Old National Bank AI Visibility Market Strategy Report - Consumer Banking Services

Answer Capsule

Old National Bank holds 7.09% valid recommendation coverage in the October 2026 LLM Authority Index Consumer Banking benchmark, ranking third of ten tracked banks. The bank is visible in 14.96% of qualified AI responses but converts that presence into recommendation credit less efficiently than peers with comparable coverage, and its average recommended rank of 3.38 trails First Horizon Bank's 2.13 at nearly identical coverage. The clearest win is a 7.09-point rise from the July 2026 baseline, when no tracked bank held any recommendation coverage. The clearest gap is placement quality: positive framing is not yet translating into top-three positions.

Who This Report Is For

This report is written for Old National Bank's marketing, digital, and product leadership teams, and for category analysts tracking how consumer banks are recommended inside AI-generated answers.

Report Card

Executive Summary

Old National Bank is visible but under-recommended relative to its presence. The bank appeared in 38 of 254 qualified observations in October 2026, a raw mention presence rate of 14.96%, but earned valid recommendation credit in only 18 of them, a coverage rate of 7.09%. That places it third in the category behind Regions Bank at 23.2% and Flagstar Bank at 15.8%, and ahead of First Horizon Bank at 6.3%.

The framing around the bank is strongly positive. Old National Bank recorded 21 positive mentions, 17 neutral mentions, and zero negative mentions, producing a net sentiment score of 0.5526, the third-highest in the tracked set behind Flagstar Bank at 0.8723 and First Horizon Bank at 0.5312. No tracked bank recorded a negative mention in October 2026.

The strongest cluster is Brand Recommendation, the only buyer-intent class with qualified observations this period. All 254 qualified observations landed there. The Consumer Bank Comparisons & Alternatives and Consumer Banking Fees, Rates & Pricing clusters carried no qualified data in October 2026, so the benchmark cannot yet describe how AI systems frame Old National Bank's fees, rates, or head-to-head matchups.

The strongest platform signal is Google AI Overviews, where the bank earned 9 valid recommendations out of 106 observations, an 8.49% coverage rate, and 1 rank-one placement. Perplexity produced the highest rank-one rate at 5.0%, though on a small base of 2 valid recommendations.

The clearest gap is placement quality. Old National Bank's average recommended rank of 3.38 trails First Horizon Bank's 2.13 despite nearly identical coverage rates, 7.09% versus 6.3%. Only 6 of the bank's 18 valid recommendations landed in the top three, a 2.36% top-three rate, compared with First Horizon Bank's 5.51%. The bank is being named, but it is being named later in the list.

A second gap sits in the citation layer. Old National Bank's own domain does not appear among the ten most-cited domains in the benchmark, while regions.com, santanderbank.com, and firsthorizon.com all do. The bank's public evidence layer is thinner than its competitors' at the source level.

What Old National Bank Is Winning

Questions This Section Answers

• Where did Old National Bank actually gain recommendation coverage?
• What are the bank's strongest wins on sentiment and rank-one placement?

Old National Bank's clearest win is directional momentum from the July 2026 baseline. The bank moved from 0.0% valid recommendation coverage in July to 7.09% in October, a 7.09-point rise that the benchmark classifies as significant. That places it third among the seven banks that moved off zero.

The second win is framing quality. With 21 positive mentions and zero negative mentions, the bank's net sentiment score of 0.5526 is the third-strongest in the category. AI systems are not framing Old National Bank cautiously or negatively. They are describing it favorably when they mention it.

The third win is a narrow but real rank-one pocket. The bank earned 2 rank-one placements in October 2026, a 0.79% rank-one rate that trails Regions Bank at 7.09%, First Horizon Bank and Pinnacle Financial Partners at 1.97% each, and ties the mid-tier with City National Bank. On Perplexity, the bank converted 1 of 2 valid recommendations into the top position.

These are real but modest wins. The bank does not hold a dominant position on any platform, and its coverage sits well below the category leader.

Where Old National Bank Has the Clearest AI Visibility Gaps

Questions This Section Answers

• Why does Old National Bank earn recommendation credit far less often than it appears in AI responses?
• Which platforms and placement metrics show the deepest gaps versus competitors?
• How does the bank's citation footprint compare to competitors' in AI answers?

The primary gap is recommendation conversion relative to presence. Old National Bank appears in 14.96% of qualified AI responses but earns recommendation credit in only 7.09%. Regions Bank, by contrast, appears in 54.3% of responses and earns recommendation credit in 23.2%. The conversion ratio matters: Old National Bank converts roughly 47% of its mentions into recommendations, while Regions Bank converts roughly 43% and Flagstar Bank converts roughly 85%.

The second gap is placement depth. Of the bank's 18 valid recommendations, only 6 landed in the top three. First Horizon Bank, with 16 valid recommendations at 6.3% coverage, placed 14 of them in the top three. The two banks are nearly tied on coverage, but First Horizon Bank is being named as a top option far more often. Old National Bank's average recommended rank of 3.38 is the weakest among banks with more than 10 valid recommendations.

