Robo-Advisors: 2026 AI Market Discovery Index

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

Answer Capsule

In the robo-advisor category for June 2026, AI platforms are concentrating buyer attention on a small set of recommended providers. Betterment leads with the highest recommendation coverage and captured value, while Wealthfront holds a strong second position. Fidelity Go and Schwab Intelligent Portfolios appear frequently but convert less presence into top-ranked recommendations. Several brands, including Ellevest and Wealthsimple, are visible in neutral contexts but rarely earn shortlist placement.

Executive Summary

Betterment has established the strongest AI recommendation position in the robo-advisor category, appearing in 54.1% of all observations and earning valid recommendation credit in 34.0% of responses. The platform captures an estimated $5.8 million in monthly AI Authority Value, more than 3.6 times the next closest competitor. Betterment leads across all three public high-intent clusters, from initial discovery through pricing evaluation.

Wealthfront holds the second position with a 29.8% recommendation coverage rate and an estimated $1.6 million in monthly captured value. Wealthfront shows particular strength in decision-stage prompts, where it achieves a 14.8% rank-one rate, the highest in the category. Fidelity Go and Schwab Intelligent Portfolios demonstrate solid presence but convert at lower rates, suggesting visibility without equivalent shortlist power.

The most exposed group includes Ellevest, Wealthsimple, and SoFi Automated Investing, each with recommendation coverage below 3%. These brands appear in AI responses primarily through neutral mentions rather than positive recommendations, indicating they are referenced as market context but not advanced as choices.

The commercial stakes are substantial. With $27.6 million in modeled monthly AI opportunity value across three public clusters alone, the gap between earning a shortlist position and being merely mentioned is no longer an abstract brand metric. It is a revenue exposure.

The AI Discovery Shift in Robo-Advisors

AI platforms are functioning as shortlist builders for investors evaluating automated investing options. When a buyer prompts an AI system for the best robo-advisor or asks it to compare platforms by fees, the response does not list every option in the category. It constructs a ranked shortlist. The brands that appear at the top of that list capture the majority of buyer consideration.

Being mentioned is not the same as being recommended. The data shows brands appearing in over a quarter of all responses while earning top-three placement in fewer than one in twenty. That gap represents the core commercial risk: presence signals awareness, but recommendation credit signals intent transfer.

The critical distinction is between citation frequency and recommendation power. AI systems retrieve from publicly available evidence, then rank based on signals including consistency of positive framing, source diversity, entity clarity, and comparative positioning. Brands with stronger evidence architecture earn ranked recommendations. Brands without it appear in passing.

In robo-advisors, this dynamic is already producing a two-tier market. The top two platforms command the majority of recommendation value. The remaining eight split a significantly smaller share, with several showing presence levels that do not translate into shortlist eligibility.

Directional Category Leaders

1. Betterment

Betterment appears in 54.1% of all observations and earns valid recommendation credit in 34.0% of responses. The platform achieves a 24.3% top-three rate and a 10.0% rank-one rate, the highest in the category. Betterment leads all three public clusters, from best robo-advisor queries through pricing and fee comparisons. Its net sentiment score of 0.70 reflects 507 positive mentions against only 10 negative across the full observation set.

The public interpretation: Betterment has built the most comprehensive AI recommendation architecture in robo-advisors, earning shortlist placement across every major buyer stage.

2. Wealthfront

Wealthfront appears in 51.9% of observations and earns recommendation credit in 29.8% of responses. The platform achieves a 22.5% top-three rate and a 14.8% rank-one rate, the highest rank-one frequency in the category. Wealthfront is particularly strong in decision-stage prompts, where pricing and fee comparisons drive buyer commitment. Its net sentiment score of 0.62 is solid, though it carries 30 negative mentions, more than any other platform in the study.

The public interpretation: Wealthfront is the strongest challenger to Betterment, with superior rank-one frequency but slightly lower overall recommendation coverage and a negative sentiment exposure worth monitoring.

3. Fidelity Go

Fidelity Go appears in 28.8% of observations and earns recommendation credit in 17.8% of responses. The platform achieves a 15.3% top-three rate and a 7.2% rank-one rate. Fidelity Go carries the highest net sentiment score in the category at 0.75, with zero negative mentions across all observations. Despite this clean sentiment record, its recommendation coverage runs at roughly half the rate of the top two platforms.

The public interpretation: Fidelity Go is well-regarded when mentioned but is not yet consistently advanced as a top-tier recommendation across the full category.

4. Schwab Intelligent Portfolios

Schwab Intelligent Portfolios appears in 26.8% of observations and earns recommendation credit in 12.6% of responses. The platform achieves a 5.1% top-three rate and a 3.3% rank-one rate. There is a significant conversion gap: Schwab appears in more than one in four responses but earns top-three placement in only one in twenty, suggesting that AI systems recognize the brand without consistently advancing it as a preferred choice.

The public interpretation: Schwab Intelligent Portfolios has strong category recognition but weak AI shortlist conversion relative to its visibility level.

