American Express Appears in 42.2% of AI Answers. Here's Our Five-Step Plan to Improve Its AI Visibility.

Mark HuntleyBy Mark HuntleyFounder and CEO
10 minutes read

Key Takeaways

  • American Express is mentioned often in benchmark answers, but it is recommended far less frequently.
  • Most recorded claim conflicts are medium or low severity and should be verified against primary sources first.
  • Priority work includes reviewing key company pages, disputed third-party citations, and the buyer questions that recur in rankings and comparisons.
  • The 90-day plan starts with a baseline scorecard, then moves to verified page briefs, publisher requests, and buyer-content briefs.

Answer Capsule

In November 2026, American Express appeared in 54 of 128 AI answers included in the Certificates of Deposits benchmark (42.19%). It received 11 recommendations (8.59%), with 3 Top 3 placements and 3 first-place recommendations. A mention means the company appeared in an answer; a recommendation means the answer affirmatively endorsed it.

Our plan compares the company with its competitors, checks conflicting AI claims against reliable sources, and identifies website pages, third-party sources and buyer questions to address. The historical results show how the figures changed across available months. Different monthly samples limit those comparisons, and conflicting AI claims require verification before corrections are proposed.

Current AI visibility

Questions This Section Answers

  • How do American Express mentions, recommendations and placements compare with its Certificates of Deposits peers?

American Express recorded 54 mentions (42.19%) and 11 recommendations (8.59%) across 128 answers included in the benchmark. Top 3 placements numbered 3; first-place recommendations numbered 3. Framing among its mentions was 17 positive, 37 neutral and 0 negative.

Rank

Company

Mentions

Mention rate

Recommendations

Recommendation rate

Top 3

Top 3 rate

#1

#1 rate

1

American Express

54

42.19%

11

8.59%

3

2.34%

3

2.34%

1

Morgan Stanley

77

60.16%

11

8.59%

3

2.34%

0

0.00%

3

Goldman Sachs Group

73

57.03%

7

5.47%

0

0.00%

0

0.00%

4

Bank of America Corp.

75

58.59%

6

4.69%

4

3.13%

0

0.00%

4

Bread Savings

33

25.78%

6

4.69%

1

0.78%

0

0.00%

4

JPMorgan Chase & Co.

67

52.34%

6

4.69%

5

3.91%

4

3.13%

7

Citigroup Inc.

60

46.88%

5

3.91%

3

2.34%

0

0.00%

7

Wells Fargo & Co.

64

50.00%

5

3.91%

0

0.00%

0

0.00%

9

U.S. Bancorp

46

35.94%

4

3.13%

0

0.00%

0

0.00%

10

Capital One Platinum Mastercard

2

1.56%

0

0.00%

0

0.00%

0

0.00%

American Express appears in a large share of the answers reviewed, but that reach does not translate into endorsement. It is named far more often than it is recommended, and its positive framing is modest relative to its neutral coverage. The practical implication is that the brand is present in the conversation without being the answer buyers are pointed toward.

Placement is thin as well. Only a small number of answers put the company in a leading position, and those sit inside the recommendation group rather than adding to it. The agency would treat presence and endorsement as separate goals, starting with a scorecard that tracks each on its own terms.

Because the monthly record is a single selected sample, these figures describe this period only. They are a starting point for prioritization, not a trend.

What changed since last month

Questions This Section Answers

  • How did American Express counts and coverage differ across the available monthly snapshots?

Metric

Nov 2026

Mentions

54 / 128 (42.19%)

Recommendations

11 / 128 (8.59%)

Top 3 recommendations

3 / 128 (2.34%)

#1 recommendations

3 / 128 (2.34%)

Only months with verified results are shown. Monthly sample coverage is listed in Methodology.

There are insufficient usable monthly observations for a prior-period comparison.

There is no usable prior period to compare against, so this month stands alone as a baseline. The observed totals should be read as one selected sample rather than as movement in either direction. A different answer total in another month would not by itself explain any change in counts or rates.

The honest conclusion is that the company now has a reference point, not a trajectory. The agency would use this month to set the scorecard's starting values and to define what a meaningful shift would look like before claiming one.

