Old National Bank AI Market Strategy Report - Consumer Banking

Answer Capsule

Old National Bank holds steady mid-tier recommendation coverage in consumer banking AI discovery at 5.1% in September 2026, tied with First Horizon Bank for third place in the tracked field. The bank appears in 11.2% of qualified AI responses but converts only a portion of that visibility into recommendation credit, leaving room to close the gap with the category leaders. Its clearest strength is a positive framing profile with zero negative mentions across 31 total mentions. Its clearest weakness is an average recommended rank of 4.33, the weakest placement among banks with meaningful recommendation coverage. The clearest opportunity is improving recommendation position quality to convert its existing shortlist presence into higher-ranked placements.

Who This Report Is For

This report is for consumer banking marketing, digital strategy, and brand leadership teams tracking how AI-generated recommendations shape bank selection and competitive positioning.

Report Card

Executive Summary

Old National Bank holds a stable position in the consumer banking AI recommendation landscape with 5.1% valid recommendation coverage in September 2026, up from zero coverage in July 2026. The bank earned 14 valid recommendation mentions out of 277 qualified observations, placing it in a tie for third with First Horizon Bank behind category leader Regions Bank at 17.3% and Flagstar Bank at 12.6%. Its two-month rise of 5.1 percentage points from the July baseline is classified as a significant gain.

The bank's raw mention presence reached 11.2% in September 2026, with 31 total mentions split between 15 positive and 16 neutral observations. Old National Bank recorded zero negative mentions for the month, contributing to a net sentiment score of 0.48. The strongest platform signal came from Google AI Overviews, where the bank achieved 6 valid recommendations and a 6.06% coverage rate, its best platform-level performance. The clearest platform gap appeared on ChatGPT, where the bank earned only 1 valid recommendation despite 2 mentions.

The strongest cluster for Old National Bank is the Brand Recommendation cluster, which accounts for all 277 qualified observations in the September benchmark. The weakest area is recommendation placement quality: the bank's average recommended rank of 4.33 trails every other tracked bank with meaningful coverage, including First Horizon Bank at 1.69 and Pinnacle Financial Partners at 2.10. Old National Bank appears in recommendation shortlists but is consistently placed lower than its peers.

What Old National Bank Is Winning

Old National Bank's clearest evidence-backed win is its positive framing profile. The bank recorded 15 positive mentions and zero negative mentions across 31 total mentions in September 2026, producing a net sentiment score of 0.48. This places it above the category median and indicates that AI systems frame the bank constructively when it appears.

The bank also holds a narrow but meaningful recommendation pocket on Google AI Overviews. With 6 valid recommendations out of 99 qualified observations on that platform, Old National Bank achieved its strongest platform-level coverage at 6.06%. This suggests the bank has some source footprint that AI Overviews retrieves and includes in recommendation shortlists.

Old National Bank's two-month rise from zero coverage in July 2026 to 5.1% in September 2026 is itself a notable achievement. The bank moved from complete absence in AI recommendation shortlists to a mid-tier position, a shift the benchmark classifies as significant.

Where Old National Bank Has the Clearest AI Visibility Gaps

Questions This Section Answers

• Why does Old National Bank's average recommended rank of 4.33 trail banks with similar coverage?
• Which platform shows the clearest gap between Old National Bank's mentions and its valid recommendations?

Old National Bank's most pressing gap is recommendation placement quality. The bank's average recommended rank of 4.33 is the weakest among all tracked banks with meaningful recommendation coverage. First Horizon Bank, which shares the same 5.1% coverage rate, achieves an average rank of 1.69. Flagstar Bank, at 12.6% coverage, holds an average rank of 3.30. Old National Bank is present in shortlists but is consistently placed below its competitors.

The bank's top-three rate of 1.4% highlights the placement problem. Only 4 of its 14 valid recommendations landed in the top three positions. By comparison, First Horizon Bank placed 13 of its 14 valid recommendations in the top three. Old National Bank earns recommendation credit but rarely appears as a leading option.

ChatGPT represents a platform-specific gap. The bank appeared in 2 mentions on ChatGPT but earned only 1 valid recommendation with no top-three placement. On Copilot, Old National Bank earned 2 valid recommendations including 1 rank-one placement, showing it can win first position on some surfaces. The inconsistency across platforms suggests the bank's recommendation strength is not evenly distributed across the AI surface landscape.

Biggest Opportunity

Questions This Section Answers

• What should Old National Bank do to convert its existing recommendation presence into higher-ranked placements?

Old National Bank's clearest opportunity is improving recommendation position quality within its existing shortlist presence. The bank already earns 14 valid recommendations per month, but its average rank of 4.33 means most of those placements fall outside the top three where buyer attention concentrates. First Horizon Bank demonstrates that a bank with identical coverage can achieve an average rank of 1.69, which suggests the gap is not a function of brand size but of the evidence layer supporting recommendation placement.

The path forward is to identify which prompts produce Old National Bank's rank-one and top-three placements, then strengthen the public evidence layer around those use cases. The bank's rank-one placements on Copilot and Perplexity show that first-position wins are achievable. Expanding the source footprint that supports those high placements could shift more of the bank's 14 valid recommendations into the top three.

Competitive Landscape

Questions This Section Answers

• How does Old National Bank's recommendation placement quality compare with other tracked banks?

