We believe Morningstar has a durable moat and is positioned to benefit as AI tools proliferate. Our moat comes from four distinct but reinforcing capabilities that we have built over decades: data provides the foundation, research applies judgment, intellectual property creates a shared language of investing, and software allows our clients to leverage those insights to scale their workflows.
Data
Our foundation is built on large, differentiated, human-curated datasets across public and private markets, built through sourcing and transformation methods that, on the whole, we believe are difficult to replicate with the rigor and reliability that investors demand. In private markets, data is opaque, unstructured, and updated inconsistently, which limits the usefulness of AI without significant human involvement. Details such as timely and robust fund performance, deal valuations, cap table history, and financing terms are typically not available on the internet and cannot be scraped. We develop our proprietary datasets based on millions of raw data points, refined through primary surveys, proprietary league tables, Freedom of Information Act responses, and journalist-led research.
Meanwhile, in the public markets, where investors face an increasingly complex universe of investment options, our data can provide structure and consistency. We aggregate data across more than 10,000 different sources, building on more than 40 years of data relationships, employing industry-standard methodologies, and proven quality assurance processes. We link data across asset classes (including private market data) and investment vehicles with common data definitions aimed at helping investors more easily compare investments across vehicles and investment types such as open-end and exchange traded funds and individual securities.
Research
Our research teams play a critical role in producing original research, interpreting data, and in creating new datasets and analytics. For example, as we expanded our coverage of semiliquid investments, our analysts produced foundational research, such as our State of Semiliquid Funds work intended to help investors contextualize and evaluate this growing segment. They also worked alongside data and product teams to define underlying data points and analytics to support investor decision making.
Similarly, our PitchBook analysts bring their expertise to bear on emerging parts of the market, with a focus on asking the right questions of market participants, identifying what data sources are credible, and translating that judgment into actionable insights before a consensus emerges.
In an AI-enabled world, we believe our combination of human insight and AI for scale and speed becomes more important, not less.
Intellectual Property
Our proprietary frameworks—including ratings, methodologies, and classification systems—create a shared language of investing that is widely recognized and embedded across the industry. Examples include Morningstar Categories, Medalist Ratings, Economic Moat Ratings, Portfolio Risk Scores, as well as PitchBook’s VC Exit Predictor, Manager Performance Scores, and Valuation Estimates. This intellectual property seeks to transform raw information into insights investors can understand and act on, and we believe it becomes more valuable as it is applied consistently across products, markets, and time.
Software and Technology
Our software platforms and AI-enabled tools embed our data, research, and IP directly into clients’ daily workflows, from investment selection and due diligence to monitoring, reporting, and risk oversight. We believe these integrations create switching costs and reinforce our role as a trusted partner. As clients look to automate more of their processes, we view our ability to combine AI with differentiated content and workflow driven products as an important opportunity to strengthen our moat. Recent AI product enhancements include the launch of PitchBook Navigator, and AI assistants in Morningstar Direct and Direct Advisory Suite.
Beyond our own platforms, we provide AI-enabled access through large language models and our clients’ internal tools, allowing our insights to flow through to firm-specific workflows. We have collaborations with leading AI platforms including OpenAI’s ChatGPT, Anthropic’s Claude for Financial Services, Microsoft’s CoPilot Studio and Foundry, and Perplexity.
Ultimately, we believe that in an AI era, AI models are only as good as the data that they are trained and grounded in; as a result, our curated datasets only become more valuable. We believe our competitive moat is not meaningfully measured by the ratio of proprietary-to-public data, but rather by the enrichment, curation, and context layered on top of raw data. That combination is what clients pay for and what we believe our competitors cannot easily replicate.
We are looking forward to sharing more on this topic in our Annual Report and at our upcoming Annual Shareholders’ Meeting on May 7.