How AI Search Is Recommending Robo-Advisors: Monthly Trends

Executive Summary

Fidelity retains the category lead in October 2026 at 89.9% valid recommendation coverage, extending its advantage over second-place Charles Schwab to 1.8 points (89.9% against 88.1%). That is a far tighter top of the field than the July baseline, when Fidelity (83.4%) led Charles Schwab (79.6%) by 3.8 points and held a clear margin over the chasing pack. The two now sit within two points of one another at the top of the category.

Charles Schwab posted the second-largest series gain among the two leaders, up 8.5 points from 79.6% in July to 88.1% in October, and the sharper month-over-month move of the two, gaining 10.7 points against September. SoFi recorded the single largest series gain of any tracked brand, up 20.3 points from 33.8% in July to 54.1% in October, alongside Vanguard (up 16.6 points to 83.0%). Seven brands are classified as significant risers this month and two as stable.

A naming transition reshapes the lower half of the field. The tracked brand Acorns now reports as Acorns Grow Inc. (2.8% coverage, having appeared for the first time in October), Wealthfront now reports as Wealthfront Corporation (18.2%), and Betterment now reports as Betterment LLC (35.1%). The former entries (Acorns, Betterment, Wealthfront) show 0.0% current coverage and are classified as significant decliners, reflecting the reclassification rather than a verified loss of recommendation presence.

Four brands carry significant declines against the July baseline: M1 Finance (down 4.2 points to 11.5%) among continuously tracked names, alongside the three reclassified entries. Ellevest (1.2%) and Wealthsimple (0.2%) are stable. This is a category where the two leading brands narrowed the gap between them while the trailing names were reshaped by an entity-naming change.

Each monthly run begins with 800 prompt-surface observations across the benchmark's defined AI/search surface universe. Those observations resolve to 515 unique questions in July 2026 and 646 unique questions in October 2026. In both months, all 800 prompts mentioned a tracked brand or competitor. Of those, 786 were relevant and 14 irrelevant in July, versus 623 relevant and 177 irrelevant in October. The public metrics use the qualified observation counts that survive both qualification stages: 705 in July and 495 in October, with 687 in August and 677 in September in between.

Key Findings

AI Response Inconsistency Alerts

Questions This Section Answers

• Which robo-advisor claims did AI platforms answer incompatibly?
• What caused the Ellevest automated investing and Schwab options commission conflicts?
• Where did ChatGPT, AI Overviews, and Copilot disagree on account minimums and fees?

Five critical or high severity factual inconsistencies were detected across four AI platforms: ChatGPT, Copilot, AI Overviews, and AI Mode. Each involves a company in the tracked robo-advisor set, and each is a factual or pricing claim on which two platforms gave incompatible answers to the same question.

Ellevest

One critical inconsistency concerns automated investing and robo-advisor availability. When asked "What are the cons of Ellevest?", ChatGPT stated that Ellevest stopped offering its own automated investing service as of April 10, 2025, with qualifying accounts transferred to Betterment, citing NerdWallet and Ellevest's own support pages. AI Overviews, answering the same question, described an active digital robo-advisory service on a monthly subscription model of around $12 per month, citing ellevest.com, a Reddit thread, and a third-party review. The two claims cannot both be true. One flagged source, a CNBC review, was cited in support of the subscription-model side with an excerpt listing Ellevest membership tiers.

Charles Schwab

Two high severity inconsistencies were detected, both surfaced by the question "Is Charles Schwab good for a brokerage account?". On options trading commission, ChatGPT stated that Schwab's options trading costs $0.65 per contract, citing Schwab's own pricing page, while AI Overviews stated that investors pay $0 in commission fees to trade U.S. stocks, ETFs, and options. Flagged sources split on the point, with a NerdWallet review supporting the commission-free claim and an Investopedia review listing a $0.65 per-contract options fee.

On cryptocurrency availability, AI Overviews stated that you cannot buy or sell cryptocurrency directly through your Schwab account, while AI Mode described crypto access as limited, with spot Bitcoin and Ethereum rolling out progressively. A flagged Finder source supported the rollout claim, describing phased direct Bitcoin and Ethereum trading through 2026.

Fidelity

One high severity inconsistency concerns client assets under management. When asked "Who are the top 5 brokerage firms?", ChatGPT stated that Fidelity manages approximately $18.0 trillion in client assets, citing The Motley Fool and Kiplinger, while AI Overviews stated over $7.1 trillion in assets. A flagged Motley Fool source was marked as supporting both figures. The two cannot both describe the same asset base.

Vanguard

One high severity inconsistency concerns account minimums. When asked "What is the best place to open a Roth IRA?", ChatGPT stated that Vanguard has no investment minimum ($0 minimums), citing Fidelity's and Schwab's IRA pages as sources, while Copilot stated that Vanguard requires a $1,000 minimum to start, citing Business Insider and two other outlets. A flagged Business Insider source contained both figures, and a flagged Fidelity page carried a $0 minimum claim.

