing reporting standards.
* The recommendations are intended to be used alongside a Corporate AI Risk Assessment Framework.
* Reporting standards include SASB, ISSB (IFRS S1 and S2), ESRS, and GRI.
Executive Summary
Investors face challenges in valuing the impact, risks, and opportunities associated with the transformation driven by Artificial Intelligence across various industries. While significant investment in AI has increased global productivity gains, the associated risks—including workforce disruption and environmental footprint—are also broadening. To properly assess corporate prospects, investors require information beyond traditional financial filings to understand how AI effects manifest across operations, products, and value chains.
The article suggests that investors need clarity on intangible factors such as public accountability, evolving regulation, liability, and workforce transformation, and how these are managed within a company's governance structure. To achieve this, companies should enhance disclosures by applying existing reporting standards like SASB or ISSB to the context of AI. A set of draft Disclosure Recommendations is being published to help companies report material AI-related information. This information benefits investors, as it allows for better risk and opportunity pricing, and also serves other stakeholders such as governments and insurers seeking informed decisions.
Full Take
The core tension in this narrative lies between the rapid technological evolution of AI and the slow, evolving framework of financial disclosure. The shift required is not merely about adding a new line item but fundamentally redefining what constitutes material information when intangible risks—like accountability or societal disruption—drive value. The focus on integrating existing standards rather than creating new ones reflects an attempt to navigate inertia while addressing novel complexity.
The pattern observed is the recognition that financial statements, traditionally backward-looking and focused on tangible assets, are insufficient for capturing dynamic, systemic risks introduced by technology like AI. This necessitates a convergence between financial accounting and sustainability/governance reporting. The suggestion that investors need visibility into governance structures regarding nebulous issues like liability suggests an implicit critique of current governance mechanisms, implying that accountability gaps exist precisely where the greatest unforeseen risks reside.
This framework points toward a potential systemic failure: if capital allocation decisions are based on incomplete risk assessments, the resulting portfolio management could inadvertently exacerbate societal or organizational disruptions. The challenge for analysts is moving from understanding *what* companies report to understanding the *quality* of the governance structures that generate that reporting. What assumptions about regulatory capture or information asymmetry fuel the current reliance on external, voluntary standards when accountability ultimately rests within corporate control? What happens if the agreed-upon reporting standards are themselves subject to the same speculative forces AI is reshaping?
Sentinel — Human
The article functions as a high-level analysis advocating for enhanced corporate disclosure standards regarding AI risks and opportunities, supported by references to existing global reporting frameworks.