Editorial illustration for Investors Lack Clear Data on Corporate AI Use, Study Finds
Investors Demand AI Data on Corporate Impact
Investors Lack Clear Data on Corporate AI Use, Study Finds
Wall Street can't get a straight answer on what AI is actually doing to corporate balance sheets, and that gap is starting to worry the people writing the checks. The 2026 Stanford Human-Centered AI Index Report puts global corporate AI investment at more than double 2025 levels, with productivity gains reported at 15% in customer support and as high as 50% in marketing output. Those numbers sound impressive until an investor tries to compare them across a portfolio, because the risks attached to AI, from labor disruption to environmental costs to a growing tally of documented AI incidents, don't show up in any standard filing.
The IPOs of SpaceX, OpenAI, and Anthropic have made the stakes obvious. Investors want in, but they're pricing companies with little precedent and even less disclosure about how AI actually factors into revenue, risk, or long-term strategy. Traditional financial statements were built for a world of steadier, more predictable business models.
AI's effects shift by sector, by company, by region, and existing reporting standards weren't designed to capture that. That mismatch is now pushing investors to ask for something more specific than what they're currently getting.
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.
Why this matters
For founders and research leads courting institutional capital, this is a signal worth reading closely. PAI isn't asking for new regulation, it's pointing to existing disclosure frameworks and saying: use them to explain your AI exposure, spending, and risk in terms investors can actually price. That's a lower bar than compliance teams might fear, and a real opportunity for companies willing to get specific about model dependencies, compute costs, or where AI sits in their revenue story.
Right now the market is pricing OpenAI, Anthropic, and SpaceX-style bets largely on narrative, and PAI's research suggests that's a data problem as much as a hype problem. If you're building a company that leans on AI for its core value proposition, expect investors to start asking sharper, more standardized questions about how that AI actually functions inside your business, not just that it exists. Waiting for regulators to force the issue seems like a bad strategy.
Companies that get ahead of this, disclosing AI use the way they'd disclose supply chain risk or R&D spend, will likely have an easier time raising capital than those still hiding behind "AI-powered" as a pitch-deck slide.
Related Reading
Business & Startups Nordic pilot adds Gemini for Education, NotebookLM to boost AI literacy
Business & Startups Kling launches Video O1, all-in-one model with MVL bridge using transformer
Business & Startups DeepSeek Seeks More Capital Weeks After USD 7B Funding Round
From Archives Instacart built an AI system trained on years of its own incident data
From Archives Anthropic CEO: Open-weight AI models carry heightened biological risks
Common Questions Answered
What does the 2026 Stanford Human-Centered AI Index Report reveal about corporate AI investment levels?
According to the report, global corporate AI investment is projected to more than double from 2025 levels in 2026. While companies report impressive productivity gains ranging from 15% in customer support to as high as 50% in marketing output, investors struggle to compare these metrics consistently across different portfolios due to lack of standardized reporting.
Why are investors concerned about the lack of clear data on corporate AI use?
Investors cannot get consistent, comparable information about what AI is actually contributing to corporate balance sheets across their portfolios. This data gap makes it difficult for them to accurately assess and price the risks and returns associated with corporate AI investments, which is becoming increasingly important as AI spending doubles.
What does the Partnership on AI recommend instead of creating new AI reporting standards?
Rather than establishing entirely new reporting standards, the Partnership on AI recommends that companies use enhanced guidance to apply existing reporting standards in the context of AI. This approach provides a lower compliance bar for companies while enabling them to explain their AI exposure, spending, model dependencies, and compute costs in terms that investors can actually price.
What specific AI-related information should companies disclose to institutional investors?
Companies should provide detailed disclosures about their model dependencies, compute costs, and where AI sits within their revenue generation and operations. By getting specific about these factors using existing disclosure frameworks, companies can help investors understand their AI exposure and associated risks more clearly.
Further Reading
- Corporate AI Governance Report 2025 | AICDI Global - Reuters Foundation / Trust
- Corporate AI: Driving transparency on adoption - Reuters Foundation / Trust
- Data problem slows corporate investment in AI - Global Venturing
- MIT study on AI profits rattles tech investors - Axios
- Artificial intelligence in EU investment funds: adoption and disclosure gaps - European Commission / ESMA
