IRAs: 2026 AI Market Discovery Index

For the strategic interpretation of this benchmark, read CiteWorks Studio's analysis of how AI search is recommending Iras

Answer Capsule

In the IRAs category for June 2026, AI systems are concentrating shortlist recommendations around a small set of established providers. Charles Schwab leads across all measured clusters with a 52.6% Top 3 rate and 57.9% valid recommendation coverage. Fidelity and Vanguard form a strong second tier. The clearest risk pattern appears among digital-first brands like SoFi, E\*TRADE, and M1 Finance, which appear in AI responses but rarely earn ranked recommendation credit.

Executive Summary

AI platforms are reshaping IRA provider discovery by acting as de facto shortlist builders. In June 2026, Charles Schwab captured $2.1M of the total modeled AI opportunity value across three high-intent clusters, more than double the share of the next closest competitor. Schwab appeared in 73.9% of all 1,497 observations and converted that presence into a 57.9% valid recommendation coverage rate, meaning the majority of its appearances were positive, ranked recommendations rather than passive mentions.

Fidelity and Vanguard occupy a strong second tier with recommendation coverage rates of 38.9% and 38.8% respectively. The more important distinction is rank quality. Fidelity achieved a 29.3% Rank 1 rate and an average recommended rank of 1.30, meaning when AI systems recommend Fidelity, they tend to place it first. Vanguard, despite nearly identical coverage, averaged a rank of 2.97 and earned only a 4.9% Rank 1 rate.

The most exposed group includes SoFi, E\*TRADE, and Merrill Edge. These brands appear in AI responses at measurable rates but convert very few appearances into ranked recommendations. SoFi appeared in 17.8% of responses but achieved only 9.7% recommendation coverage. E\*TRADE appeared in 21.0% of responses but converted just 7.9% into valid recommendations. Merrill Edge sits at the bottom of the measured universe with a 1.9% recommendation coverage rate and the lowest net sentiment score in the category.

Recommendation power in this category is not driven by brand awareness alone. It is driven by the quality and structure of public evidence that AI systems can retrieve, cite, and trust when constructing shortlists.

The AI Discovery Shift in IRAs

Traditional IRA provider discovery relied on search engine rankings, paid placement, and brand recognition accumulated over decades. AI platforms operate differently. They synthesize multiple public sources, including comparison articles, official product pages, review content, and community discussions, and produce a shortlist that reflects not just visibility but the depth and credibility of publicly available information about each provider.

This shift matters because AI-generated responses are increasingly the first filter in buyer decision-making. A provider that appears in an AI response but is not recommended is functionally absent from the shortlist. A provider that earns a consistent Top 3 recommendation gains placement in the most commercially influential position in the discovery process.

The June 2026 data shows that AI platforms are already concentrating IRA recommendations. The top three providers by recommendation coverage, Schwab, Fidelity, and Vanguard, hold a structural advantage that challenger brands cannot close through brand spend alone. Visibility in AI responses is a necessary condition, but it is not sufficient. What converts visibility into commercial value is ranked recommendation credit, and that is where most brands in this category are falling short.

Directional Category Leaders

1. Charles Schwab

Charles Schwab is the dominant force in AI-driven IRA discovery. It appeared in 73.9% of all observations and earned 868 valid recommendations, the highest count in the category, translating to a 57.9% recommendation coverage rate. Schwab achieved a 52.6% Top 3 rate and a 10.8% Rank 1 rate, with an average recommended rank of 2.09. Its modeled monthly AI Authority Value reached $2.1M. Schwab led every measured cluster: Discovery, Comparison, and Pricing and Fees.

The public interpretation: Charles Schwab has the strongest combination of visibility, recommendation frequency, and rank position of any IRA provider across AI platforms.

2. Fidelity

Fidelity holds the strongest rank quality in the category. It achieved a 29.3% Rank 1 rate and an average recommended rank of 1.30, meaning when AI systems recommend Fidelity, they almost always place it first. Fidelity appeared in 46.8% of observations and earned 582 valid recommendations, a 38.9% coverage rate. Its net sentiment score of 0.90 was the highest in the measured universe. Fidelity captured $1.2M in modeled monthly AI Authority Value.

