How AI Search Is Recommending IRAs

AI search is reshaping how investors discover and choose IRA providers. Instead of browsing search results and visiting multiple brand websites, buyers are increasingly asking AI systems to compare providers, explain fee structures, surface alternatives, and recommend shortlists. The IRA provider that appears in an AI-generated shortlist gains a meaningful advantage at the moment of decision, and the provider that does not may never enter the buyer's consideration set.

The LLM Authority Index benchmark for June 2026 reveals that AI recommendation power in the IRA category is highly concentrated. Charles Schwab dominates across all measured buyer stages, while several well-known brands appear frequently in AI responses but rarely earn the recommendation credit needed to influence buyer shortlists. This report interprets the benchmark findings and explains what they mean for competitive positioning in AI-led discovery. It is benchmark-based industry analysis, not a client result.

Methodology

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

2. Brands/entities 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 date/window: June 2026, snapshot-based measurement.

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

5. Number of prompts tested: Prompt count was not provided. A total of 1,497 observations were analyzed across three high-intent prompt clusters.

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 means 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 recommendation or ranked recommendation that earns recommendation credit. Visibility is not the same as recommendation credit. A brand can be mentioned without being recommended.

9. Ranking/scoring metrics used: Valid recommendation coverage, Top 3 rate, Rank 1 rate, average recommended rank, net sentiment score, and modeled monthly AI Authority Value, comprising AI Recommendation Value and AI Visibility Assist Value.

10. Limitations: This is a point-in-time benchmark. AI outputs can change between measurement periods. Modeled values are estimates and not revenue. This report is not a full audit or full market census. Prompt count was not provided; findings are based on observations analyzed.

Key Findings

Charles Schwab dominates recommendation-stage visibility across all buyer stages. The benchmark shows Schwab appeared in 73.9% of all 1,497 observations and converted that presence into a 57.9% valid recommendation coverage rate. Its 52.6% Top 3 rate and $2.1M in modeled monthly AI Authority Value place it well ahead of every other provider in the measured universe. The analysis found Schwab won every measured cluster, including Discovery, Comparison, and Pricing and Fees.

Fidelity holds the strongest rank position in the category. When AI systems recommend Fidelity, they tend to place it first. The benchmark shows Fidelity achieved a 29.3% Rank 1 rate and an average recommended rank of 1.30, the best average rank position in the measured universe. Its net sentiment score of 0.90 was the highest among all providers. Fidelity captured $1.2M in modeled monthly AI Authority Value.

Vanguard is widely visible but rarely earns the top shortlist position. The analysis found Vanguard appeared in 55.9% of responses and earned a 38.8% valid recommendation coverage rate, nearly identical to Fidelity in coverage volume. However, its average recommended rank of 2.97 and 4.9% Rank 1 rate show that AI systems tend to place Vanguard lower in shortlists. Vanguard captured $1.05M in modeled monthly AI Authority Value. Its strength is broad visibility rather than top-position placement.

SoFi and E*TRADE show the largest gaps between visibility and recommendation power. SoFi appeared in 17.8% of responses but earned only 9.7% valid recommendation coverage. E*TRADE appeared in 21.0% of responses but achieved only 7.9% recommendation coverage. Both brands are present in AI responses but rarely earn the recommendation credit needed to influence buyer shortlists.

Merrill Edge has near-zero AI recommendation power in the IRA category. The dataset marked Merrill Edge appearing in only 9.7% of observations with a 1.9% valid recommendation coverage rate. Its net sentiment score of 0.28 was the lowest in the measured universe. Merrill Edge captured $103K in modeled monthly AI Authority Value, the smallest figure in the category.

What Changed in the Market

IRA provider discovery has shifted. Buyers are no longer only moving from Google search results to brand websites. They are also asking AI systems to compare providers, explain reputation, summarize pricing, surface alternatives, and recommend shortlists. This shift changes where competitive advantage is built and how it is lost.

For a category as trust-heavy and decision-sensitive as IRAs, AI systems are functioning as de facto shortlist builders. They construct responses by synthesizing multiple public sources: comparison articles, review content, official product pages, fee disclosures, and community discussions. The result is a shortlist that reflects not just brand awareness but the depth, credibility, and consistency of publicly available information about each provider. A brand with strong consumer recognition but a thin or poorly structured public evidence layer can appear in AI responses without earning a shortlist position.

The data from June 2026 shows that AI platforms are not treating all IRA providers equally. Recommendation power is concentrated around a small group of providers with strong, well-structured source footprints. Charles Schwab, Fidelity, and Vanguard collectively account for the majority of modeled AI recommendation value in the category. The remaining seven providers compete for a smaller share of AI-driven buyer attention.

This concentration has a direct commercial consequence. When a buyer asks an AI system to recommend an IRA provider, the shortlist it returns is not a neutral sample of the market. It is a filtered set shaped by the public evidence available to each AI platform. Providers that have invested in that evidence layer appear on shortlists. Providers that have not are displaced, regardless of their product quality or brand spending.

