Fidelity AI Market Strategy Report - Online Stock Brokers

Answer Capsule

Fidelity holds the highest rank-one recommendation rate in the online stock broker category at 21.2%, with the best average rank of 1.48 when recommended. However, its overall recommendation coverage of 30.2% is significantly lower than Charles Schwab's 52.5%, limiting total captured AI authority value. Fidelity's clearest win is its ability to secure the top shortlist position when it appears, but its narrower presence across AI platforms and prompt clusters leaves substantial opportunity on the table. The clearest opportunity is expanding retrievability and source-layer signals to increase the frequency of appearances across all buyer stages.

Who This Report Is For

This report is for Fidelity's marketing, digital strategy, and competitive intelligence teams evaluating the brand's position in AI-generated broker recommendations and shortlists.

Report Card

• Report type: AI Company Market Strategy Report
• Target company: Fidelity
• Category / market studied: Online Stock Brokers
• Reporting month: June 2026
• AI platforms tracked: ChatGPT, Copilot, Gemini, Google AI Mode, Google AI Overviews, Perplexity
• Public high-intent clusters: 3 (Discovery, Comparison, Pricing Evaluation)
• AI observations analyzed: 1,479
• Competitors tracked: 9 (Charles Schwab, Robinhood, Interactive Brokers, Vanguard, Webull, E*TRADE, Public, Tastytrade, Merrill Edge)

Executive Summary

Fidelity occupies a strong but incomplete position in the AI recommendation landscape for online stock brokers. The June 2026 LLM Authority Index benchmark shows Fidelity achieving the highest rank-one rate in the category at 21.2% and the best average recommended rank at 1.48. When Fidelity appears in an AI-generated shortlist, it is most often the first option. This is a meaningful signal of recommendation quality and buyer-stage influence.

However, Fidelity's overall recommendation coverage of 30.2% trails Charles Schwab's 52.5% by a wide margin. Fidelity appears in 43.5% of all AI responses but converts that presence into valid recommendations at a rate that leaves room for improvement. The monthly AI Authority Value of $555.9K places Fidelity fourth in the category behind Charles Schwab ($1.57M), Robinhood ($635.6K), and Interactive Brokers ($610.4K).

Fidelity's strongest cluster is the Discovery stage, where it achieves a 31.0% top-three rate and a 23.2% rank-one rate. Its weakest cluster is the Comparison stage, where the top-three rate drops to 23.3% and recommendation coverage falls to 26.7%. The strongest platform signal comes from Copilot, where Fidelity achieves a 59.5% top-three rate and a 46.6% rank-one rate. The clearest platform gap is on ChatGPT, where Fidelity appears in only 14.6% of responses with an 8.5% recommendation coverage rate.

Fidelity's net sentiment score of 0.8292 is among the highest in the category, with zero negative mentions across all 1,479 observations. This indicates that when AI systems reference Fidelity, the framing is overwhelmingly positive. The challenge is not how Fidelity is described but how often it is retrieved and recommended relative to competitors.

What Fidelity Is Winning

Fidelity wins the rank-one position more consistently than any other broker in the category. With a 21.2% rank-one rate and an average recommended rank of 1.48, Fidelity is the most likely broker to appear first when it earns a shortlist position. This is a structural advantage in buyer consideration, as the first recommendation in an AI response carries disproportionate influence.

Fidelity shows exceptional performance on Copilot, where it achieves a 59.5% top-three rate and a 46.6% rank-one rate across 247 observations. This is the strongest single-platform performance for any broker in the category. On Copilot, Fidelity appears in 79.8% of responses and converts that presence into valid recommendations at a 63.2% coverage rate.

Fidelity maintains a perfect positive framing record. Across 644 total appearances, Fidelity received zero negative mentions and only 110 neutral mentions. The net sentiment score of 0.8292 reflects consistent positive framing across all platforms and clusters. This is a significant advantage over competitors like E*TRADE (0.5644) and Merrill Edge (0.2148), which face more mixed or cautionary treatment.

In the Discovery cluster, Fidelity achieves a 23.2% rank-one rate and a 31.0% top-three rate, placing it second only to Charles Schwab in awareness-stage recommendation power. This cluster represents the largest monthly opportunity at $4.1M across the category, and Fidelity captures a meaningful share of it.

