22nd State Bank AI Visibility Market Strategy Report - Consumer Banking

Answer Capsule

22nd State Bank recorded no measurable AI visibility in the October 2026 Consumer Banking benchmark. Across 311 qualified observations, the bank registered a 0.00% raw mention presence rate, 0.00% valid recommendation coverage, and no top-three or rank-one placements on any tracked AI platform. The category leader, Regions Bank, holds 41.2% valid recommendation coverage, a gap of 41.2 percentage points over 22nd State Bank. The clearest opportunity is foundational: establishing any presence in the Brand Recommendation prompt class where every qualified observation in this benchmark currently sits.

Who This Report Is For

This report is for 22nd State Bank leadership, marketing, and strategy teams evaluating where the bank stands in AI-generated recommendations for consumer banking, and for anyone assessing competitive visibility at the decision moment in this category.

Report Card

Executive Summary

22nd State Bank has no measurable presence in the October 2026 Consumer Banking AI visibility benchmark. Across 311 qualified observations spanning six AI platforms, the bank recorded zero mentions, zero valid recommendations, zero top-three placements, and zero rank-one placements. Its raw mention presence rate is 0.00%, and its valid recommendation coverage is 0.00%.

The benchmark tracks seven consumer banks. Six of them registered at least some presence in October 2026. 22nd State Bank and Century Bank are the only two brands with no measurable footprint in the qualified observation set. This places 22nd State Bank outside the recommendation shortlist entirely for the prompts measured in this benchmark.

The category leader, Regions Bank, holds 41.2% valid recommendation coverage and a 31.8% top-three rate. United Bank, the second-ranked brand, holds 6.4% coverage. The gap between 22nd State Bank and even the lowest-ranked brand with measurable coverage, River Bank & Trust at 0.6%, is the difference between no presence and some presence.

All 311 qualified observations in October 2026 fell into the Brand Recommendation buyer-intent class. This means the benchmark measured prompts asking which bank to choose or which banks are best. The Pricing & Value and Multi-Brand Comparison classes recorded no qualified observations, so the benchmark cannot yet report on how AI systems position 22nd State Bank for pricing, fees, or head-to-head comparison prompts.

The strongest platform signal in the category belongs to Regions Bank, which captured 57.8% valid recommendation coverage on AI Overviews and 50.0% on Perplexity. 22nd State Bank registered no presence on any of the six tracked platforms: ChatGPT, Copilot, Gemini, Perplexity, AI Overviews, or AI Mode.

The clearest gap is foundational. Before 22nd State Bank can compete for recommendation placement, it must first appear in AI-generated responses at all. The benchmark data suggests the bank is not currently part of the public evidence layer that AI systems draw from when forming consumer banking recommendations.

What 22nd State Bank Is Winning

Questions This Section Answers

• Did 22nd State Bank record any mentions, recommendations, or top-three placements in the October 2026 benchmark?
• Is the bank's zero sentiment score a positive signal or a null result?

The October 2026 benchmark data does not show any measurable wins for 22nd State Bank. The bank recorded no mentions, no valid recommendations, no top-three placements, and no rank-one placements across any tracked platform or cluster.

This is stated plainly because the evidence supports no other conclusion. The absence of negative sentiment is not a win; it reflects the absence of any mention at all. The bank's net sentiment score is 0.0000, but this is a null result, not a positive signal.

The only structural observation that can be made is that 22nd State Bank has no negative framing to correct. Unlike a brand that appears in AI responses with cautionary or unfavorable context, 22nd State Bank simply does not appear. The remediation path is therefore about building presence rather than repairing reputation.

Where 22nd State Bank Has the Clearest AI Visibility Gaps

Questions This Section Answers

• On which AI platforms and clusters is 22nd State Bank completely absent?
• How does the bank's zero presence compare with small-footprint competitors like River Bank & Trust and Bryant Bank?
• What does the citation footprint suggest about why 22nd State Bank is not being surfaced?

22nd State Bank is absent from every platform and every cluster measured in the October 2026 benchmark. This is the clearest possible visibility gap: the bank is not present in AI-generated recommendations for consumer banking at all.

The benchmark's qualified observation set covers six AI platforms. On ChatGPT, Copilot, Gemini, Perplexity, AI Overviews, and AI Mode, 22nd State Bank recorded zero mentions. By contrast, Regions Bank appeared in 80.1% of qualified observations and converted that presence into a top-three recommendation in 31.8% of cases.

The gap is not a matter of weak recommendation conversion. It is a matter of no presence to convert. Where Regions Bank is visible but sometimes under-recommended relative to its presence, 22nd State Bank is neither visible nor recommended.