Regions Bank holds dominant recommendation-stage strength in consumer banking with 17.3% valid recommendation coverage, while Flagstar Bank holds the second position at 12.6%. Old National Bank sits in a mid-tier cluster with First Horizon Bank at 5.1% coverage, behind the two leaders but ahead of the remaining tracked banks.

Average recommended rank covers rank-eligible recommendations only.

The table shows Old National Bank tied for third in coverage but holding the weakest average recommended rank among banks with meaningful recommendation activity. Its top-three rate of 1.44% is lower than Pinnacle Financial Partners at 3.25%, a bank with less overall coverage. The bank's sentiment score of 0.48 is healthy, but positive framing is not translating into prominent recommendation placement.

Prompt Evidence

Google AI Overviews / Brand Recommendation Prompt: "best consumer banking options" Result: Old National Bank appeared in a recommendation shortlist with 6 valid recommendations on this platform, its strongest surface-level performance.

Copilot / Brand Recommendation Prompt: "top bank recommendations" Result: Old National Bank earned 2 valid recommendations including 1 rank-one placement, demonstrating first-position wins are possible on this surface.

ChatGPT / Brand Recommendation Prompt: "open bank account online" Result: Old National Bank appeared in 2 mentions but earned only 1 valid recommendation with no top-three placement, showing a presence-to-recommendation conversion gap.

What CiteWorks Studio Would Do Next

Phase 1: AI Market Discovery Audit Map the specific prompts where Old National Bank earns recommendation credit and identify which competitors displace it from top-three positions.

Phase 2: Recommendation Readiness Plan Prioritize the use cases where the bank's rank-one placements on Copilot and Perplexity can be expanded into broader prompt coverage.

Phase 3: Owned Answer Layer Buildout Strengthen owned content around the bank's product strengths so AI systems have clearer material to cite when forming recommendation shortlists.

Phase 4: Citation / Authority Layer Development Build the external source footprint that supports higher recommendation placement, focusing on the evidence layer that moves the bank from position four into the top three.

Phase 5: Monthly AI Visibility and Recommendation Tracking Track whether placement improvements follow the evidence layer changes and adjust the strategy based on monthly recommendation rank movement.

Why This Matters

AI-generated recommendations are becoming a primary input into consumer banking decisions. Old National Bank has established that it belongs in the conversation, appearing in 11.2% of qualified AI responses and earning recommendation credit in 5.1%. But presence alone is not enough. The bank's average recommended rank of 4.33 means that when AI systems do recommend it, the bank typically appears below the options buyers see first.

The next move is targeted correction of the prompt, page, and citation layers that determine recommendation position. Old National Bank does not need to build visibility from scratch. It needs to convert its existing visibility into higher-ranked placements by strengthening the evidence that moves a bank from a mid-list mention to a top-three recommendation.

Core Metrics

Sentiment Score

Questions This Section Answers

• How is Old National Bank's net sentiment score calculated, and why does classified sentiment matter?

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

For Old National Bank, the calculation is (15 × 1 + 16 × 0 + 0 × -1) / 31, producing a net sentiment score of 0.48.

This score matters because unclassified mention counts are misleading. A bank with high raw presence but mostly neutral or negative framing is not in the same competitive position as a bank with positive recommendation-driven mentions. 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, because the framing of a mention determines whether it helps or hurts the bank's position in the buyer's consideration set.

Sentiment by Platform

Methodology

1. This report analyzes Old National Bank's AI market positioning within the Consumer Banking vertical using the LLM Authority Index AI Market Discovery benchmark for September 2026.
2. The reporting window is September 2026, with July 2026 as the baseline measurement and August 2026 referenced for month-over-month context.
3. Five AI and search surface families produced qualified observations: ChatGPT, Copilot, Perplexity, Google AI Mode, and Google AI Overviews. Gemini produced no qualified observations in September 2026.
4. The analysis is based on 277 qualified benchmark observations, drawn from 700 source prompt-surface observations and 538 unique questions.
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 cluster. No qualified observations were recorded in the Pricing & Value or Multi-Brand Comparison clusters in September 2026.
7. Stage 0 extraction captured prompt-level observations including the query, AI surface, answer type, brand outcome, recommendation placement, sentiment, and citations where exposed.
8. A mention is defined as any qualified observation where the brand appears, regardless of context or position.
9. A valid recommendation is defined as a qualified observation where the brand appears in a recommendation shortlist with rank-eligible placement.
10. Limitations: this public benchmark does not measure market share, attributable sales, every possible AI response, organic-search ranking, or causality from metric movement alone. The benchmark records what AI systems surfaced; it does not explain why those systems produced those outputs.
11. The August intermediate month is referenced in prose where it clarifies the September result; comparison tables use July and September only.
12. Small counts matter in this vertical. Old National Bank's 14 valid recommendations are meaningful, but platform-level breakdowns with fewer than 5 mentions should be interpreted with caution.

See How AI Is Recommending Your Brand

The public benchmark shows where Old National Bank stands in AI-generated consumer banking recommendations. A company-level AI visibility audit can map the specific prompts, competitor displacement patterns, and evidence sources behind the bank's 5.1% recommendation coverage, showing exactly where the bank wins shortlist inclusion and why its average rank trails peers with similar coverage.