The third gap is platform absence or thin presence. On Google AI Mode, the bank earned 5 valid recommendations out of 78 observations, a 6.41% coverage rate, but zero rank-one placements. On Copilot, the bank earned zero valid recommendations despite appearing in 3 of 18 observations. On Gemini, the bank earned 1 valid recommendation out of 16 observations. These are thin signals on small bases, but they suggest the bank's recommendation presence is concentrated rather than distributed.

The fourth gap is the source layer. Old National Bank's domain does not appear among the ten most-cited domains across 5,328 citations in the benchmark. Regions Bank, Santander Bank, and First Horizon Bank all have their own domains in the top ten. The bank's public evidence layer appears thinner at the source level, which may help explain why AI systems mention the bank without consistently recommending it.

Biggest Opportunity

The clearest opportunity is to convert Old National Bank's positive framing into top-three placement. The bank already earns favorable mentions at a rate comparable to First Horizon Bank, but it lands in the top three far less often. The gap is not visibility. It is placement.

The path runs through the Brand Recommendation cluster, the only buyer-intent class with qualified data this period. Within that cluster, the bank needs to strengthen the prompts and source pages that produce top-three placements. First Horizon Bank's pattern shows that a bank with similar coverage can convert most of its recommendations into top-three positions. Old National Bank's pattern shows that positive sentiment alone does not produce that outcome.

The secondary opportunity is the citation layer. The bank's domain is not among the most-cited sources in the category, while three competitors' domains are. Building a stronger owned answer layer and a more retrievable public evidence footprint could help AI systems find and cite the bank's own pages when forming recommendations.

Competitive Landscape

Questions This Section Answers

• Where does Old National Bank rank at the recommendation stage compared to other banks?
• Which peers convert coverage into top-three placements more efficiently than Old National Bank?

Regions Bank holds dominant recommendation-stage strength in the October 2026 Consumer Banking benchmark, with Flagstar Bank as the strongest challenger. Old National Bank sits in the mid-tier cluster, third by valid recommendation coverage but weaker on placement quality than First Horizon Bank at similar coverage.

Average recommended rank covers rank-eligible recommendations only.

Old National Bank ranks fifth by top-three rate and fifth by rank-one rate, despite ranking third by valid recommendation coverage. The table shows the bank converting coverage into placement less efficiently than First Horizon Bank and Pinnacle Financial Partners, both of which hold lower or comparable coverage but stronger top-three and rank-one rates.

AI Response Inconsistency Alerts

Questions This Section Answers

• What conflicting claims did AI platforms make about Old National Bank's construction loan terms?
• Which platforms conflicted and how severe was the inconsistency?

One critical or high-severity factual inconsistency was detected for Old National Bank across two AI platforms. The conflict concerns the bank's minimum down payment requirements for construction loans.

When asked "Which bank is best for a house construction loan?", Google AI Overviews stated that Old National Bank accepts down payments as low as 10% on loans under $1 million, citing CNBC, Bankrate, and TD Bank. Copilot stated that Old National Bank offers low down payment options of 3% or less, citing Bankrate, CNBC, and NerdWallet. A minimum down payment cannot be both 10% and 3% or less for the same lender's construction loan program. The two platforms drew on overlapping source pages but produced materially different claims about the bank's eligibility terms.

This is a high-severity eligibility conflict with a confidence score of 0.9. The conflicting claims appeared on Google AI Overviews and Copilot, two of the six tracked platforms. The question that triggered the conflict was a high-intent construction loan prompt, a decision-stage query where accurate eligibility information matters most.

Prompt Evidence

Questions This Section Answers

• Which specific prompts drove Old National Bank's recommendation credit on AI platforms?
• Where did Old National Bank earn or fail to earn a rank-one placement, and on which platforms?

Google AI Overviews / Brand Recommendation Prompt: "Which banks deposit checks immediately?" Result: Old National Bank appeared in the response and earned recommendation credit, contributing to its 8.49% coverage rate on AI Overviews.

Perplexity / Brand Recommendation Prompt: "Who is offering the lowest mortgage rates right now?" Result: Old National Bank earned a rank-one placement on Perplexity, one of only two rank-one positions the bank recorded across all platforms.

Copilot / Brand Recommendation Prompt: "Which bank is best for a house construction loan?" Result: Copilot stated that Old National Bank offers low down payment options of 3% or less, conflicting with Google AI Overviews' claim of 10% minimum down payments on the same question.

Google AI Mode / Brand Recommendation Prompt: "What is the best bank for senior citizens?" Result: Old National Bank appeared in the response but did not earn a rank-one placement, consistent with its pattern of presence without top-position conversion on AI Mode.

What CiteWorks Studio Would Do Next

Phase 1: AI Market Discovery Audit Map the specific prompts, platforms, and source pages where Old National Bank earns recommendation credit versus where it appears without placement, starting with the construction loan and mortgage rate prompts surfaced in this benchmark.

Phase 2: Recommendation Readiness Plan Prioritize the Brand Recommendation cluster and identify which product lines, geographies, and use cases produce the bank's top-three placements, then build a plan to extend that pattern.

