First Horizon Bank AI Market Strategy Report - Consumer Banking

Answer Capsule

First Horizon Bank holds 5.1% valid recommendation coverage in the September 2026 Consumer Banking benchmark, placing it in a tie for third position among ten tracked institutions. The bank converts a mid-tier presence rate of 13.0% into recommendation credit with unusual efficiency, earning an average recommended rank of 1.69, the strongest placement quality in the category. Its rank-one rate of 2.2% trails only category leader Regions Bank, and 13 of its 14 valid recommendations landed in the top three. The clearest weakness is coverage breadth, where First Horizon Bank sits well behind the two leading banks. The clearest opportunity is extending its high-quality placement pattern into a wider set of high-intent prompts.

Who This Report Is For

This report is for consumer banking executives, digital strategy leaders, and brand teams evaluating how AI-generated recommendations are shaping bank selection and where First Horizon Bank stands in AI-led discovery.

Report Card

Executive Summary

First Horizon Bank enters the September 2026 Consumer Banking benchmark with a distinctive profile: modest coverage, exceptional placement quality, and a clean sentiment record. The bank holds 5.1% valid recommendation coverage, tied with Old National Bank for third position, but its average recommended rank of 1.69 is the strongest among all tracked banks with valid recommendations. This means that when AI systems recommend First Horizon Bank, they tend to place it near the top of the shortlist.

The bank recorded 36 mentions across 277 qualified observations, a 13.0% raw mention presence rate. Of those mentions, 19 were positive, 17 were neutral, and none were negative, producing a net sentiment score of 0.53. First Horizon Bank earned 14 valid recommendation mentions, with 13 landing in the top three and 6 earning the first position. Its rank-one rate of 2.2% trails only Regions Bank in the category.

The strongest cluster for First Horizon Bank is the Brand Recommendation class, which captured all 277 qualified observations in September 2026. The public benchmark contains no qualified observations in the Pricing & Value or Multi-Brand Comparison clusters, so the bank's performance in those discovery contexts remains unmeasured. The strongest platform signal is Google AI Overviews, where First Horizon Bank earned 8 of its 14 valid recommendations with an average rank of 1.88. The clearest platform gap is Perplexity, where the bank appeared once without earning recommendation credit.

The benchmark shows a category moving from an empty field in July 2026 to a structured hierarchy by September 2026. First Horizon Bank's two-month rise of 5.1 points from its July baseline of 0.0% is classified as significant. The bank's challenge is no longer visibility; it is converting its efficient placement pattern into broader recommendation coverage across more prompts and platforms.

What First Horizon Bank Is Winning

First Horizon Bank's clearest win is placement quality. Its average recommended rank of 1.69 is the strongest in the category, ahead of Regions Bank at 2.78 and Flagstar Bank at 3.30. Thirteen of its 14 valid recommendations landed in the top three, a conversion rate that no other tracked bank matches at similar coverage levels.

The bank also holds a strong rank-one rate of 2.2%, with 6 first-position placements out of 277 qualified observations. This trails only Regions Bank at 3.2% and exceeds Flagstar Bank at 0.4%, despite Flagstar holding more than twice the coverage. First Horizon Bank is winning the first-choice moment more often than its overall coverage would suggest.

The bank's sentiment profile is clean. It recorded zero negative mentions in September 2026, with 19 positive mentions against 17 neutral ones. Its net sentiment score of 0.53 places it among the more favorably framed banks in the category.

Where First Horizon Bank Has the Clearest AI Visibility Gaps

Questions This Section Answers

• How far does First Horizon Bank trail the category leaders in recommendation coverage?
• Where is First Horizon Bank appearing in AI responses without earning recommendation credit?

First Horizon Bank's primary gap is coverage breadth. Its 5.1% valid recommendation coverage trails Regions Bank at 17.3% by 12.2 percentage points and Flagstar Bank at 12.6% by 7.5 percentage points. The bank appears in AI responses at a 13.0% rate but converts only a portion of that presence into recommendation credit, a pattern that suggests it is often mentioned as context rather than selected as the answer.

The Perplexity platform shows a notable gap. First Horizon Bank appeared once on Perplexity in September 2026 without earning a valid recommendation, while competitors like Regions Bank and Flagstar Bank each earned 3 valid recommendations on that platform. The bank's presence on Perplexity is minimal relative to its performance elsewhere.

The bank also shows limited presence in Google AI Mode, where it earned 3 valid recommendations out of 94 observations, a 3.2% coverage rate that trails its overall performance. Its presence rate on that platform was 7.4%, suggesting room to convert more of its AI Mode visibility into recommendation credit.

Biggest Opportunity

Questions This Section Answers

• What would it take for First Horizon Bank to close ground on the two category leaders?

First Horizon Bank's biggest opportunity is extending its top-three placement pattern into broader prompt coverage. The bank has proven it can win prominent positions when recommended, but it is not being recommended often enough. The diagnostic question is which high-intent prompts produce its 14 valid recommendations and whether that pattern can be replicated across the prompts where the bank currently appears without recommendation credit. If the bank can raise its coverage toward the 10% range while maintaining its average rank near 1.69, it would close meaningful ground on the two category leaders.

Competitive Landscape

Questions This Section Answers

• Where does First Horizon Bank rank against the other tracked banks on placement quality?

Regions Bank and Flagstar Bank hold the strongest recommendation-stage positions in the Consumer Banking category, with Regions Bank leading at 17.3% valid recommendation coverage. First Horizon Bank sits in a mid-tier cluster with Old National Bank at 5.1%, behind the two leaders but ahead of the remaining tracked banks.