The benchmark also shows that competitor brands with very small footprints can still register measurable presence. River Bank & Trust, with a 1.0% raw mention presence rate and 0.6% valid recommendation coverage, appeared in the qualified observation set. Bryant Bank, despite a month-over-month decline, still recorded 7 mentions and 6 valid recommendations. 22nd State Bank recorded none.

The source layer offers one additional signal. The top ten cited domains in the benchmark include regions.com, hancockwhitney.com, and bankwithunited.com, the owned domains of three tracked competitors. No domain associated with 22nd State Bank appears in the top ten cited sources. This suggests the bank's public evidence layer is not currently surfacing in the citation footprint that AI systems draw from for this category.

Biggest Opportunity

Questions This Section Answers

• Which buyer-intent prompt class offers 22nd State Bank the clearest path to first presence?
• What does the bank need to build before it can compete for recommendation placement?

The single biggest opportunity for 22nd State Bank is to establish baseline presence in the Brand Recommendation prompt class, which is the only buyer-intent class with qualified observations in the October 2026 benchmark. This class captures prompts asking which bank to choose or which banks are best. Every tracked brand's coverage, top-three, and rank-one figures in this benchmark come from these discovery and consideration prompts.

For 22nd State Bank, the path from zero to presence requires building the owned answer layer and citation architecture that AI systems can retrieve and synthesize. The benchmark's evidence and source layer shows that AI systems draw from a mix of search engines, review and comparison sites, user-generated platforms, and brand-owned domains. Competitors with measurable presence have owned domains appearing in the citation footprint. 22nd State Bank does not.

The opportunity is not to outrank Regions Bank immediately. It is to become part of the consideration set at all, so that when a buyer asks an AI system for a consumer banking recommendation, 22nd State Bank has a chance to appear.

Competitive Landscape

Questions This Section Answers

• Who leads the October 2026 Consumer Banking benchmark, and by how wide a margin?
• Which brands sit outside the recommendation set entirely?

Regions Bank holds dominant recommendation-stage strength in the October 2026 Consumer Banking benchmark, with 41.2% valid recommendation coverage and a 31.8% top-three rate. United Bank is the strongest challenger at 6.4% coverage. 22nd State Bank and Century Bank sit outside the recommendation set entirely, with no measurable presence.

Average recommended rank covers rank-eligible recommendations only.

22nd State Bank and Century Bank are the only two brands in the tracked set with no top-three placements, no rank-one placements, and no rank-eligible recommendations. The table shows 22nd State Bank at the bottom of the competitive set alongside Century Bank, with no measurable recommendation-stage presence to report.

Prompt Evidence

ChatGPT / Brand Recommendation Prompt: "What is the best bank for senior citizens?" Result: 22nd State Bank was not mentioned. Regions Bank appeared in 82.8% of ChatGPT observations and held a 20.7% top-three rate on this platform.

AI Overviews / Brand Recommendation Prompt: "high yield savings account" Result: 22nd State Bank was not mentioned. Regions Bank recorded a 53.5% top-three rate and a 30.2% rank-one rate on AI Overviews, the platform where it holds its strongest recommendation position.

Gemini / Brand Recommendation Prompt: "local banks near me" Result: 22nd State Bank was not mentioned. United Bank recorded a 7.9% rank-one rate on Gemini, the highest rank-one rate for any non-leader brand on that platform.

Perplexity / Brand Recommendation Prompt: "open a checking account online" Result: 22nd State Bank was not mentioned. Regions Bank held a 50.0% valid recommendation coverage rate on Perplexity, and River Bank & Trust recorded its only rank-one placement on AI Overviews.

What CiteWorks Studio Would Do Next

Phase 1: AI Visibility Market Discovery Audit Map every prompt in the consumer banking category where 22nd State Bank should appear, identify which competitors are being recommended instead, and establish a baseline for the bank's current presence across all six tracked AI platforms.

Phase 2: Recommendation Readiness Plan Define the specific prompt classes, buyer-intent contexts, and geographic or product niches where 22nd State Bank has the clearest path to first recommendation, prioritizing the Brand Recommendation class where all qualified observations currently sit.

Phase 3: Owned Answer Layer Buildout Develop the pages, structured content, and factual reference material that AI systems can retrieve and synthesize when forming consumer banking recommendations, starting with the products and services most likely to trigger high-intent prompts.

Phase 4: Citation and Authority Layer Development Build the public evidence layer that AI systems draw from, including review site presence, comparison site listings, and third-party references that support retrievability and appear in the citation footprint alongside competitor-owned domains.

