MORN · Morningstar, Inc.

We appreciate your feedback. We assess our reportable segments on an ongoing basis under applicable accounting rules (ASC 280). As of Q2 26, Morningstar Sustainalytics and Morningstar Indexes did not meet the ASC 280 qualitative or quantitative reporting thresholds to become a reportable segment.

Morningstar Direct desktop is a standalone solution for our core personas focused on managed investment due diligence as well as product marketing and distribution. We believe our proprietary managed investment database is a key differentiator and a source of truth in the heavily regulated industries we serve. Integrating our data, research, intellectual property (IP), and software allows us to offer sticky capabilities like Performance Reporting, which offers a flexible, robust calculation engine, as well as Presentation Studio, our reporting solution that supports industry presentations, fact sheet generation, and more. In addition, we generally see higher growth rates in our reporting options that distribute our data, similar to our data licensing segment.

As of June 30, 2026, Morningstar Credit had more than 600 credit rating analysts and analytical support staff.

We believe that helping investors navigate the convergence of public-private markets represents one of the most compelling opportunities for Morningstar. We are monetizing our efforts across multiple parts of the business. PitchBook: PitchBook continues to expand private credit data coverage, including business development company (BDC) data and research. A recently launched BDC report and analysis of BDC portfolio health has seen high engagement and contributed to growth in credit investor users. We have also steadily introduced enhancements to the Lumonic portfolio monitoring solution, such as a new Model Context Protocol (MCP) Library that includes portfolio review and valuation sheet artifacts, allowing users to integrate their full private credit portfolio data captured in Lumonic into AI-enabled workflows. PitchBook also continues to emphasize its proprietary intellectual property (IP) and analytics like Valuation Estimates, VC Exit Predictor and Time to Exit, and Credit Default

We also recognize that part of the Morningstar Direct product area (notably Reporting Solutions) is not strictly license-based. Is Reporting Solutions a small/mid/large share, and is it growing faster or slower than the license base? How much should we therefore use revenue-per-license as a proxy for price? Morningstar Direct growth was driven by increased revenue per license and expansion with existing clients in Reporting Solutions. We do not disclose the size and growth rates for each product, and do not provide forward-looking guidance on product mix or expected acceleration over time. Revenue reflects a variety of factors, including customer mix, product usage, contract terms, and adoption patterns, so we would not recommend revenue per license as a proxy for price. Morningstar Direct software continues to add value as a primary access point for Morningstar data and analytics, while increased demand to use these services off-platform drives growth in Reporting Solutions.

While we do not provide forward-looking guidance, there were no significant seasonal or comparable period-driven benefits contributing to the increase in PitchBook or Morningstar Direct organic revenue growth in Q2 26.

As client needs evolve, we continuously evaluate how we package, price, and deliver our data, research, and intellectual property (IP). With the emergence of AI-native workflows, clients are asking us for both a curated, integrated experience and more flexible ways to access Morningstar content. Many continue to value Morningstar Direct as a product that brings together our data, research, analytics, and workflows, which is why we're investing in new AI capabilities within the platform. At the same time, clients increasingly want to use our content in their own applications and third-party AI platforms, creating new opportunities to extend and monetize our data, research, and IP. We see these models as complementary, and our focus is on giving clients access to Morningstar content in the ways that best meet their needs, while preserving its quality, independence, and differentiation. We are focused on accelerating the pace of our IP launches and creating more connections between user

PitchBook Direct Data is priced separately from the platform license — by data volume, refresh frequency, and technical specifications — via application programming interface (API) or scheduled data feed. It's a small but growing extension of the platform that doesn't require a platform license to access. In practice, many PitchBook Direct Data clients also hold platform licenses, since PitchBook's proprietary intellectual property (IP) — VC Exit Predictor, Manager Scores, Valuation Estimates — is only available on the platform.

We continue to invest in the businesses to support growth with disciplined expense management, and we do not provide forward-looking guidance. We would note that the year-over-year change in adjusted operating income (AOI) margins has been relatively modest: Q2 26 Direct Platform AOI margin was 45.2% versus 46.0% in the prior year period, and PitchBook Q2 26 AOI margin was 30.3% versus 31.7%. We view these changes as largely reflecting the normal ebb and flow of operating the business and investing for growth. We monitor metrics such as licensed users and net revenue renewal rates, although some of these metrics may become less meaningful as pricing models change and as we move toward more firmwide, value-based pricing structures. We’ll continue to evaluate and consider enhancements to our disclosures. Examples of recent investments in Direct include expansion of our AI-ready data, supported investment types, portfolio X-ray tool, broader data distribution through cloud-native

In Q2 26, Morningstar Wealth organic revenue included a $5.8 million negative impact from the sunsetting of Morningstar Office and Morningstar Sustainalytics organic revenue included a $1.7 million negative impact from the sunsetting of the second-party opinions product. Excluding these sunsetting products, Morningstar Wealth organic revenue would have increased 5.3% and Morningstar Sustainalytics organic revenue would have decreased 1.6%. Both Morningstar Office and second-party opinions were retired in Q1 26. The sunsetting of these products will stop impacting year-over-year organic growth in Q2 27.