Where Fidelity Has the Clearest AI Visibility Gaps

Fidelity's most significant gap is overall recommendation coverage relative to Charles Schwab. Schwab appears in 72.7% of all AI responses and earns valid recommendations in 52.5% of them. Fidelity appears in 43.5% of responses and earns valid recommendations in 30.2% of them. This means Schwab is retrieved and recommended in more than half of all AI interactions, while Fidelity is recommended in fewer than one in three.

The Comparison cluster is Fidelity's weakest buyer stage. In this consideration-stage cluster, Fidelity's top-three rate drops to 23.3% and its recommendation coverage falls to 26.7%. Charles Schwab leads this cluster with a 41.3% top-three rate, and Interactive Brokers shows a 27.5% top-three rate. Fidelity is being displaced in comparison prompts where buyers are actively evaluating alternatives.

ChatGPT represents Fidelity's largest platform gap. On ChatGPT, Fidelity appears in only 14.6% of responses with an 8.5% recommendation coverage rate. This is dramatically lower than its performance on Copilot (79.8% presence, 63.2% coverage) and Perplexity (67.8% presence, 40.0% coverage). Given ChatGPT's broad user base, this gap likely limits Fidelity's exposure to a significant portion of AI-assisted broker research.

Fidelity's presence on Google AI Mode is also limited at 15.4% with an 11.4% recommendation coverage rate. While the rank-one rate on this platform is strong at 9.8%, the narrow presence means Fidelity is missing opportunities on a growing AI search surface.

The Pricing Evaluation cluster shows a gap in top-three positioning. Fidelity achieves a 29.4% top-three rate in this decision-stage cluster, compared to Charles Schwab's 48.2% and Interactive Brokers' 36.4%. Given that pricing prompts carry the highest buyer-stage multiplier, this gap represents a meaningful loss of influence at the decision moment.

Biggest Opportunity

Fidelity's biggest opportunity is expanding its retrievability and source-layer signals to increase the frequency of appearances across all platforms and prompt clusters. Fidelity already wins when it appears, with the best rank-one rate and average rank in the category. The constraint is not recommendation quality but recommendation frequency.

The data suggests Fidelity has a narrower public evidence footprint than Charles Schwab, which appears in 72.7% of responses versus Fidelity's 43.5%. Strengthening the citation architecture through comparison content, editorial coverage, third-party validation, and structured data would increase the likelihood that AI systems retrieve and recommend Fidelity across more prompts and platforms. The ChatGPT gap alone, where Fidelity appears in only 14.6% of responses, represents a significant addressable opportunity.

Prompt Evidence

Copilot / Discovery Prompt: "What is the best brokerage for beginners?" Result: Fidelity appeared as the first recommendation with a rank-one position, reflecting its strongest platform performance across the full benchmark.

ChatGPT / Comparison Prompt: "Compare Fidelity vs Schwab for retirement investing" Result: Fidelity was mentioned but appeared outside the top-three positions, consistent with its lower recommendation coverage on ChatGPT.

Perplexity / Pricing Evaluation Prompt: "Which brokerage has the lowest fees for active traders?" Result: Fidelity was recommended in the top three, showing strong performance on Perplexity where it achieves a 35.5% top-three rate.

Gemini / Discovery Prompt: "Best online stock brokers for 2026" Result: Fidelity appeared in the response but was not positioned as a top recommendation, reflecting its 19.3% top-three rate on Gemini.

What CiteWorks Studio Would Do Next

Phase 1: AI Market Discovery Audit Map Fidelity's full recommendation profile across all 10 buyer-stage clusters to identify the specific prompts and platforms where retrievability is weakest.

Phase 2: Recommendation Readiness Plan Identify the source-layer gaps that limit Fidelity's appearance frequency, particularly on ChatGPT and Google AI Mode, and prioritize the citation types that drive recommendation credit.

Phase 3: Owned Answer Layer Buildout Develop structured, AI-optimized owned content that improves Fidelity's retrievability for comparison and pricing prompts where it currently underperforms.

