Valuations stabilize after AI fears; Morgan Stanley favors TRU, SPGI, MSCI, and EFX

Overview

This report reviews changes in information services industry share prices and valuations following AI-disruption concerns, and reassesses the sector in conjunction with first-half 2026 results, AI product progress, and competitive risks. Morgan Stanley believes the market has largely priced in AI risks, fundamentals remain sound, and favors TRU, SPGI, MSCI, and EFX.

Core views

The report argues that the information services sector became a focal point for AI-disruption concerns beginning in June 2025. Over the year through June 30, 2026, sector stocks declined by an average of 36%, while the S&P 500 rose 21%. Share prices subsequently began to stabilize: through August 18, the sector increased by an average of 11%, compared with a 3% gain for the S&P 500, led by IT, TRI, and FDS, which had previously been hit the hardest. The institution believes the recent de-rating was excessive and that potential AI disruption has been more fully reflected in share prices; the sector currently trades at 14x forward 12-month EV/EBITDA, significantly below its five-year average of 21x, while TRU, SPGI, EFX, and MSCI valuations are each 1 to 2 standard deviations below their five-year averages. Earnings provide the fundamental basis for this view. Among covered information services companies, 80% reported revenue above market expectations, 70% reported adjusted EBITDA above expectations, and 90% reported adjusted EPS above expectations. Most companies maintained full-year guidance in the first half of 2026; companies making revisions primarily raised guidance: TRI and FDS increased full-year revenue targets due to strong industry or segment performance, TRU and EFX raised revenue guidance while maintaining organic growth guidance, and IT lowered revenue guidance primarily because of foreign exchange rather than operating factors while raising adjusted EBITDA guidance due to strong first-half performance. The institution notes that the AI debate itself has not fundamentally changed, but positive earnings have pushed the market's assumed timing of potential disruption further out. Because investors may attribute any weakness in KPIs, core business revenue, or adjusted EBITDA margins to AI, future earnings for every company may still be significant catalysts. Over the long term, the report expects the industry to be a net beneficiary of generative AI: companies can achieve growth and productivity gains through new product development, improved data ingestion, and operational efficiency; proprietary data, established brands, and products requiring “decision-grade” accuracy can help defend against AI substitution. In the first half of 2026, AI tools accounted for a larger share of new contract value, and corporate discussions shifted toward MCP connectivity, token consumption, and efficiency gains. FDS stated that more than one-third of its non-compensation expense growth came from investments in core infrastructure and token costs; programming-related token usage grew 5x quarter over quarter, while committed usage of AI-generated lines of code nearly increased 10x. More than 20% of its top 100 customers have paid for MCP, and one large hedge fund expanded its business with FDS 6x after connecting to MCP. MCO has more than 100 MCP/Smart API connections in use or trial, with early paid adoption by major banks. Product development and customer adoption also support the view that AI commercialization remains at an early stage. TRI stated that generative AI products accounted for 32% of its ACV; MSCI launched more than 80 new products over the past two quarters, compared with only more than 40 for all of fiscal 2024. Since launching IndexAI Insights in February, MSCI has seen adoption by more than 1,000 customers, with hundreds of customers using its online integrations across major large language models; VRSK's XactAI users have increased to 7,000 licenses since March, 10x the prior level. Among FDS's top 50 clients, 90% use more than four AI products, client AI usage rose more than 85% quarter over quarter, ASV for AI clients grew more than 50% faster than for non-AI clients, and AI SKU products contributed more than 10% of total ASV growth. On risk assessment, the institution's view on competitive risks is unchanged: AI-native tools and technology alternatives lower barriers to entry, and areas lacking differentiated assets or relying on commoditized data are especially likely to face more competitors and client-built alternatives. However, incumbent companies can use cash flow to reinvest, accelerate development, enhance data in real time, and deepen content; products with weaker proprietary data face greater competitive pressure. To alleviate this concern, evidence is needed that AI disruption is not translating into customer losses or weaker growth, including accelerating new contracts, improved retention, a rising share of contract value from AI-enabled products, higher usage, and improving organic revenue and forward-looking KPIs. The institution is somewhat more constructive on near-term business model risk. MCP connections allow incumbent platforms with proprietary data to embed differentiated data into customers' preferred large language models and workflows, thereby maintaining relevance as interfaces change, reinforcing data moats, and reducing the risk of being bypassed by AI-native competitors. The report has not heard examples of customer-count impacts materially affecting enterprise contracts; contract renewals continue to reflect the value created by modern tools and unique data, and providing MCP access can in many cases increase contract value through additional charges. Although some companies have longer sales cycles, others have seen longer contract terms after implementing AI products; as usage reaches a certain scale, enterprises are considering consumption-based pricing, though it is not yet necessary to implement. Margins are the area where risks have become more prominent. As AI investment and token consumption accelerate, efficiency gains from automation are reinvested in technology rather than flowing directly to profits, making it difficult for the market to quantify savings. The report believes AI spending will become a structural component of the cost base; the key question is whether investment and token consumption will outpace productivity gains, thereby suppressing future margin expansion. TRI's fiscal 2026 guidance for 100 basis points of margin expansion had already drawn attention, and weaker third-quarter guidance heightened concerns that token costs, CoCounsel marketing, and Thomson LLM investment could cause expansion to fall short of expectations; if elevated investment persists or accelerates, margin expansion from the near term through fiscal 2027 could be more moderate. At the stock level, TRU trades at 10.0x 2027 EV/EBITDA. The institution considers its healthy consumer credit trends, upside from a mortgage recovery, strong underlying margins, regulatory and proprietary data barriers, and innovation and productivity opportunities from TruIQ and OneTRU attractive; it expects its credit bureau business to outperform over the coming years, with adjusted EPS nearly doubling by 2028. Approximately 65% of SPGI revenue and approximately 80% of profit come from its resilient benchmarking business. The institution expects high-single-digit revenue growth, 50 to 75 basis points of margin expansion, and low-double-digit EPS growth, and believes its approximately 5-turn 2027 P/FCF valuation discount to MCO is supportive. MSCI has 9% 2024-to-2028 organic revenue CAGR, an approximately 61% EBITDA margin, and accelerating new subscription business; its 19.8x 2027 EV/EBITDA valuation reflects its higher-quality growth. EFX trades at 11.0x 2027 EV/EBITDA and signed approximately $300 million in government contracts in the first half of 2026, primarily benefiting 2027; mortgage applications are approximately 50% of 2015-to-2019 levels, and a return to normal could add more than $4 to EPS, though this may take time if interest rates remain elevated. The institution's analysis of 2027 consensus revenue growth and P/E regression shows 2027 consensus growth of 8.2% for TRU versus 5.2% implied by the market, and 9.0% for EFX versus 6.8% implied by the market, leading it to view both as the most undervalued names in the sector.