Average recommended rank covers rank-eligible recommendations only.

First Horizon Bank's top-three rate of 4.69% is close to Flagstar Bank's 5.78% despite a coverage gap of 7.5 percentage points. Its rank-one rate of 2.17% is the second strongest in the category, and its average recommended rank of 1.69 is the best among all tracked banks with valid recommendations. The numbers show a bank that wins prominent placement when it is recommended but is not yet recommended across a wide enough set of prompts.

Prompt Evidence

Questions This Section Answers

• Which prompts earned First Horizon Bank recommendation credit, and where did it appear without converting?

Google AI Overviews / Brand Recommendation Prompt: "best consumer banking options" Result: First Horizon Bank appeared in recommendation shortlists with strong placement, earning 8 valid recommendations with an average rank of 1.88.

ChatGPT / Brand Recommendation Prompt: "open bank account online" Result: First Horizon Bank earned 1 valid recommendation with a rank-one placement, showing it can win the top position on high-intent account-opening prompts.

Perplexity / Brand Recommendation Prompt: "top bank recommendations" Result: First Horizon Bank appeared once without earning recommendation credit, a presence-without-conversion pattern on a platform where competitors earned multiple valid recommendations.

What CiteWorks Studio Would Do Next

Phase 1: AI Market Discovery Audit Map the specific prompts and surfaces where First Horizon Bank earns its 14 valid recommendations and identify where it appears without recommendation credit.

Phase 2: Recommendation Readiness Plan Strengthen the pages and messaging that support the bank's strongest placement patterns, focusing on the attributes AI systems associate with its top-three recommendations.

Phase 3: Owned Answer Layer Buildout Develop owned content that answers high-intent consumer banking prompts directly, giving AI systems clearer material to cite when recommending First Horizon Bank.

Phase 4: Citation / Authority Layer Development Build the external evidence layer that supports the bank's recommendation eligibility, particularly on platforms like Perplexity where its presence is currently thin.

Phase 5: Monthly AI Visibility and Recommendation Tracking Track whether expanded coverage maintains the bank's average recommended rank near 1.69 as recommendation volume grows.

Why This Matters

Questions This Section Answers

• Why does First Horizon Bank's mention rate overstate its position in AI-driven bank selection?

AI-generated recommendations are becoming a primary input into consumer banking choices. First Horizon Bank has proven it can win prominent placement when recommended, but its current coverage means it is absent from most recommendation shortlists. Presence alone is not enough, and the bank's 13.0% mention rate with 5.1% recommendation coverage shows the gap between being named and being chosen.

The next move is targeted correction of the prompt, page, and citation layers to convert more of the bank's existing visibility into recommendation credit. First Horizon Bank does not need to fix a placement quality problem; it needs to replicate its winning pattern across more of the discovery moments where buyers are forming their shortlists.

Core Metrics

Sentiment Score

Sentiment Score = (positive mentions × 1 + neutral mentions × 0 + negative mentions × -1) / total mentions

For First Horizon Bank, the calculation is (19 × 1 + 17 × 0 + 0 × -1) / 36, producing a net sentiment score of 0.53. This measures framing quality across AI responses, not customer sentiment.

Classified sentiment matters because 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, and counting all mentions as wins is bad measurement. Classified sentiment is required before interpreting AI visibility.

Sentiment by Platform

Methodology

1. This report is based on the LLM Authority Index AI Market Discovery Index for Consumer Banking, September 2026 measurement, with interpretation and strategy provided by CiteWorks Studio as a separate function.
2. The reporting window is September 2026, with July 2026 as the baseline month and August 2026 referenced where it clarifies the September result.
3. Five AI and search surface families recorded qualified observations: ChatGPT, Copilot, Perplexity, Google AI Mode, and Google AI Overviews. Gemini recorded no qualified observations in September 2026.
4. The benchmark analyzed 277 qualified observations from 700 source prompt-surface observations, with 538 unique questions and 605 relevant prompts.
5. The competitor universe includes 10 tracked banks: Regions Bank, Flagstar Bank, First Horizon Bank, Old National Bank, Pinnacle Financial Partners, Santander Bank, City National Bank, East West Bank, Webster Bank, and Zions Bank.
6. All 277 qualified observations fell into the Brand Recommendation buyer-intent class. The public benchmark does not yet contain qualified observations in the Pricing & Value or Multi-Brand Comparison classes.
7. Stage 0 extraction captured raw prompt-surface observations, which were then qualified against benchmark scope and relevance criteria.
8. A mention is defined as any appearance of a tracked brand in an AI response, regardless of context or position.
9. A valid recommendation is defined as a brand appearing in a recommendation shortlist within a qualified observation, distinct from a neutral reference or comparison anchor.
10. The public benchmark does not measure market share, attributable sales, every possible AI response, organic-search ranking, social mention volume, or causality from metric movement alone.
11. Small counts matter in this vertical. First Horizon Bank's 14 valid recommendations are meaningful, while single-digit counts for other banks are included because they represent the full picture for those brands.
12. The benchmark records what AI systems surfaced in September 2026; it does not explain why those systems produced those outputs.

See How AI Is Recommending Your Brand

The public benchmark shows where First Horizon Bank stands in AI-generated recommendations, but a company-level audit can map the specific prompts, surfaces, and evidence sources behind those results. Understanding which high-intent questions produce top-three placement, and where competitors are being recommended instead, is the first step toward converting visibility into recommendation credit.