The public interpretation: Fidelity is the brand AI systems most consistently place at the top of IRA shortlists, making it the strongest challenger to Schwab's volume advantage.

3. Vanguard

Vanguard appeared in 55.9% of observations and earned 581 valid recommendations, a 38.8% coverage rate nearly identical to Fidelity. However, Vanguard's average recommended rank of 2.97 and 4.9% Rank 1 rate show that AI systems place it lower in shortlists than its presence rate suggests it deserves. Vanguard captured $1.05M in modeled monthly AI Authority Value. Its strength is broad recognition rather than top-position dominance.

The public interpretation: Vanguard is widely surfaced by AI systems but is less likely than Fidelity to earn the lead shortlist position when both brands appear in the same response.

4. Robinhood

Robinhood appeared in 54.2% of observations and earned 560 valid recommendations, a 37.4% coverage rate. Its Top 3 rate of 17.4% and average rank of 3.50 place it solidly in the middle tier. Robinhood captured $989K in modeled monthly AI Authority Value. The platform shows platform-specific strength on Google AI Overviews, where it achieved a 15.4% Rank 1 rate and 49.6% recommendation coverage.

The public interpretation: Robinhood has meaningful AI recommendation presence but lacks the consistent top-tier positioning of the three leaders above it.

5. Betterment

Betterment appeared in 41.4% of observations and earned 431 valid recommendations, a 28.8% coverage rate. Its Top 3 rate of 7.4% and average rank of 3.80 reflect a mid-tier position. Betterment captured $953K in modeled monthly AI Authority Value and shows particular strength on Perplexity, where it achieved an 11.9% Rank 1 rate. Among robo-advisor positioned brands, Betterment is the clearest AI recommendation presence.

The public interpretation: Betterment is a recognized option in AI responses but is not consistently placed in the top shortlist positions across all platforms.

6. Wealthfront

Wealthfront appeared in 33.7% of observations and earned 295 valid recommendations, a 19.7% coverage rate. Its Top 3 rate was 4.9% and its average rank was 4.05. Wealthfront captured $329K in modeled monthly AI Authority Value. The platform holds moderate visibility but limited top-tier recommendation power and trails Betterment meaningfully in both coverage and rank quality.

The public interpretation: Wealthfront appears in AI responses with moderate consistency but has not built the recommendation depth needed to compete at the top of the shortlist.

7. SoFi

SoFi appeared in 17.8% of observations but earned only 145 valid recommendations, a 9.7% coverage rate. Its Top 3 rate was 4.5% and its average rank was 3.32. The gap between its presence rate and its recommendation rate is one of the largest in the category. SoFi captured $335K in modeled monthly AI Authority Value despite holding measurable consumer brand recognition.

The public interpretation: SoFi is mentioned by AI systems regularly but rarely earns the recommendation credit needed to influence buyer shortlists.

8. M1 Finance

M1 Finance appeared in 17.9% of observations and earned 151 valid recommendations, a 10.1% coverage rate. Its Top 3 rate was 2.7% and its average rank of 4.49 was the highest (weakest) average rank in the category. M1 Finance captured $248K in modeled monthly AI Authority Value. When it does appear in shortlists, AI systems tend to place it at the bottom.

The public interpretation: M1 Finance is present in AI responses but consistently occupies the lowest shortlist positions, limiting its commercial impact from AI discovery.

9. E\*TRADE

E\*TRADE appeared in 21.0% of observations but earned only 118 valid recommendations, a 7.9% coverage rate. Its Top 3 rate was 2.3% and its average rank was 3.96. E\*TRADE's net sentiment score of 0.43 is the second lowest in the category. It captured $126K in modeled monthly AI Authority Value, a significant gap relative to its brand scale.

The public interpretation: E\*TRADE carries weak sentiment signals in AI responses and converts far less of its visibility into recommendation credit than its brand recognition would suggest.