Benchmark Context

Questions This Section Answers

• Why is the October qualified observation count so much smaller than July's?
• How does the qualified benchmark set differ from the raw prompt collection?
• What does the recommendation-shaped answer share and shortlist share indicate about the benchmark?

The report separates the raw collection universe from the qualified analysis set. Brand-level recommendation percentages are calculated within the qualified benchmark set.

The qualified surface breadth held at all six canonical AI/search surface families in both the baseline and current months. August (687 qualified observations) and September (677) sit between the two endpoints, so the October qualified set is the smallest of the four months, reflecting a lower relevant share rather than a smaller collection.

Benchmark-Level Metrics

The recommendation-shaped answer share climbed across the series, from 38.2% in July to a low of 31.1% in August, then to 46.7% in September and 62.6% in October. The valid recommendation shortlist share followed a similar path, easing to 70.0% in August before rising to 75.2% in September and 91.3% in October. Both measures reached their series high in the current month.

AI Recommendation Trend

Questions This Section Answers

• How did recommendation coverage shift across the tracked robo-advisors from July to October?
• Which brands posted the largest gains, and how narrow is the gap between the top two?
• Which continuously tracked brand declined against the July baseline?

A Tightening Top: Fidelity and Charles Schwab Converge as the Mid-Field Rises

The category-level movement came from a broad combination of gains rather than one brand's shift. Seven brands posted significant increases against the July baseline: four continuously tracked names (Charles Schwab, Fidelity, SoFi, Vanguard) and three brands newly tracked under a legal entity name (Betterment LLC, Wealthfront Corporation, Acorns Grow Inc.). M1 Finance is the only continuously tracked brand to post a significant decline, down 4.2 points against baseline.

What Changed This Month

Questions This Section Answers

• What drove Fidelity's rank-one rate increase to 57.2%?
• Why did Charles Schwab's shortlist presence widen without a matching rank-one gain?
• How should the reclassified entities' "significant decliner" labels be read?

Fidelity

Fidelity's coverage rose from 83.4% in July to 89.9% in October, a significant gain of 6.5 points. The month-over-month move against September was also significant, up 6.1 points from 83.8%.

Raw presence reached 99.8% in October, meaning Fidelity appears in nearly every qualified observation, up 1.5 points from July's 98.3%. Its top-three rate climbed 31.4 points across the series, from 56.9% in July to 88.3% in October.

The signal worth noticing is rank-one placement. Fidelity's rank-one rate rose 15.9 points, from 41.3% in July to 57.2% rank-one rate in October, representing 283 rank-one placements. The leader is not only being shortlisted more often but is being placed first more often.

Highest-priority diagnostic: Which surfaces drove the rank-one increase, and whether that first-place share holds when Charles Schwab appears in the same shortlist.

Charles Schwab

Charles Schwab's coverage rose from 79.6% in July to 88.1% in October, a significant 8.5-point gain. Against September's 77.4%, that is a 10.7-point month-over-month increase, the largest single-month move of any continuously tracked brand this month, and it extends a two-month upward streak.

Raw presence reached 99.0% in October, up 5.2 points from July's 93.8%. Top-three rate rose 40.4 points, from 42.8% in July to 83.2% in October, or 412 top-three placements. Rank-one rate rose 12.8 points to 19.8%.

The distinction is that Charles Schwab widened its shortlist presence dramatically while its rank-one share, at 19.8%, remains well below Fidelity's 57.2%. It is now a near-constant alternative in AI shortlists but is placed first far less often than the leader.

Highest-priority diagnostic: On the prompts where both Fidelity and Charles Schwab appear, which one AI systems place first, and whether the answer differs by surface.

SoFi

SoFi posted the largest series gain of any brand, up 20.3 points from 33.8% in July to 54.1% in October, a significant increase. The month-over-month move against September's 35.3% was also significant, up 18.8 points, extending a two-month upward streak.

Raw presence rose from 39.1% in July to 61.0% in October, a 21.9-point increase, with 302 present observations. Top-three rate rose 10.1 points, from 4.7% to 14.8%. Rank-one rate held essentially flat, at 2.4% in October versus 2.5% in July.

SoFi's coverage gain came from both appearing in more answers and being recommended more often within them, but its rank-one share did not move. It is now a frequent shortlist name rather than a frequent first choice.

Highest-priority diagnostic: Which prompt types produce SoFi shortlist placements, and which competitor occupies the rank-one slot within those same prompts.

Vanguard

Vanguard's coverage rose from 66.4% in July to 83.0% in October, a significant 16.6-point gain, with a 17.6-point month-over-month increase against September's 65.4% extending a two-month streak.