Phase 4: Citation / Authority Layer Development Strengthen third-party validation signals through editorial coverage, comparison content, and review platform presence to expand the public evidence layer.

Phase 5: Monthly AI Visibility and Recommendation Tracking Monitor Fidelity's recommendation coverage, rank-one rate, and platform-level performance to measure progress and adjust strategy as AI systems evolve.

Why This Matters

Fidelity has the best recommendation quality in the online stock broker category. When AI systems recommend Fidelity, they place it first more often than any competitor. But recommendation quality does not matter if the brand is not being retrieved in the first place.

The gap between Fidelity's rank-one rate and its overall recommendation coverage means the brand is winning the shortlist positions it earns but earning fewer shortlist positions than its market position would suggest. As AI-led discovery becomes more central to how investors research and choose brokers, Fidelity's ability to expand its retrievability across platforms and prompt clusters will determine whether it maintains its competitive position or cedes ground to brokers with broader AI footprints.

Core Metrics

• Mentions: 644
• Valid recommendations: 447
• Top 3 recommendation count: 412
• Rank 1 recommendation count: 313
• Average recommended rank: 1.48
• Positive mentions: 534
• Neutral mentions: 110
• Negative mentions: 0
• Raw mention presence rate: 43.5%
• Valid recommendation coverage: 30.2%
• Top 3 recommendation rate: 27.9%
• Rank 1 recommendation rate: 21.2%
• Strongest cluster by recommendation behavior: Discovery (C01)
• Strongest platform by recommendation behavior: Copilot

Sentiment Score

Sentiment Score = (534 x 1 + 110 x 0 + 0 x -1) / 644 = 0.8292

This score means 82.9% of Fidelity's mentions carry positive framing, with no negative mentions recorded. This is among the highest sentiment scores in the category and reflects consistent positive treatment across all platforms and clusters.

Why this matters: unclassified mention counts are misleading. Share of voice is a diagnostic metric, not a business KPI. A positive recommendation, neutral reference, cautionary mention, and competitor-displaced mention are not equal. Counting all mentions as wins is bad measurement. Classified sentiment is required before interpreting AI visibility. Fidelity's high sentiment score confirms that when it is mentioned, the framing supports buyer consideration. The constraint is the frequency of those mentions, not their quality.

Sentiment by Platform

Methodology

1. Market studied: Online Stock Brokers, including full-service, discount, and commission-free brokerage platforms.
2. Brands and entities included: Charles Schwab, Fidelity, Robinhood, Interactive Brokers, Vanguard, Webull, E*TRADE, Public, Tastytrade, and Merrill Edge. This universe covers the major publicly traded and privately held brokers in the U.S. market but is not a full market census.
3. Data collection window: June 2026, based on a snapshot of AI platform outputs during that reporting period.
4. AI platforms tested: ChatGPT, Copilot, Gemini, Google AI Mode, Google AI Overviews, and Perplexity.
5. Observation count: 1,479 total AI observations analyzed across three public high-intent clusters. Unique prompt count was not available in the public version of the benchmark.
6. Prompt categories: Discovery (awareness-stage), Comparison (consideration-stage), and Pricing Evaluation (decision-stage) prompts representing the buyer journey from initial research to purchase decision. The full benchmark covers 10 clusters; this public version covers 3.
7. Definition of a mention: A mention means the company appeared in an AI-generated response, regardless of sentiment, rank, or recommendation status.
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. Neutral references, cautionary mentions, and competitor-displaced appearances do not qualify as valid recommendations.
9. Ranking and scoring metrics used: Valid recommendation coverage, top-three rate, rank-one rate, average 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 can change with model updates, data source changes, and content shifts. Modeled values are estimates based on commercial intent proxies and are not revenue, pipeline, or booked demand. This report reflects the public three-cluster version of the benchmark and is not a full audit or full market census.

See How AI Is Recommending Your Brand

The benchmark reveals where Fidelity wins shortlist positions and where competitors are being recommended instead. For a brand with the highest recommendation quality in the category, the path forward is clear: expand retrievability to match that recommendation quality. CiteWorks Studio can map Fidelity's full AI recommendation profile, identify the specific source-layer gaps limiting appearance frequency, and build the citation architecture needed to close the gap with category leaders.