What the Benchmark Found

Recommendation Leaders

Charles Schwab is the dominant force in AI-driven IRA discovery. It appeared in 73.9% of all 1,497 observations and earned 868 valid recommendations, 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, the highest in the category by a wide margin. Schwab performed consistently across every measured cluster: Discovery, Comparison, and Pricing and Fees.

Fidelity holds the strongest rank position in the measured universe. It achieved a 29.3% Rank 1 rate and an average recommended rank of 1.30, meaning AI systems that recommend Fidelity 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 category. Fidelity captured $1.2M in modeled monthly AI Authority Value.

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

Mid-Tier Providers

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 recommended rank of 3.50 place it in the middle tier. Robinhood captured $989K in modeled monthly AI Authority Value. The platform shows notable strength on Google AI Overviews, where it achieved a 15.4% Rank 1 rate and 49.6% recommendation coverage.

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 recommended rank of 3.80 reflect a mid-tier position. Betterment captured $953K in modeled monthly AI Authority Value. The benchmark shows particular strength for Betterment on Perplexity, where it achieved an 11.9% Rank 1 rate.

Visible but Under-Recommended

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 recommended rank was 4.05. Wealthfront captured $329K in modeled monthly AI Authority Value. The platform shows moderate presence but limited top-tier recommendation power.

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 recommended rank was 4.49, the highest average rank number in the category, indicating consistent placement at the lower end of shortlists when it appears. M1 Finance captured $248K in modeled monthly AI Authority Value.

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 recommended rank was 3.32. SoFi captured $335K in modeled monthly AI Authority Value. The gap between its presence rate and recommendation coverage rate is among the largest in the measured universe.

Commercially Weak Performers

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 recommended rank was 3.96. E*TRADE captured $126K in modeled monthly AI Authority Value. The brand carries a net sentiment score of 0.43, one of the weaker framing signals in the category.

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 was 0.28, the lowest in the category. Merrill Edge captured $103K in modeled monthly AI Authority Value.

Why Visibility Is Not Enough

A brand can appear in AI answers and still fail to win the buyer shortlist. This is the core distinction the benchmark makes clear, and it separates brands that are present in AI-led discovery from brands that actually shape buyer decisions.

Raw mention presence measures how often a company is named in AI responses. Valid recommendation coverage measures how often a company is actually recommended or shortlisted with positive framing. These are different signals with different commercial implications. E*TRADE appeared in more than one in five responses but earned valid recommendation credit in fewer than one in twelve. SoFi shows the same pattern. Both brands are visible in AI outputs but commercially weak in the recommendation layer.

Top 3 placement carries more weight than general visibility. Charles Schwab earned a 52.6% Top 3 rate. Merrill Edge earned a 0.7% Top 3 rate. The distance between those two figures is the distance between being a buyer's shortlist option and being an afterthought. Being named in an AI response without earning a top position is closer to a background mention than a genuine recommendation.

Rank 1 placement is the most commercially valuable position in a shortlist. Fidelity achieved a 29.3% Rank 1 rate. Vanguard, despite nearly identical recommendation coverage volume, achieved only 4.9%. When AI systems recommend Fidelity, they tend to put it first. When they recommend Vanguard, they tend to place it third or lower. That difference shapes which brand the buyer acts on.

Net sentiment and framing determine whether a mention works for or against a brand. Fidelity's net sentiment score of 0.90 means AI systems frame it positively in nearly every response. E*TRADE's 0.43 and Merrill Edge's 0.28 mean their appearances carry weaker framing that is less likely to move a buyer toward action. Neutral mentions are not recommendation credit.

Modeled monthly AI Authority Value is a benchmark estimate, not revenue. It reflects the relative commercial weight of each brand's recommendation presence across the measured observation set. The $2.1M attributed to Schwab versus the $103K attributed to Merrill Edge illustrates the degree of concentration in the category. That concentration is a competitive risk for every provider outside the top three.

The Citation Layer

AI platforms do not recommend brands arbitrarily. They construct responses by retrieving and synthesizing publicly available information. The concentration of recommendation power around Charles Schwab, Fidelity, and Vanguard reflects the depth, structure, and consistency of their public evidence layers.

These three providers benefit from extensive comparison content, official product pages with clear fee disclosures, strong editorial coverage across financial media, review platform presence, and consistent citation in trusted third-party sources. AI systems can retrieve, compare, and synthesize this information when building shortlists. The evidence layer is wide, credible, and well-structured.

Digital-first brands like SoFi, Wealthfront, and Betterment have public evidence that is thinner or less consistently structured. They appear in AI responses because financial content references them, but the benchmark evidence suggests their citation architecture does not support ranked recommendation placement at the same rate. Betterment's strength on Perplexity may reflect that platform's particular affinity for robo-advisor content, but it does not carry across the full set of measured platforms.