10. Merrill Edge

Merrill Edge is the weakest performer in the measured universe. It appeared in only 9.7% of observations and earned 28 valid recommendations, a 1.9% coverage rate. Its Top 3 rate was 0.7% and its net sentiment score of 0.28 was the lowest in the category. Merrill Edge captured $103K in modeled monthly AI Authority Value.

The public interpretation: Merrill Edge has minimal AI visibility and near-zero recommendation power in the IRA category, despite the brand strength of its parent institution.

The Buying Moments That Now Decide the Category

Best Brokerage and Investment Platform Discovery

This awareness-stage cluster captured 514 observations with a modeled monthly AI opportunity value of $8.7M. Charles Schwab led with a 45.3% Top 3 rate and $703K in captured value. Fidelity followed with a 30.4% Top 3 rate and $405K captured. Buyers at this stage are searching for general guidance on where to open an IRA, and AI systems are answering with a tight shortlist dominated by two providers. Brands absent from this cluster miss the entry point of the entire decision journey.

Brokerage and Investment Platform Comparisons

This consideration-stage cluster captured 468 observations with a modeled monthly AI opportunity value of $9.4M. Charles Schwab led with a 54.1% Top 3 rate and $750K captured. Fidelity followed with a 38.9% Top 3 rate and $408K captured. This is the highest-stakes cluster for challenger brands because buyers here are actively evaluating options. The gap between Schwab and the rest of the market widens at this stage, suggesting AI systems draw on stronger comparative evidence for leading providers.

Brokerage and Investment Platform Pricing and Fees

This decision-stage cluster captured 515 observations with a modeled monthly AI opportunity value of $10.5M, the largest of the three clusters. Charles Schwab led with a 51.7% Top 3 rate and $653K captured. Fidelity followed with a 36.3% Top 3 rate and $398K captured. Buyers searching for fee information are close to committing. Fidelity's strong rank quality in this cluster suggests its public fee disclosure content performs well in AI retrieval, which may explain its consistently high Rank 1 placement across platforms.

Why Recommendation Power Is Concentrating

AI platforms do not recommend brands based on marketing spend or brand awareness signals. They construct shortlists by retrieving and synthesizing publicly available information: comparison articles, official product and fee pages, editorial reviews, and community discussion. The concentration of recommendation power around Schwab, Fidelity, and Vanguard directly reflects the depth and structure of their public evidence layers.

These three providers benefit from decades of financial media coverage, consistent citation in authoritative comparison sources, well-structured official content with clear fee and product disclosures, and extensive review coverage across multiple platforms. When an AI system queries IRA providers across six platforms, it draws on all of this material simultaneously.

Digital-first brands like SoFi, Wealthfront, and Betterment have public evidence that is thinner or less structured relative to the category leaders. They appear in AI responses because they are referenced in general financial content, but they have not built the citation architecture that earns ranked recommendation credit at scale. Merrill Edge and E\*TRADE face a different problem: their public evidence carries weaker sentiment signals, which lowers their eligibility for positive placement even when they are retrieved.

Public source quality determines recommendation eligibility. Comparison content, authoritative citations, and structured product information are not marketing assets in the traditional sense. They are the raw material AI systems use to decide which brands belong on a shortlist.

The Category's Most Visible Warning Sign

The most commercially significant warning sign in this dataset is the presence-to-recommendation gap visible in E\*TRADE's numbers.

E\*TRADE appeared in 21.0% of all AI responses. That level of presence, across six platforms and 1,497 observations, represents real visibility. A buyer asking any of the measured AI systems about IRAs will encounter E\*TRADE at a meaningful rate. But only 7.9% of those appearances converted into valid recommendations. The rest were neutral or contextual references that carry no shortlist influence. E\*TRADE's net sentiment score of 0.43 is the second lowest in the category, signaling that when AI systems do retrieve E\*TRADE content, it is not generating the positive, comparative framing that earns recommendation credit.

The result is a brand that is paying the informational cost of AI visibility without capturing its commercial value. In a category where Schwab converts 57.9% of its appearances into recommendations and Fidelity averages a rank of 1.30 when recommended, E\*TRADE's 7.9% conversion rate represents a structural gap, not a marginal one. As AI-assisted discovery becomes a more central part of how investors find and choose IRA providers, that gap will have real account acquisition consequences.