Analysis framework

The report first compares share prices and valuations during the period of AI concerns with historical levels and the S&P 500, then tests fundamentals using first-half 2026 performance versus consensus expectations, changes in full-year guidance, and AI product adoption data. It subsequently analyzes transmission mechanisms across three AI risk categories—competition, business models, and margins—and selects preferred stocks based on comparisons of growth, margins, business moats, and relative valuation.

Methodology notes

• Valuation MethodEV/EBITDA valuation

  Forward 12-month and 2027 EV/EBITDA relative to historical averages and peers

  The report measures valuation using enterprise value-to-EBITDA multiples, comparing the sector and individual companies with five-year historical averages, standard deviations, and peers to assess the extent of de-rating.

• Valuation Method

  Growth-adjusted valuation analysis using regression of 2027 consensus revenue growth and P/E multiples

  The report places 2027 consensus revenue growth on the x-axis and P/E multiples on the y-axis, comparing market-implied growth with analyst consensus through the regression relationship to identify TRU and EFX as relatively undervalued.

• Competition and Strategy FrameworkMoat / competitive advantage

  Proprietary data, regulatory barriers, brands, and decision-grade products embedded in workflows

  The report evaluates companies' ability to withstand AI disruption based on whether these assets can sustain customer value, pricing power, and retention amid the proliferation of AI tools and new competitors.

Asset mapping & comparison

Structured mapping from thesis to named assets (strengths, weaknesses, peers, risks).

• TransUnion (TRU.N)

  The institution favors it as a beneficiary of consumer credit, a mortgage recovery, and AI-driven data consumption.

  Strengths

  Healthy consumer credit trends, a potential mortgage recovery, strong underlying margins, regulatory and proprietary data barriers; revenue and EPS have both exceeded expectations for the past 11 quarters.

  Weaknesses

  FICO pass-through and acquisition-related headwinds obscure margin expansion.

  Comparison

  10.0x 2027 EV/EBITDA, 1 turn below EFX; one of the sector's most undervalued names in the growth-adjusted valuation analysis.

  Risks

  A weaker-than-expected mortgage recovery and weakness in AI-related growth, KPIs, or margins.

• S&P Global (SPGI.N)

  The institution favors its resilient benchmarking business, compounding characteristics of its ratings business, and positioning with relatively manageable AI risk.

  Strengths

  Approximately 65% of revenue and approximately 80% of profit come from the benchmarking business; revenue is expected to grow at a high-single-digit rate, EPS at a low-double-digit rate, with M&A and infrastructure financing supporting issuance growth.

  Weaknesses

  The market intelligence business still faces concerns about AI disintermediation, while issuance comparisons are more challenging.