AI recommendation trend

Observed recommendation rate (%), Nov 2026

  • American Expressno change
    Nov 20268.59%

Separate selected monthly samples. Missing points are unavailable, not zero. Provider/prompt equivalence is unverified; rate movement does not demonstrate causation.

/ Take the next step

Want to Understand Your AI Citation Footprint?

We start every engagement with a full audit of how AI systems reference your brand today.

Measurable, Repeatable Programme

Build a durable foundation of credible citations that compounds over time and continues to influence AI answers as new queries emerge

Citation Architecture Review

Identify which high-authority community sources are and aren't working in your favour across AI platforms.

AI Visibility Audit

Understand exactly how LLMs are referencing your brand today and which sources are shaping those answers.

Step 1. Measure

Questions This Section Answers

  • What should the agency measure and verify first for American Express?

AI answers contained 5 conflicting claims about American Express: 0 critical, 0 high, 3 medium, 2 low. Of these, 0 were rated critical or high priority; the critical claims are included in that total. These ratings help prioritize fact-checking. They do not establish that a company or publisher made an error.

The conflicting claims recorded about American Express give the client a concrete place to start. Most sit at medium severity, with a couple rated low, and none were flagged as critical or high. That mix suggests a fact-checking exercise rather than an emergency, and the ratings are a way to order the work, not a finding that anyone made a mistake.

The agency would build a visibility comparison alongside a claim list, so the client can see which topics recur and which deserve verification first. Each claim would be checked against current primary sources before any action is proposed.

Step 2. Make the website consistent

Questions This Section Answers

  • Which recorded claim differences about American Express should be verified before proposed page edits?

Review topic

Severity

Recorded AI claim A

Recorded AI claim B

total consolidated assets

medium

$308 billion

~$155 billion

inclusion in top 15 banks ranking

medium

American Express is in the top 15 (rank 15 by assets)

American Express is not in the top 15

APY

low

3.10% to 3.65% APY

~3.5-4.0% APY

CLEAR Plus credit amount

low

$219 CLEAR+ credit

$209 CLEAR Plus credit

Both columns quote AI answers, not verified company facts. Resolve the entity, date and source before deciding whether a correction is warranted; different dates can explain an apparent conflict.

The disputed topics cluster around balance-sheet size, ranking placement, savings rates and a card benefit amount. Each pair of claims could reflect different dates, different product versions or simple error, and the report cannot say which. Resolving the entity and date comes before deciding whether a correction is warranted.

The agency would prepare page briefs only after verification, using approved facts and clear dating. Where the evidence supports the company's own position, a brief would say so; where it does not, the topic would be marked for no change rather than forced into a correction queue.

Step 3. Align the external narrative

Questions This Section Answers

  • Which cited third-party pages are relevant to fact review for American Express?

Cited page selected for review

Citation appearances in all captured answers

Answers mentioning the company

Conflicting claims linked to this page

https://en.wikipedia.org/wiki/List_of_largest_banks_in_the_United_States

120

8

2

https://federalreserve.gov/releases/lbr/current/

90

7

2

https://bankrate.com/banking/savings/best-high-yield-interests-savings-accounts/

15

5

1

https://cnbc.com/select/best-high-yield-savings-accounts/

7

5

1

https://wsj.com/buyside/personal-finance/banking/best-high-yield-savings-account

6

4

1

https://nerdwallet.com/banking/best/high-yield-online-savings-accounts

7

3

1

These pages were selected because they relate to the company in the captured answers. Their current contents still need checking before any correction request. A page linked to a conflicting claim is not necessarily its cause, and the same answer may contribute to more than one row.

The cited pages selected for review include a widely referenced bank ranking list and a Federal Reserve release, both linked to more than one disputed claim. Savings-rate comparison pages from financial publishers also appear. These are candidates for review, not established sources of error, and a citation does not show that a page influenced an answer.

The agency would check each page's current contents against primary sources, then decide whether an evidence-backed request to the publisher is justified. Requests would be framed as corrections only where the page is demonstrably out of date or wrong.

