Investor Questions and Answers: August 25, 2026 We encourage current shareholders, potential shareholders, and other interested parties to send questions to us in writing and we make written responses available on a periodic basis. The following answers respond to selected questions received through August 5, 2026. We retain the discretion to combine answers for duplicate or similar questions into one comprehensive response. If you would like to submit a question, please send an e-mail to investors@morningstar.com or write us at the following address: Morningstar, Inc. Investor Relations 22 W. Washington St. Chicago, IL 60602 Use of Non-GAAP Measures These Investor Questions and Answers reference non-GAAP financial measures, including but not limited to, adjusted operating income margin and organic revenue. These non-GAAP measures may not be comparable to similarly titled measures reported by other companies. Reconciliation of non- GAAP financial measures can be found at https://shareholders.morningstar.com/financials-stock- info/key-financials/default.aspx. Artificial Intelligence 1. As MCP connectors expand your distribution and addressable market, what is the risk that AI- native interfaces (e.g., Claude, Perplexity) increasingly disintermediate Morningstar in the distribution layer of the value chain, and how are you thinking about defending pricing power in that scenario? 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.
2. Do you anticipate an unbundling of the Direct offering, where AI-native platforms control the interface and clients purchase narrower, more targeted subsets of Morningstar data rather than a full license? 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 segments such as advisors and asset managers. 3. Are there AI-enabled tools within the Direct platform today, and do you expect these features to support pricing above historical trends, or in line with the past? To what extent do you view these AI capabilities as a source of incremental, monetizable value versus "table stakes" that clients now expect, given peers are rolling out similar functionality? 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, clients are increasingly expecting AI-native delivery of our data, research, and IP to plug in seamlessly to the new tools they’re using and experiences they’re building. Our approach is to meet clients where they are so that it’s easy to power their AI tools with our AI-ready content and services.
It’s true that some basic AI features are expected. However, we believe that pairing AI technologies with Morningstar’s differentiated data, research, and IP as we draw on our firsthand knowledge of our clients’ compliance requirements and how they use our products, can help clients move faster with more confidence. As we incorporate AI to support our goal of embedding our content more deeply with clients and accelerating their workflows, we will continue to look for opportunities to align price with the value we deliver. 4. As you develop consumption-based pricing, what additional usage information will the Model Context Protocol (MCP) connectors give you (beyond tool call volume) to inform those pricing decisions? 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. 5. Kunal’s letter in Q2 referenced experimenting with consumption-based models. What is the impetus for this? Is the seat-based model no longer providing revenue commensurate with value? How do you distinguish between value-based and consumption-based pricing, and how will that distinction shape your approach? For example, one user may consume data infrequently yet derive significant value, while another may pull large volumes of more replicable data – how do you reconcile the two? 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 general partners and M&A diligence workflows at investment banks. This adoption coincides with increased use of our data and IP in these tools. As a result, our approach to commercialization may evolve to more appropriately monetize our content. We are working with our clients to match price to the value delivered from our data, research, and scope of our enterprise relationships.
6. AOI Margins in Direct and PitchBook have compressed y/y in Q1 and Q2. The compression appears to be driven by increased compensation costs along with increased technology infrastructure costs such as AI initiatives and cloud migration. How prolonged could this be and how much of the increased spend is related to the product/data roadmap for AI? When can we expect the current investment phase to translate into a measurable revenue and margin contributor? What KPI's can we track? 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 environments such as Snowflake, and new turnkey integrations with Perplexity and Microsoft. We also launched our first publicly available skills (specialized capabilities that enable AI-driven workflows), making it easier for investors using our Model Context Protocol (MCP) server to conduct full investment screening and investment comparison workflows. In PitchBook, recent investments include daily valuation estimates covering more than 15,000 VC-backed companies, late-stage private-company research, the LCD Default Predictor, StepStone deal- level benchmarks, and expanded AI distribution through Navigator and third-party integrations. 7. At the investor day there were comments made on evolving the pricing model for PitchBook. How do AI-enabled products and data fit into this model? Are customers looking for more value at the current price point or are they willing to pay more to get access to the company's AI-enabled products and data? 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. Capital Allocation 8. Following the CRSP acquisition, how are you prioritizing debt reduction, incremental M&A, dividends, and buybacks—and what leverage or return thresholds govern those choices? 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. Morningstar 9. In Q2 26, the earnings release stated that "Excluding the impact of the sunsetting of Morningstar Office in Morningstar Wealth and the second-party opinions product in Morningstar Sustainalytics, organic revenue would have increased 8.2%". How should investors think about quantifying the headwinds associated with each product along with the timing of when these headwinds will start to fade? When do the sunsetting products (SPO and Morningstar Office) exit the organic growth calculation? 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. 10. PitchBook and Direct accelerated this quarter, should this acceleration continue or was this seasonal/comp related? 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.