  Comparison

  2027 P/FCF is approximately 5 turns below MCO; current forward 12-month EV/EBITDA is approximately 17x, more than 1 standard deviation below the five-year average.

  Risks

  Insufficient resilience in the ratings business or failure of market intelligence growth to stabilize.

• MSCI (MSCI.N)

  The institution favors its high growth, high margins, subscription business trends, and AI-enhanced product capabilities.

  Strengths

  2024-to-2028 organic revenue CAGR of 9% and an EBITDA margin of approximately 61%; new subscription growth and run rate are accelerating, while AI can improve products, efficiency, and retention.

  Weaknesses

  Its 19.8x 2027 EV/EBITDA is at the higher end of peers.

  Comparison

  Although at the higher end of peer valuations, the institution believes its high organic growth supports the premium; current forward 12-month EV/EBITDA is approximately 21x, more than 1 standard deviation below the five-year average.

  Risks

  AI product adoption, subscription growth, or productivity gains fall short of expectations.

• Equifax (EFX.N)

  The institution favors its government contracts, future mortgage recovery, data and regulatory barriers, and AI efficiency upside.

  Strengths

  Signed approximately $300 million in government contracts in the first half of 2026, primarily benefiting 2027; proprietary data and regulatory barriers help limit AI-disruption risk.

  Weaknesses

  The mortgage recovery depends on the interest-rate environment and may take time to materialize.

  Comparison

  11.0x 2027 EV/EBITDA; one of the sector's most undervalued names in the growth-adjusted valuation analysis, with valuation more than 2 standard deviations below five-year average forward 12-month EV/EBITDA and P/E.

  Risks

  Persistently weak mortgage volumes, government contract contributions below expectations, or AI investment failing to translate into margin improvement.

Key data

• Information services sector forward 12-month EV/EBITDA14xBelow the five-year average of 21x.
• Sector relative performance, one year through June 30, 2026-36%Average year-over-year performance of information services stocks; the S&P 500 was +21% over the same period.
• Sector relative performance, from June 2026 through August 18+11%Average increase for information services stocks; the S&P 500 rose 3% over the same period.
• Share of covered companies beating expectationsRevenue 80%; adjusted EBITDA 70%; adjusted EPS 90%Second-quarter 2026 results relative to consensus expectations.
• TRU 2027 EV/EBITDA10.0xThe institution considers this valuation attractive; it is 1 turn below EFX.
• SPGI business mixApproximately 65% of revenue and approximately 80% of profit from the benchmarking businessThe report views this as the foundation of revenue and profit resilience.
• MSCI operating metrics2024 to 2028 organic revenue CAGR 9%; EBITDA margin approximately 61%The report uses these metrics to support its higher-growth, higher-margin positioning.
• EFX government contractsApproximately $300 millionSigned in the first half of 2026 and primarily benefiting 2027.
• EFX mortgage recovery sensitivity$4+ EPSPotential EPS accretion if mortgage applications recover from approximately 50% of 2015-to-2019 levels to normal levels.
• TRU and EFX growth-implied gapTRU: consensus 8.2% vs. market-implied 5.2%; EFX: 9.0% vs. 6.8%Based on 2027 growth and P/E regression analysis.

Impact & implications

The report believes AI-disruption concerns will continue to subject information services companies' earnings and KPIs to heightened scrutiny, but current valuations already reflect substantial risk. If companies continue to demonstrate organic growth, AI product adoption, customer retention, and commercialization progress, platforms with proprietary data and regulatory barriers can outperform market expectations; conversely, AI investment continuing to outpace productivity gains would weaken the margin-recovery thesis.

Risks

• AI-native competitors, lower-cost alternatives, and client-built tools may lower barriers to entry, particularly affecting products without proprietary data.
• Any weakness in KPIs, core segment revenue, or adjusted EBITDA margins could be attributed by the market to AI disruption and pressure share prices.
• If AI infrastructure, token consumption, and product investment continue to outpace productivity gains, they could become higher structural costs and limit margin expansion.
• Long-term pricing power may remain under pressure, especially for companies lacking proprietary data or strong moats.

What to watch

• Whether new contract wins, customer retention, and organic revenue growth accelerate, validating that AI has not caused customer losses or weaker growth.
• The share of contract value from AI-enabled products, customer usage rates, AI product adoption, and their contributions to customer growth and monetization.
• Adjusted EBITDA margins and the relationship between AI investment, token costs, and productivity gains.
• TRI's margin expansion performance and whether elevated investment slows margin expansion from the near term through fiscal 2027.
• Whether SPGI's ratings business resilience and market intelligence business growth stabilize.
• EFX mortgage-volume recovery and realization of government contract contributions to 2027 growth.