Step 4. Optimize priority company pages

Questions This Section Answers

  • Which observed American Express website pages should receive review briefs?

Company website page

Citation appearances in all captured answers

Benchmark answers citing the page

Recommending answers citing the page

https://apply.americanexpress.com/amex-morgan-stanley-credit-cards/?page_url=38

10

2

1

https://global.americanexpress.com/card-benefits/view-all/morgan-stanley-platinum-card

8

2

1

https://americanexpress.com/en-us/credit-cards/credit-intel/credit-card-requirements/

6

3

0

https://apply.americanexpress.com/amex-morgan-stanley%20-credit-cards/?page_url=38

5

1

0

https://americanexpress.com/en-us/benefits/

4

1

1

https://americanexpress.com/en-us/credit-cards/credit-intel/how-to-apply-for-credit-card/

4

2

0

https://americanexpress.com/us/credit-cards/card/platinum/

4

0

0

https://global.americanexpress.com/card-benefits/detail/oura-credit/platinum

4

0

0

The company's website was cited 230 times across 156 distinct page addresses; the table shows 8 pages selected for review. Zeros in the answer columns mean no matching answers were recorded. A cited page is not automatically a page that accompanied a recommendation.

The company's own pages were cited widely, but the selected pages show a split between card-application and benefits pages that appear in recommending answers and informational pages that do not. That pattern is worth examining, though a cited page is not automatically one that accompanied a recommendation.

The agency would write page briefs for the priority pages using approved facts and the questions buyers actually ask. Briefs would note where existing coverage already answers a question, so new content is proposed only where a genuine gap exists.

Step 5. Publish around buyer questions

Questions This Section Answers

  • Which observed buyer questions support content briefs for American Express?

Question observed in the research

Benchmark answers to this question

Company mentions

Company recommendations

What are the top 100 finance companies in the USA?

4

4

1

What are the top 50 banks in the USA?

4

3

1

What are the top 20 banks in the USA?

3

2

1

What is the most exclusive credit card for Morgan Stanley?

2

2

1

Which are the top 5 credit cards?

2

2

1

Which banks have high yield savings accounts?

2

2

1

What's so special about having an American Express card?

1

1

1

What's so special about the American Express card?

1

1

1

These are recorded answer observations, not search volume, verified demand, or proof of missing website content.

The recorded questions lean toward rankings and comparisons: top banks, top finance companies, top credit cards and high-yield savings. A smaller cluster asks what makes an American Express card distinctive, and those questions were the ones where the company was both mentioned and recommended. These are answer observations, not search volume or proof of demand.

The agency would check existing coverage against each question before proposing anything new, then draft buyer-question content where a gap is confirmed. Ranking-style questions would be handled carefully, since they depend on verified facts rather than promotional framing.

Our 90-day action plan

Questions This Section Answers

  • How should the agency sequence the proposed American Express work and judge completion?

Period

Deliverable

Completion criterion

Days 1–30

Baseline scorecard, scoped fact-review register and page/source shortlist

Each record has an owner, evidence requirement and review status

Days 31–60

Approved page briefs and justified publisher requests

Separate confirmed edits, no-change decisions, requests and responses

Days 61–90

Buyer-content briefs and a fresh scorecard

Record work completed separately from measured answer outcomes and comparability limits

The first month establishes the baseline scorecard, a scoped fact-review register and a shortlist of pages and sources, each with an owner and an evidence requirement. The second month turns verified findings into approved page briefs and justified publisher requests, with confirmed edits and no-change decisions recorded separately.

The third month adds buyer-content briefs and a fresh scorecard. Completed work would be reported separately from measured answer outcomes, and no gain would be promised in advance.

Methodology

Questions This Section Answers

  • How are American Express answer metrics and citation populations defined?

This report covers 2026-11. The exact dates when the original AI answers were collected have not been verified. The visibility rates use 128 answers that met the benchmark's inclusion rules. A company is recommended only when the answer both endorses it and uses positive wording. Top 3 and first-place counts are part of those recommendations, not extra recommendations.