5. Acorns

Acorns appears in 19.8% of observations and earns recommendation credit in 9.1% of responses. The platform achieves a 5.9% top-three rate and a 2.5% rank-one rate. Its net sentiment score of 0.52 reflects 141 positive mentions against 6 negative. Acorns maintains a consistent secondary position across clusters without breaking into the top tier.

The public interpretation: Acorns holds a stable but secondary role in AI recommendations, rarely competing for the top positions that drive buyer selection.

6. Vanguard Digital Advisor

Vanguard Digital Advisor appears in 17.8% of observations and earns recommendation credit in 8.6% of responses. The platform achieves a 3.9% top-three rate and a 2.4% rank-one rate. Vanguard carries zero negative sentiment but does not convert its trusted brand position into frequent top-ranked recommendations.

The public interpretation: Vanguard Digital Advisor is trusted but not frequently recommended as a leading choice in AI-generated shortlists.

7. M1 Finance

M1 Finance appears in 17.6% of observations and earns recommendation credit in 8.6% of responses. The platform achieves a 3.9% top-three rate and a 1.4% rank-one rate. Its net sentiment score of 0.53 reflects 127 positive mentions against 5 negative. M1 Finance and Vanguard Digital Advisor are effectively equivalent in recommendation coverage, both occupying the middle tier.

The public interpretation: M1 Finance has comparable visibility to Vanguard but slightly lower rank-one frequency, placing both in a similar commercial position.

8. Ellevest

Ellevest appears in 7.7% of observations but earns recommendation credit in only 0.9% of responses. The platform achieves a 0.5% top-three rate and a 0.4% rank-one rate. Ellevest has a net sentiment score of 0.0, with positive and negative mentions in equal balance. It is referenced primarily in neutral or comparative contexts, not as an actively recommended option.

The public interpretation: Ellevest is visible in AI responses but is not positioned as a recommended choice, representing the sharpest recommendation gap in the category.

9. SoFi Automated Investing

SoFi Automated Investing appears in 5.9% of observations and earns recommendation credit in 2.5% of responses. The platform achieves a 1.0% top-three rate and a 0.4% rank-one rate. SoFi has a net sentiment score of 0.51, with 40 positive mentions and zero negative, but its overall presence and recommendation footprint remain limited.

The public interpretation: SoFi Automated Investing has limited AI recommendation presence and rarely appears in positions that influence buyer decisions.

10. Wealthsimple

Wealthsimple appears in 6.0% of observations but earns recommendation credit in only 0.3% of responses. The platform achieves a 0.2% top-three rate and a 0.2% rank-one rate. Its net sentiment score of 0.08 reflects only 6 positive mentions across all observations, the lowest positive count in the category.

The public interpretation: Wealthsimple has minimal AI recommendation presence and is not a material factor in the current U.S. robo-advisor shortlist landscape.

The Buying Moments That Now Decide the Category

Best Robo-Advisor & Top Automated Investing Platforms

This consideration-stage cluster captures the highest-volume discovery prompts, where buyers are entering the category for the first time. Betterment leads with 33.3% recommendation coverage and a 9.8% rank-one rate. Wealthfront follows with 30.9% coverage and a 16.5% rank-one rate. Fidelity Go achieves 21.1% coverage with an 8.1% rank-one rate. The modeled monthly opportunity value for this cluster is $7.6 million. Brands that do not appear in this cluster are effectively absent from the category's entry point.

Robo-Advisor Comparisons & Platform Alternatives

This evaluation-stage cluster captures buyers comparing specific platforms before narrowing their choice. At $10.3 million in modeled monthly opportunity value, it is the highest-value public cluster. Betterment leads with 32.7% coverage and an 11.4% rank-one rate. Wealthfront follows with 28.0% coverage and a 15.3% rank-one rate. Fidelity Go achieves 15.5% coverage with an 8.2% rank-one rate. Platforms outside the top three are rarely surfaced as primary alternatives in this high-stakes cluster.

Robo-Advisor Pricing, Fees & Cost Evaluation

This decision-stage cluster captures buyers in the final stage of platform selection, where fee structures and cost comparisons drive commitment. Modeled monthly opportunity value is $9.7 million. Wealthfront's rank-one rate of 12.8% in this cluster is its strongest performance across any public segment, reflecting well-structured fee comparison content. Betterment leads coverage at 32.2%, with Fidelity Go at 15.8%. For most other platforms, this cluster is where recommendation gaps are most commercially costly.

Why Recommendation Power Is Concentrating

AI platforms construct recommendations from the public evidence layer: official brand content, financial media coverage, review and comparison sites, community discussions, and regulatory or trust signals. The concentration of recommendation power around Betterment and Wealthfront reflects structural advantages in each of these source categories, not simply brand size or assets under management.

Betterment benefits from the deepest citation architecture in the category. Appearing in 715 of 1,321 observations with positive sentiment in 507 of those appearances, the platform provides AI systems with dense, consistent, and diverse evidence to draw from when constructing ranked responses. That density matters because AI systems favor brands they can cite confidently across multiple source types.