E*TRADE and Merrill Edge face a different challenge. Their public evidence layer appears to carry weaker sentiment signals. Sources that shape AI framing for these brands may be introducing neutral or cautionary context that reduces their eligibility for positive shortlist placement. The source pattern may indicate a gap between brand investment and the third-party editorial narrative that AI systems are retrieving.

Search-visible pages, editorial reviews, comparison directories, and community discussions are all part of the public evidence layer that AI systems appear to synthesize. Brands with deeper, more consistent, and more positively framed source footprints tend to earn stronger recommendation placement. That connection cannot be attributed to any single source type, but the pattern across the measured universe is consistent with it.

What Brands Need to Fix

Weak valid recommendation coverage. Several brands appear in AI responses but fail to convert presence into recommendation credit. Closing this gap requires a stronger public evidence layer that AI systems can retrieve and trust when constructing shortlists, not just general brand mentions in financial content.

Low Top 3 and Rank 1 placement. Vanguard has broad visibility but rarely earns the top position. Betterment and Wealthfront appear consistently but are not reliably placed in the top three. Improving rank position requires content and citation architecture that frames the brand as a first-choice option, not a supporting alternative.

Uneven prompt-cluster coverage. Charles Schwab won every measured cluster. Most competitors showed weaker performance at specific buyer stages, particularly Decision-stage prompts around Pricing and Fees. Brands need to ensure their evidence layer supports recommendation across all buyer stages, not just general awareness queries.

Weak or neutral framing signals. E*TRADE and Merrill Edge carry the weakest net sentiment scores in the category. AI systems frame these brands with less positive conviction. Improving framing requires identifying and strengthening the public sources that shape how AI platforms describe each brand.

Thin source footprint. Brands in the middle and lower tiers lack the depth of comparison content, review coverage, and trusted editorial citations that the top three providers have built. A thinner source footprint means AI systems have less credible material to work with when considering whether to recommend a brand.

Inconsistent or incomplete entity information. For trust-heavy financial products like IRAs, AI systems rely on consistent, verifiable information about fees, account types, minimums, and regulatory standing. Brands with incomplete or inconsistent public information create uncertainty that can suppress recommendation credit.

Limited pricing and comparison content. The Decision-stage cluster around Pricing and Fees is a high-intent prompt cluster where buyers are close to action. Brands that do not have strong, retrievable, well-framed pricing and fee content are poorly positioned in the moment of highest commercial intent.

How CiteWorks Studio Helps

1. Map AI recommendation visibility. Track prompts, platforms, company presence, valid recommendations, Top 3 and Rank 1 performance, framing, and citation sources across the IRA category and adjacent financial verticals.

2. Identify the sources shaping AI answers. Find the editorial, review, forum, directory, owned, and search-visible sources that influence brand framing and recommendation placement across the measured AI platforms.

3. Build the citation architecture plan. Strengthen the public evidence layer so AI systems have more accurate, consistent, and persuasive source material to synthesize when constructing shortlists.

Commercial Takeaway

AI-led discovery is changing where buyer shortlists are formed in the IRA category. Charles Schwab, Fidelity, and Vanguard now control the majority of modeled AI recommendation value, and that concentration is likely to deepen as AI adoption continues. Brands that depend on traditional brand awareness and search visibility without addressing the recommendation layer are being displaced at the moment of buyer decision.

The opportunity is not to chase AI mentions. It is to earn valid recommendation credit, top-three placement, and positive framing in the prompt clusters where buyers are making their shortlist decisions. SoFi and E*TRADE have significant consumer brand recognition. That recognition does not appear to translate into AI recommendation power at the scale that their visibility would suggest. The brands winning in AI-led discovery are the brands that AI systems can retrieve, verify, and frame positively.

The total modeled monthly AI Authority Value estimated across the measured universe for this category is $28.6M. Charles Schwab captures $2.1M of that modeled value. Merrill Edge captures $103K. The distance between those figures is a commercial signal, not a revenue guarantee. Modeled benchmark value is an estimate of relative recommendation weight, not booked business. But the concentration it reflects is real, and for brands outside the top three, the gap is widening.

Find Out Where You Stand in AI Recommendations

AI discovery is an active channel already shaping buyer choice in the IRA category. If your brand appears in AI responses but is not being recommended, or if competitors are winning the shortlist positions that should be yours, the benchmark data can show you exactly where the gap is and what is driving it.

CiteWorks Studio can show where your brand appears in AI-generated responses, where competitors are being recommended instead, which prompt clusters carry the most commercial risk for your position, which sources are shaping AI framing for your category, and what needs to change to improve recommendation-stage visibility. Request an AI Visibility Audit, an AI Market Discovery Profile, or a Citation Architecture Review to see where you stand.

Benchmark Source

This analysis is based on the 2026 AI Discovery Index for IRAs, published by LLM Authority Index. The benchmark dataset and public industry report were supplied for this category. Read the full benchmark report at the LLM Authority Index.