Phase 3: Owned Answer Layer Buildout Strengthen the bank's own pages on the topics where AI systems are forming recommendations, particularly construction loans, mortgage rates, and senior banking, so the bank's own content is retrievable and citable.

Phase 4: Citation / Authority Layer Development Address the gap between Old National Bank's absence from the top-cited domains and the presence of regions.com, santanderbank.com, and firsthorizon.com, focusing on the comparison publishers and reference sources AI systems already cite.

Phase 5: Monthly AI Visibility and Recommendation Tracking Track coverage, top-three rate, rank-one rate, and sentiment month over month to measure whether placement quality improves relative to First Horizon Bank and the mid-tier cluster.

Why This Matters

Questions This Section Answers

• Why isn't positive framing alone enough to make Old National Bank a top recommendation in AI answers?

AI presence alone is not enough. Old National Bank appears in 14.96% of qualified AI responses, but it earns recommendation credit in only 7.09% and top-three placement in only 2.36%. A buyer asking an AI system for the best bank for a construction loan or the lowest mortgage rates may see Old National Bank mentioned without seeing it recommended as a top option.

The next move is targeted correction of the prompt, page, and citation layers. The bank's positive framing is an asset. The placement gap is the problem. Closing it requires strengthening the specific prompts and source pages that produce top-three placements, and building a citation footprint that AI systems can retrieve when forming recommendations.

Core Metrics

Sentiment Score

Questions This Section Answers

• Why is Old National Bank's sentiment score a better diagnostic than raw mention counts?

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

For Old National Bank in October 2026: (21 × 1 + 17 × 0 + 0 × -1) / 38 = 0.5526.

This matters because unclassified mention counts are misleading. A bank that appears in 38 AI responses with 21 positive mentions and zero negative mentions is in a different position than a bank with 38 mentions split evenly between positive and negative framing. Share of voice is a diagnostic metric, not a business KPI. A positive recommendation, a neutral reference, a cautionary mention, and a competitor-displaced mention are not equal. Counting all mentions as wins is bad measurement. Classified sentiment is required before interpreting AI visibility.

Old National Bank's 0.55 sentiment score is the third-highest in the category, behind Flagstar Bank at 0.87 and First Horizon Bank at 0.53. The bank's framing is strong. The gap is placement, not perception.

Sentiment by Platform

Methodology

1. This report is benchmark-based analysis of the October 2026 LLM Authority Index Consumer Banking measurement, interpreted by CiteWorks Studio. It is not a client result and does not imply that CiteWorks Studio caused any benchmark outcome.
2. The reporting month is October 2026. The benchmark baseline is July 2026, with intermediate measurements in August 2026 and September 2026 referenced in prose only.
3. Six AI and search surface families were tracked: ChatGPT, Copilot, Gemini, Perplexity, AI Overviews, and AI Mode. All six recorded at least one qualified observation in October 2026.
4. The October 2026 measurement began with 800 prompt-surface observations and 574 unique questions. After qualification, 254 observations formed the public denominator for all recommendation metrics.
5. Ten consumer banks were tracked: Regions Bank, Flagstar Bank, Old National Bank, First Horizon Bank, City National Bank, Pinnacle Financial Partners, East West Bank, Santander Bank, Webster Bank, and Zions Bank.
6. Three public high-intent clusters were defined: Brand Recommendation (consideration stage), Consumer Bank Comparisons & Alternatives (evaluation stage), and Consumer Banking Fees, Rates & Pricing (decision stage). Only Brand Recommendation carried qualified observations in October 2026.
7. Stage 0 extraction retained the query, AI surface, answer, brand outcome, recommendation placement, sentiment, and, where exposed, citations or attributable evidence sources for each prompt-level observation.
8. A mention is counted when a tracked brand appears in a qualified AI response, regardless of context or position. A valid recommendation is counted when a brand appears in a recommendation shortlist, as marked by the dataset. Mentions and recommendations are separate signals and are not collapsed.
9. Top-three rate measures the share of qualified observations where a brand appears among the top three recommended options. Rank-one rate measures the share where the brand is the first recommendation. Average recommended rank covers rank-eligible recommendations only.
10. The benchmark does not measure market share, attributable sales, every possible AI response, organic-search ranking, social mention volume, private or sponsored channels, or causality from a metric movement alone.
11. Unique prompt count is available for October 2026 at 574. The public benchmark does not expose the full prompt-level dataset.
12. The July 2026 baseline of 0.0% coverage across all ten banks reflects a month with no recommendation-shaped AI responses, not a failure of any tracked brand.

See How AI Is Recommending Your Brand

The public benchmark shows where Old National Bank stands in AI-generated recommendations across consumer banking. A company-level AI visibility audit maps the specific prompts, platforms, competitors, and source pages behind those numbers into a prioritized strategy. If you want to see where your brand is being recommended, where it is being displaced, and which prompts are driving the difference, start with an AI visibility audit built on the same measurement framework behind this report.