Raw presence rose from 80.4% to 93.7%. Top-three rate rose 35.7 points, from 28.5% in July to 64.2% in October, or 318 top-three placements. Rank-one rate rose 4.6 points to 6.9%.

Vanguard moved from a mid-field position into the top three by coverage in October, but like Charles Schwab and SoFi its rank-one share remains modest. The increase is concentrated in shortlist inclusion rather than first-place placement.

Highest-priority diagnostic: Whether Vanguard's shortlist gains came at the expense of a single competitor or were spread across several.

M1 Finance

M1 Finance's coverage declined from 15.7% in July to 11.5% in October, a significant 4.2-point drop against baseline, though the month-over-month move against September's 11.4% was flat, up 0.1 point and within normal variation. The decline happened earlier in the series, not this month.

Raw presence fell from 18.0% in July to 13.3% in October, down 4.7 points. Top-three rate was essentially flat, at 1.4% in July against 2.0% in October, and rank-one rate held near zero.

M1 Finance's absolute counts are small: 57 valid recommendations in October, with 66 present observations. The 4.2-point series decline reflects a thin presence base, and the current-month stabilization should be read against that scale.

Highest-priority diagnostic: Which prompt classes stopped surfacing M1 Finance earlier in the series, and whether that pattern has now halted.

The Reclassified Entities

Three brands in the tracked set now report under legal entity names that did not appear in the prior months: Betterment LLC (35.1% coverage, rank 5th), Wealthfront Corporation (18.2%, rank 6th), and Acorns Grow Inc. (2.8%, rank 8th). Their former entries, Betterment, Wealthfront, and Acorns, each show 0.0% current coverage and are classified as significant decliners because the series carries both identifiers.

Betterment LLC recorded 181 present observations and 174 valid recommendations in October. Wealthfront Corporation recorded 96 present observations and 90 valid recommendations. Acorns Grow Inc. recorded 23 present observations and 14 valid recommendations.

The distinction the reader should notice is that these movements reflect a change in how the tracked entities are named within the benchmark, not a verified collapse in recommendation presence. Comparisons between the old and new identifiers should be treated as an entity-naming transition rather than a competitive loss.

Highest-priority diagnostic: Whether the entity-name change altered which prompts surface each brand, or whether the underlying prompt set is unchanged under the new identifier.

Buyer-Intent Interpretation

Questions This Section Answers

• Which buyer-intent clusters produced qualified observations in this series?
• Why can't the benchmark yet answer pricing or direct comparison questions?

In October, every qualified observation again fell into the Brand Recommendation cluster, capturing discovery and consideration intent. The Pricing & Value and Multi-Brand Comparison clusters carried no qualified observations in any month of the series.

The public benchmark therefore shows how AI systems recommend robo-advisors when buyers ask for a recommendation outright. It cannot yet answer commercial questions about price positioning, fees, or direct brand-to-brand comparison, and those questions require company-level analysis with a broader prompt set targeting pricing and comparison intents.

Brand Opportunity Summary

The benchmark identifies where attention is warranted; a company-level analysis is needed to explain why.

Evidence Behind the Benchmark

Questions This Section Answers

• What types of prompt-level observations build the aggregate metrics?
• Does source presence in AI answers prove causation?

The aggregate metrics are built from prompt-level observations (query, surface, recommendation outcome, rank, sentiment, and citations where exposed). Company-level analysis can go deeper into prompt, competitor, surface, and evidence patterns. Source presence is not automatically treated as proof of causation.

About This Benchmark

This report is part of the CiteWorks Studio AI Visibility Market Discovery research program.

• AI Visibility Industry Market Methodology
• AI Visibility Industry Market Metrics
• AI Visibility Industry Market Standards

Report-Specific Interpretation Notes

• Small-count movements apply to Ellevest (6 valid recommendations), Wealthsimple (1 valid recommendation), M1 Finance (57 valid recommendations), and Acorns Grow Inc. (14 valid recommendations); each should be read with its absolute count in mind.
• The qualified denominator (705 in July, 687 in August, 677 in September, 495 in October) differs from the raw collection of 800 each month; percentages use the qualified set.
• The entity-name transition across Acorns, Betterment, and Wealthfront means their series endpoints carry different identifiers; movement should be read as a naming change rather than a verified presence loss.
• Month-over-month movement identifies changes worth investigating; it does not by itself establish the cause of those changes.

Next Step

The Public Benchmark Shows Where a Brand Is Winning or Losing. A Company-Level Audit Shows Why.

The aggregate percentage answers which brands AI systems recommend and how that shifted from July to October. It does not yet answer the questions beneath the numbers: which high-intent prompts are won and lost, which competitor takes the recommendation when a brand loses, what attributes AI associates with each option, and which external sources shape those answers across the six canonical AI/search surface families.

A company-specific AI visibility audit maps those prompt, surface, competitor, ranking, sentiment, and evidence-source patterns into a prioritized visibility strategy.

Request an AI visibility audit