Decision-making and trust
In an era of information overload, investors are clear about where trust begins. Financial statements and investor-focused communications remain the anchors of decision-making, with 69% and 64% of respondents, respectively, relying on these inputs to a large or very large extent.
Traditional sources still matter. Many investors rely on analyst reports (58%) and credit ratings or other third-party data (both 52%), though GenAI (34%) and alternative data (36%) are used selectively. The message is not anti-innovation—it’s pro-credibility. Investors want numbers they can test, governance they can understand, and a narrative that connects strategy to cash flows.
At the same time, trust increasingly depends on what companies can show about the future. Investors want to see companies create value and control risks, especially in AI. Here the disclosure gap is pronounced. Only 37% of respondents say companies disclose information about AI strategies and policies “completely” or “to a large extent.” Satisfaction is similarly limited for AI governance (34%) and performance (35%), and lowest for headcount impacts (23%). This matters, because expectations for AI-driven value are specific: most investors look for cost reduction (64%), operational agility (54%), and new business models (42%). Without evidence—such as key performance indicators (KPIs) that tie AI programmes to productivity, margins, and revenue—investors hesitate to fully price these benefits. And transparency drives value.
What enables companies to close the gap is as much about leadership as it is about technology. Many investors point to executive advocacy and AI fluency as critical. These elements must be backed by strong data quality, secure architectures, and disciplined change management. Those capabilities create a trust architecture that makes disclosures more credible and outcomes more repeatable—exactly what investors need if they’re to incorporate forward-looking narratives into valuation.