Fund Data, Essentials, and Research Distribution are the three largest products in Morningstar Data and account for approximately 70% of Morningstar Data revenue. These products continue to see momentum backed by new client use cases and data set expansion. Exchange market data drove slower revenue growth due to Q2 25 revenue benefiting from a late renewal with meaningful expansion along with lower retention in Q2 26.

Note that starting in Q2 25, Advisor Workstation includes revenue attributable to Direct Advisory Suite, but the quarterly supplemental decks prior to Q2 26 inadvertently excluded this impact. Organic revenue improved in Q2 26 due to continued strength with enterprise clients, as well as additional revenue recognized through new partnerships and the transition of previous Morningstar Office users to Direct Advisory Suite.

The 55% figure is a monthly active user (MAU) rate among PitchBook-licensed users who have ever accessed the premium connector. We selected MAU because usage on PitchBook varies by workflow: Some clients engage regularly, while others engage episodically, with usage tied to a specific deal, fundraise, or diligence process. Given that mix, a user’s activity in any particular month may not reflect their overall engagement with the platform, so we view MAU rate as one useful indicator rather than a comprehensive measure of adoption. Longer term, we're focused on measuring collective engagement across both the platform and Model Context Protocol (MCP), since that combined view better reflects how clients are actually working.

MCP connectors are currently monetized in three ways. First, we're paid by large language model (LLM) providers for essential/publicly-oriented data made available through their platforms, reflecting the quality and structure of that data. Second, paying PitchBook subscribers can access PitchBook data through premium connectors in platforms like Claude and ChatGPT at no additional charge today — though we have the ability to price for this separately as part of our broader pricing strategy. Third, we're piloting expanded premium connector access, which today still requires at least one full PitchBook platform license, with the ability to add connector-only seats. We're also testing a separate direct MCP capability, similar to an application programming interface (API), that is expected to use consumption-based pricing. We’re still evaluating the broader pricing structure and are taking a thoughtful approach. Notably, connector access does not extend to PitchBook's

We're evolving PitchBook's pricing to reflect how clients can increasingly derive value, not only through the platform, but also through data and intellectual property (IP) accessed via large language model (LLM) integrations and Model Context Protocol (MCP) connectors. This is fundamentally about capturing the value of the workflow and IP layer, not just data access: Proprietary tools like the VC Exit Predictor, Manager Scores, and Valuation Estimates remain platform-exclusive, and that's deliberate, because the PitchBook platform is where we believe switching costs are highest. We expect that some clients prefer seat-based licenses while others are shifting toward value- or consumption-based models. We intend to price to capture the value delivered across both and we're early in this transition. Clients have told us pricing certainty matters, so we're testing structures that can balance flexibility with predictability.

We're actively evaluating a more modular approach to how PitchBook is packaged and priced, informed by how clients are increasingly accessing our data: through the platform directly, through direct data/application programming interface (API) feeds, and increasingly through large language model (LLM) and Model Context Protocol (MCP) connector integrations. As discussed at the Annual Shareholders’ Meeting in May, we've already introduced elements of this approach, including essential data licensing to LLM providers and premium connector access for paying subscribers. Additionally, we’re piloting expanded premium connector access, which today still requires at least one full platform license, with the ability to add connector-only seats. We're also testing a separate direct MCP capability, similar to an API, for consumption-based use cases. We’re still evaluating the broader pricing structure and are taking a thoughtful approach.

In global credit markets, there are a variety of different long- and short-term debt programs where frequent issuers or program sponsors seek greater predictability of rating fees independent of their annual debt issuance or reduced fee levels when actual issuance volumes exceed certain threshold limits. In many instances, credit rating agencies including Morningstar Credit offer lower issuance fees or capped issuance fees in return for higher annual surveillance or higher program fees. Morningstar Credit offers such program fee structures for frequent issuers in select market segments ranging from structured finance master trusts to large scale fundamental issuers such as financial institutions and sovereigns.