Wealthfront compensates for slightly lower overall coverage with superior rank-one frequency. A 14.8% rank-one rate indicates that when Wealthfront is recommended, it is frequently the first choice. This pattern is strongest in decision-stage prompts where fee transparency and platform comparison content are most clearly differentiated, suggesting that Wealthfront's public evidence is particularly strong in source categories that AI systems weight heavily at the final selection stage.

The gap between presence and recommendation is most visible for Schwab Intelligent Portfolios. Schwab appears in 26.8% of responses but earns top-three placement in only 5.1%. This pattern typically indicates that available public evidence positions the brand as a recognized name rather than a recommended option, a distinction AI systems reflect in how they structure shortlists.

The Category's Most Visible Warning Sign

Ellevest presents the sharpest example of visibility without recommendation power in this category. The platform appears in 7.7% of all observations but earns recommendation credit in fewer than 1% of responses. More significantly, its net sentiment score is exactly 0.0, meaning that across all analyzed observations, positive and negative mentions are perfectly balanced. AI systems encounter Ellevest primarily in comparative or contextual frames, not in the positive, authoritative framing that generates shortlist placement.

For a platform built around serving a specific investor demographic, the inability to convert category presence into AI recommendation credit is a precise commercial problem. The buyers most likely to seek Ellevest's differentiated positioning are asking the same high-intent prompts this study tracks. They are receiving shortlists that do not include Ellevest as a recommended option. That gap, at $27.6 million in modeled monthly opportunity value across three clusters, is not recoverable through traditional marketing channels.

What This Means for the Category

Shortlist compression is the defining structural change in robo-advisor discovery. Two platforms are capturing the majority of AI recommendation value. The eight remaining platforms are not competing on equal terms for the remainder. They are competing for a fraction of buyer attention in a market where the top two are systematically advanced and the rest are systematically referenced without being recommended.

Competitor displacement is already operating at scale. A buyer who asks an AI system for the best robo-advisor in June 2026 is receiving a response where Betterment and Wealthfront appear first in the overwhelming majority of cases. Platforms that are not in that shortlist are not being considered, regardless of their actual product quality or pricing competitiveness. The AI response has already made the selection decision before the buyer reaches the platform's own marketing.

Trust-source dependency creates a compounding advantage for established leaders. The more consistently a platform is recommended, the more evidence of its recommendation accumulates across review and comparison sources, which in turn strengthens future AI recommendations. Platforms that are currently underperforming in AI shortlists face a structural disadvantage that grows over time without deliberate intervention in their public evidence architecture.

For platforms outside the top two, the path forward requires attention to entity clarity, source diversity, comparison content coverage, and sentiment consistency across the public evidence layer. These are not content marketing adjustments. They are infrastructure changes to how AI systems retrieve, evaluate, and rank each brand in response to high-intent buyer queries.

What This Public Benchmark Does Not Include

- Full cluster dataset for all 10 prompt clusters

- Prompt-level response tables showing which platforms win or lose specific queries

- Citation-source failure maps identifying which evidence sources are missing or weak

- Platform-by-platform recovery priorities across each AI system tracked

- Entity and schema diagnostics for structured data readiness

- Source-layer gap analysis across official, review, comparison, and community content

- Company-specific content recommendations for improving recommendation eligibility

- Exact competitor threat profiles showing displacement risk by prompt and platform

- Full paid opportunity model with platform-level value estimates

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

Methodology and Disclaimers

1. Market studied: Robo-Advisors, including automated investing platforms and digital advisory services.

2. Brands and entities included: Betterment, Wealthfront, Fidelity Go, Schwab Intelligent Portfolios, Acorns, Vanguard Digital Advisor, M1 Finance, Ellevest, SoFi Automated Investing, and Wealthsimple. The universe covers major U.S. robo-advisor platforms but may not include all regional or niche providers.

3. Data collection date and window: June 2026, snapshot taken June 18, 2026.

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

5. Observations analyzed: A total of 1,321 observations were analyzed across all platforms and clusters. Prompt count was not separately reported.

6. Prompt categories: Three public high-intent clusters were analyzed: Best Robo-Advisor & Top Automated Investing Platforms (consideration stage), Robo-Advisor Comparisons & Platform Alternatives (evaluation stage), and Robo-Advisor Pricing, Fees & Cost Evaluation (decision stage). The full report includes 10 clusters.

7. Definition of a mention: A mention means the company appeared in an AI-generated response, regardless of sentiment or ranking position.

8. Definition of a valid recommendation: A valid recommendation is a positive, shortlist-quality or ranked recommendation that earns recommendation credit. Visibility is not the same as recommendation credit.

9. Ranking and scoring metrics used: Valid recommendation coverage, top-three rate, rank-one rate, top-ten rate, average recommended rank, net sentiment score, monthly AI Authority Value, monthly AI Recommendation Value, monthly AI Visibility Assist Value, and captured share of AI opportunity.

10. Limitations: This is a point-in-time benchmark. AI outputs change with model updates, source changes, and query variation. Modeled values are estimates based on commercial intent proxies and do not represent actual revenue. This report is not a full audit or complete market census. The public version covers 3 of 10 clusters; the full report provides deeper analysis across all clusters and platforms.

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.