11. How is Morningstar monetizing its public-private efforts? 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 Predictor supporting key deal sourcing and deal execution use cases. Collectively, these efforts are designed to drive greater demand for PitchBook and its data as we can better support our clients’ core use cases. - Morningstar Credit: Credit ratings referencing the private markets have been an important driver of Morningstar Credit’s growth over the past few years, driving roughly a quarter of credit ratings revenue in 2025 and we continue to see good long-term growth opportunities in this space. - Morningstar Wealth: We are expanding our multi-asset model offering to include a suite of public/private portfolios in collaboration with Apollo, Franklin Templeton, and J.P. Morgan Asset Management that give financial advisors a single, research-supported avenue to access private markets. - Morningstar Retirement: We are consulting with private asset managers to help them determine the appropriate liquidity levels and structure for private investments used within defined contribution retirement plans. We anticipate that some registered investment advisors will begin incorporating private investments in Advisor Managed Accounts, driving greater engagement and asset growth. - Morningstar Direct Platform: We’ve expanded coverage on semiliquid funds and have seen growth in our data licenses for semiliquid fund data and ratings, including both existing clients expanding the scope of their licenses, and new client types who have traditionally served only the private market space. As we continue to add semiliquid fund data and analytics designed to better reflect portfolio composition, including private market exposure to our software products, we have seen installations in new user groups within our advisor and asset manager markets. - Morningstar Indexes: We have added licenses for Morningstar PitchBook Private Market Indexes, including the Unicorn series, Modern Market 100, and Evergreen Fund Index.
Morningstar Credit 12. How many credit rating analysts work in Credit? As of June 30, 2026, Morningstar Credit had more than 600 credit rating analysts and analytical support staff. 13. Some of the other credit rating agencies have frequent issuer programs for their clients to take advantage of. Does Morningstar Credit have a similar type of program and if so, how do the economics compare relative to other issuance types? Are you increasingly seeing clients utilize these programs as the business continues to build momentum in the marketplace? 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. Morningstar Direct Platform 14. Morningstar Direct licenses have been roughly flat (down ~0.6% YoY in Q2 26), yet Morningstar Direct organic revenue grew ~7% YoY, implying mid-to-high-single-digit revenue-per-license growth. What are the primary drivers of this pricing power? How sustainable is this level of price/mix growth, and do you expect license growth to reaccelerate over time? 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.
The mix over time is driven by client preferences, deployment models, and usage patterns with the overall business value to our clients captured in total revenue. 15. How is the Morningstar Direct desktop positioned with its typical users – is it a primary, standalone solution, or more often used alongside complementary platforms such as Bloomberg, LSEQ Workspace, FactSet or S&P Capital IQ Pro? Where do you see Direct's strongest and most defensible differentiation? 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. 16. Can you give an update on the mix of Morningstar Data? Why was organic revenue growth slower in Q2 compared to the prior quarter? 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. 17. Why did organic revenue growth in Q2 improve in Advisor Workstation as compared to the prior quarter? 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.
PitchBook 18. In the Q2 letter you noted that, from the late-2025 launch through mid-July, roughly 20% of PitchBook accounts had accessed the PitchBook Premium connector, and that June monthly active users represented ~55% of all users who had ever accessed the connector. How should we interpret the ~45% who accessed but were not monthly-active in June – is that a normal trial-to-adoption curve? 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. 19. Is there an update on offering PitchBook via a more modular commercial model, and how do you expect that pricing structure to take shape? 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. 20. In Q2, PitchBook had a small increase in y/y license growth as compared to the prior 3 quarters. Can you share more color on any changes you've observed in terms of traction with clients? Are there any details you can share on what types of users are driving the growth in PitchBook licenses, are these new client wins or greater penetration with existing clients? How has client sentiment trended over the last three months and are there any notable trends within the venture capital and corporate client segments?
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. 21. How is the PitchBook Direct Data offering monetized? Are users required to be PitchBook license customers first? 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. 22. How is AI impacting PitchBook data gathering and processing? Why haven’t we seen much improvement in PitchBook margins given AI should have helped drive efficiency in this effort? 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. 23. How are PitchBook Model Context Protocol (MCP) Connectors monetized today, if at all? 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 proprietary intellectual property (IP). Tools including the VC Exit Predictor, Manager Scores, and Valuation Estimates remain available only within the platform itself. Segment Disclosures 24. Given the recent index acquisition and the smaller footprint of the wealth business, will the index business be broken out as its own business line sometime in the future? Including it in Corporate and All Other does partly obscure a business of growing importance. 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.