We continue to focus on aligning price with the overall value that our clients receive. MCP connectors give us greater visibility into how clients interact with and derive value from our content, and we take those insights into consideration as we evaluate our pricing approach. As we’ve noted, we track access to MCP at the user level, although we do not disclose additional individual engagement metrics tracked.

We are focused on making our software more AI native at its core, expanding beyond the existing assistants. Morningstar's extensive data, intelligence, and research give us an opportunity to leverage AI to curate a simpler and easier-to-use set of tools, unique and differentiated agents, and native use of our unique intellectual property (IP). We’ve introduced several AI-enabled tools within the Direct platform. For example, AI Assistant, which is used for natural language screening, filtering, fund selection, performance & risk analysis, and more, has seen strong initial engagement. On the advisor side, AI Assistant is available in beta to a small subset of Direct Advisory Suite users for natural language screening, portfolio analysis, research, client management, and proposals. We’ve also introduced a coding assistant, which offers programmatic access to Morningstar Data, as well as AI synthesis of our proprietary Risk Model. In our content licensing business,

Historically, we have used seat growth to measure incremental client value for products like the PitchBook platform and Morningstar Direct, although a large portion of the Direct Platform business is not seat-based. Value-based pricing establishes a fee level (typically an annual license fee) that reflects business impact, expected buyer return on investment, and/or realized outcomes without considering the number of seats. Consumption-based pricing links client fees to data consumption (e.g., application programming interface (API) calls) and customers pay for the amount of data they consume, so these fees inherently vary from period to period. As customers adopt tools and technology to execute more work with the same (or smaller) workforce, consumption-based models may better capture the value of our data and intellectual property (IP) in certain businesses and customer segments. For example, we see increased adoption of AI-powered tools and technology for deal sourcing among

Consistent with recent quarters, Q2 26 licensed user growth continued to be driven primarily by expansion within our existing core client base — private equity, asset management, and commercial banking — rather than new logo additions alone, reflecting deeper embedding across teams and use cases at the accounts we already serve. We’ve continued to see softness in the venture capital and corporate client segments. Venture capital continues to see lower retention and challenges in expansion efforts, and corporate clients tend to have more limited and budget-sensitive use cases. Our core investor and advisor segments have stayed more resilient.

We believe that the best way to serve our clients depends on their specific needs. On platform, we’re able to offer targeted workflows and certain proprietary intellectual property (IP) not available elsewhere. Meanwhile Model Context Protocol (MCP) connectors and other AI-native experiences provide additional meaningful opportunities for clients to engage with and extract value from our content. In our experience to date, we’ve found that many clients have been interested in leveraging both MCP and platform access. In each case, we maintain the licensing relationship with our clients so we believe that we can continue to align price with the value that our content delivers.

PitchBook has used AI in data operations for years. Our hybrid AI/human intelligence model — AI-powered ingestion, classification, and entity resolution paired with human validation and primary research — allows us to source and structure data from hundreds of thousands of sources daily, many of which are not publicly available. In the AI era, the human research layer can become even more valuable because it provides the training signal and quality benchmark that help keep AI-driven outputs accurate. Our data operations team realizes substantive efficiency gains each year. That said, AI's standalone margin contribution is difficult to isolate, while savings from the efficiencies we’ve realized have largely been redeployed into expanding coverage and new datasets rather than flowing straight through to margin. This is consistent with our philosophy of building our data moat for long-term growth.

While we do not share a target leverage ratio or return threshold, our long-term capital allocation priorities remain consistent: preserve a strong balance sheet; invest in growth and value-creating acquisitions; grow our dividend over time; and repurchase shares opportunistically. In 2026, we’ve prioritized share repurchases, reflecting our optimism about our prospects and ability to compound value in the business, reducing our shares outstanding by 5.6% year-to-date through the end of June.

We are not in a position to comment on formal requirements within our clients’ home offices. That said, we see Morningstar data, research, and intellectual property used widely in the due diligence process at firms representing significant assets under management, including by gatekeepers at many of the largest US wealth management firms. Our data and software are commonly available to advisors and researchers at these firms for use in the investment decision-making process. Our research and ratings can meaningfully influence the investment decision-making process. Our Morningstar Rating (or “star rating”) is widely used in fund marketing materials, and past research has shown a correlation between star ratings and fund flows. We also see good home office engagement with the analyst-driven Morningstar Medalist Rating, which provides a forward-looking assessment across mutual funds, exchange-traded funds, and other managed investments, including semiliquid funds and