What This Means for the Category

The IRA provider market is undergoing shortlist compression. AI systems are concentrating recommendations around a small group of established providers, and that compression is already visible in the data. Schwab, Fidelity, and Vanguard together dominate the recommendation landscape, while seven other providers compete for the remaining shortlist positions. As AI adoption grows, brands outside the top tier will find it increasingly difficult to break into buyer consideration sets without addressing the underlying evidence gaps that AI systems rely on.

Competitor displacement is not a future risk. It is measurable in this dataset. SoFi and E\*TRADE have genuine consumer brand recognition, yet both are being displaced at the recommendation stage by brands with stronger public evidence architecture. Brand awareness that does not translate into AI recommendation power no longer protects market position the way it once did.

Trust-source dependency is becoming the defining structural factor in this category. AI platforms depend on public sources that are authoritative, current, and well-structured. Brands that have invested in this infrastructure hold a compounding advantage because better-cited brands attract more coverage, more comparisons, and more structured content over time. Brands that have not made this investment are falling further behind with each AI platform update.

Underperforming brands in this category need more than content volume. They need stronger entity recognition across structured and unstructured sources, clearer product and fee information in formats AI systems can retrieve and trust, more authoritative citation patterns across financial media and comparison platforms, and a clear strategy for earning recommendation credit across multiple AI systems rather than managing presence alone.

What This Public Benchmark Does Not Include

- Full cluster dataset covering all 10 measured clusters

- Prompt-level response tables showing exact AI outputs per query

- Citation-source failure maps identifying which sources are missing or underperforming

- Platform-by-platform recovery priorities for each brand across all six AI systems

- Entity and schema diagnostics for structured data readiness

- Source-layer gap analysis comparing public evidence depth by brand

- Company-specific content and citation recommendations

- Exact competitor threat profiles by cluster and platform

- Full paid opportunity model with platform-level valuation breakdown

This page shows the market shape. The paid report shows the repair map.

Methodology and Disclaimers

1. Market studied: IRAs and brokerage and investment platform discovery, comparison, and pricing decisions.

2. Brands included: Charles Schwab, Fidelity, Vanguard, Robinhood, Betterment, SoFi, Wealthfront, M1 Finance, E\*TRADE, and Merrill Edge. This is not a full market census.

3. Data collection window: June 2026, snapshot-based measurement.

4. AI platforms tested: ChatGPT, Copilot, Gemini, Google AI Mode, Google AI Overviews, and Perplexity.

5. Observations analyzed: 1,497 observations across three high-intent clusters. Prompt count was not separately disclosed in this public benchmark.

6. Prompt categories: Awareness (Best Brokerage and Investment Platform Discovery), Consideration (Brokerage and Investment Platform Comparisons), and Decision (Brokerage and Investment Platform Pricing and Fees).

7. Definition of a mention: A mention is recorded when the company appeared in an AI-generated response, regardless of sentiment or rank position.

8. Definition of a valid recommendation: A valid recommendation is a positive, shortlist-quality or ranked recommendation that earns recommendation credit. Visibility and recommendation credit are distinct metrics and are not used interchangeably in this report.

9. Metrics used: Valid recommendation coverage, Top 3 rate, Rank 1 rate, Top 10 rate, average recommended rank, net sentiment score, and modeled monthly AI Authority Value. Modeled AI Authority Value is an estimate based on observed recommendation patterns and is not a revenue figure.

10. Limitations: This is a point-in-time benchmark. AI outputs are dynamic and can change as platforms update their models and retrieval sources. Modeled values are directional estimates, not financial projections. This benchmark is not a full audit and does not represent a complete census of IRA providers or AI platforms.

For a Company-Specific Authority Index Report

For a company-specific Authority Index report, the deeper analysis would show which prompts each company wins or loses, which AI platforms are under-recognizing the brand, which source layers are shaping recommendations, and what changes may improve AI shortlist eligibility.