An answer can mention or recommend several companies, so company counts cannot be added to find the number of distinct answers. The net sentiment score is positive minus negative mentions, divided by all company mentions; it describes answer wording, not customer satisfaction.

The wider citation analysis covers 800 captured answers and 4018 appearances of cited URLs. Citation appearances across all captured answers differ from the number of benchmark answers citing a page and the number that also recommend a company. The current contents of cited pages and disputed company claims still need independent verification.

Monthly sample coverage:

Reporting month

Answers included

AI services covered

Services

Nov 2026

128

6

ChatGPT, Copilot, Gemini, Google AI Mode, Google AI Overviews, Perplexity

July 2026 measured a different set of companies, so it is excluded from this comparison. August 2026 measured a different set of companies, so it is excluded from this comparison. September 2026 measured a different set of companies, so it is excluded from this comparison. October 2026 measured a different set of companies, so it is excluded from this comparison.

The answer samples differ between months. These comparisons show how the recorded results changed; they do not establish why they changed.

The answers were collected at dates that have not been verified, and the rates rest on the answers that met the benchmark's inclusion rules. A company counts as recommended only when an answer endorses it with positive wording, and leading placements sit inside that group rather than adding to it.

Answers can mention several companies, so counts cannot be summed into distinct answers. Sentiment describes answer wording, not customer satisfaction. Cited pages and disputed claims still need independent verification, and the monthly samples differ, so comparisons show what changed, not why.

Our first three priorities

The first priority is verification. The disputed claims about assets, ranking and rates should be checked against current primary sources, with the entity and date settled first, before any correction or publisher request is considered.

The second is the owned pages. Briefs for the priority card and benefits pages should use approved facts and the buyer questions recorded here, with existing coverage checked before new content is proposed.

The third is the scorecard. Setting a clear baseline now, with presence and endorsement tracked separately, gives the client a defensible way to judge later work without overstating what this month shows.

/ Learn More

Understanding AI search visibility.

AI search experiences create answers by pulling information from many places online and summarizing it into a single response.

What Is AI Citation Intelligence?
AI citation intelligence is the process of measuring where AI platforms source their information and how frequently a brand is mentioned or referenced in AI-generated responses. Because LLMs synthesize across multiple sources, the sites and brands that appear repeatedly tend to influence how a topic or company is framed. This practice focuses on identifying which sources shape AI outputs and tracking brand visibility across different AI systems.
What Is Citation Architecture?
Citation architecture describes the set of sources that consistently inform how AI systems talk about a brand, product, or topic. LLMs draw from websites, articles, forums, and public discussion, and the sources they rely on most often become the backbone of their answers. Building strong citation architecture means ensuring that accurate, credible, high authority sources are the ones most likely to shape the way AI tools summarize and recommend a brand.
What Is Generative Engine Optimization?
Generative engine optimization (GEO) is the practice of improving the chances that AI systems use and cite your brand or content when generating answers. While traditional SEO is centered on ranking pages in search results, GEO focuses on how LLMs retrieve, interpret, and combine information when responding to a question. The objective is to strengthen the content and sources AI systems rely on, so your brand is treated as a trusted reference in AI responses.
What Is AI Share of Voice?
AI share of voice tracks how often a brand appears in AI-generated answers compared with competitors in the same category. It reflects visibility across AI platforms such as ChatGPT, Gemini, Claude, and Perplexity. Monitoring AI share of voice helps organizations see whether AI systems consistently include and recommend their brand for key queries or whether competitor brands are showing up more often.

About The Author

Mark Huntley

Mark Huntley

Founder and CEO

Mark Huntley, J.D. is founder of CiteWorks Studio, a strategic advisory focused on visibility, authority, and recommendation presence in AI-shaped search environments. His work centers on embedding-level GEO, vector optimization, and cosine gap engineering — helping brands align their digital presence with the retrieval systems that increasingly shape discovery, interpretation, and choice.

Archived editions

Browse earlier published research snapshots for this article. The latest edition remains at the current URL.

VIEW ALL CASE STUDIESREQUEST AN AI VISIBILITY AUDIT