Phase 5: Monthly AI Visibility and Recommendation Tracking Monitor 22nd State Bank's presence, recommendation coverage, top-three rate, and rank-one rate month over month against the benchmark, and adjust the prompt, page, and citation strategy based on where movement occurs.

Why This Matters

Questions This Section Answers

• Why is absence from AI recommendations harder to fix than weak recommendation conversion?
• What does the benchmark show about the relationship between presence and valid recommendation coverage?

AI-generated recommendations are becoming a primary discovery layer for consumer banking decisions. When a buyer asks an AI system which bank to choose, the answer shapes the shortlist before the buyer ever visits a bank website. 22nd State Bank is not currently part of that answer.

Presence alone is not enough. The benchmark shows that even brands with measurable presence, like Hancock Whitney at 9.7% raw mention presence, can convert that presence into valid recommendation coverage at a much lower rate, just 1.3%. But absence is a harder problem. A brand that does not appear cannot be recommended, cannot be compared, and cannot be chosen at the recommendation stage.

The next move for 22nd State Bank is targeted correction of the prompt, page, and citation layers that determine whether AI systems surface the bank at all. The benchmark identifies the gap. The remediation work begins with building the public evidence layer that AI systems can find, retrieve, and synthesize into a recommendation.

Core Metrics

Sentiment Score

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

For 22nd State Bank, the sentiment score is 0.0000 because the bank recorded zero mentions across all 311 qualified observations. This is a null result, not a neutral framing signal.

This distinction matters. A brand with equal positive and negative mentions would also score near zero, but that would reflect balanced framing. 22nd State Bank's zero reflects absence. The bank is not being discussed, recommended, or cautioned against. It is simply not present.

Unclassified mention counts can be misleading because they treat all appearances as equivalent. A positive recommendation, a neutral reference, a cautionary mention, and a competitor-displaced mention are not the same thing. Share of voice is a diagnostic metric, not a business KPI. Counting all mentions as wins is bad measurement. Classified sentiment is required before interpreting AI visibility, and for 22nd State Bank, there is no sentiment to classify because there are no mentions.

Sentiment by Platform

Methodology

1. This report is a benchmark-based analysis of 22nd State Bank's AI visibility in the Consumer Banking category for October 2026. It is not a client implementation case study and does not reflect any CiteWorks Studio engagement with the company.
2. The reporting window is October 2026. Baseline comparison data is drawn from September 2026 where available.
3. Six AI platforms were tracked: ChatGPT, Copilot, Gemini, Perplexity, AI Overviews, and AI Mode. Keyword-level platform variants are rolled into their parent families.
4. The benchmark began with 795 prompt-surface observations in October 2026, representing 557 unique questions. Of these, 793 mentioned a tracked brand or competitor, 603 were relevant to the category, and 190 were irrelevant. The public metrics use the 311 observations that survived both qualification stages.
5. Seven consumer banks were tracked: 22nd State Bank, Bryant Bank, Century Bank, Hancock Whitney, Regions Bank, River Bank & Trust, and United Bank.
6. Three public high-intent clusters were defined: Best Consumer Banking Products & Providers (consideration stage), Consumer Banking Comparisons & Alternatives (evaluation stage), and Consumer Banking Rates, Fees & Pricing (decision stage). Only the first cluster recorded qualified observations in October 2026.
7. Stage 0 extraction retains the query, the AI surface, the answer, brand outcomes, recommendation placement, sentiment, and, where exposed, citations or attributable evidence sources.
8. A mention is counted when a tracked brand appears in an AI response, regardless of whether it is recommended. A valid recommendation is counted when a brand appears in a valid recommendation shortlist, as marked by the dataset.
9. Raw mention presence rate is the share of qualified observations in which the brand was mentioned at all. Valid recommendation coverage is the share of qualified observations in which the brand appeared in a valid recommendation shortlist. Top-three rate and rank-one rate measure placement within the recommendation shortlist.
10. Average recommended rank covers rank-eligible recommendations only. A brand with no rank-eligible recommendations receives N/A for this metric.
11. The benchmark does not measure market share, attributable sales or revenue, organic-search ranking, social mention volume, or private and sponsored channels. It does not establish causality from a metric movement alone.
12. Coverage percentages are calculated on a small qualified base. A single placement moves the percentage more for brands with few recommendations than for the leader. Month-to-month movement at low counts should be read alongside absolute counts.

See Where Your Brand Stands in AI Recommendations

The public benchmark shows category-level standings. A company-level AI visibility audit maps the specific prompts, platforms, competitors, and citation sources that determine whether your brand appears in AI-generated recommendations. If 22nd State Bank is not appearing in AI responses for consumer banking, the audit identifies where the gaps are and what the public evidence layer needs to support retrievability and recommendation.