New York Life Appears in 86.4% of AI Answers. Here's Our Five-Step Plan to Improve Its AI Visibility.

Mark HuntleyBy Mark HuntleyFounder and CEO
9 minutes read

Key Takeaways

  • New York Life appears in most benchmark answers and is recommended more often than its peers, with especially strong first-place placement.
  • The main priority is protecting recommendation strength, not increasing raw mention volume.
  • No conflicting claims were recorded, so the first step is to verify facts against primary sources before proposing page changes.
  • The plan focuses on cited third-party pages, priority company pages, and buyer questions about the best annuity provider.

Answer Capsule

In November 2026, New York Life appeared in 146 of 169 AI answers included in the Annuities benchmark (86.39%). It received 102 recommendations (60.36%), with 75 Top 3 placements and 62 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 New York Life mentions, recommendations and placements compare with its Annuities peers?

New York Life recorded 146 mentions (86.39%) and 102 recommendations (60.36%) across 169 answers included in the benchmark. Top 3 placements numbered 75; first-place recommendations numbered 62. Framing among its mentions was 116 positive, 30 neutral and 0 negative.

Rank

Company

Mentions

Mention rate

Recommendations

Recommendation rate

Top 3

Top 3 rate

#1

#1 rate

1

New York Life

146

86.39%

102

60.36%

75

44.38%

62

36.69%

2

MassMutual

154

91.12%

101

59.76%

43

25.44%

14

8.28%

3

Allianz Life

122

72.19%

94

55.62%

68

40.24%

12

7.10%

4

Nationwide

135

79.88%

89

52.66%

27

15.98%

4

2.37%

5

Athene

125

73.96%

76

44.97%

33

19.53%

15

8.88%

6

Lincoln Financial

62

36.69%

40

23.67%

3

1.78%

1

0.59%

7

Corebridge Financial

36

21.30%

20

11.83%

10

5.92%

2

1.18%

8

Pacific Life

27

15.98%

9

5.33%

4

2.37%

1

0.59%

9

Fidelity

40

23.67%

4

2.37%

0

0.00%

0

0.00%

10

Brighthouse Financial

2

1.18%

1

0.59%

0

0.00%

0

0.00%

New York Life is the most recommended company in this benchmark and holds the strongest first-place position among the listed insurers. Its mentions are almost entirely positive, with no negative framing recorded. That combination suggests the company is not just present in answers but is often the answer buyers are steered toward.

The gap worth noting is between being mentioned and being recommended. MassMutual appears in more answers but is recommended less often and placed first far less often. For the agency, this means the priority is protecting and extending recommendation strength rather than chasing raw mention volume.

What changed since last month

Questions This Section Answers

  • How did New York Life counts and coverage differ across the available monthly snapshots?

Metric

Jul 2026

Sep 2026

Oct 2026

Nov 2026

Mentions

206 / 246 (83.74%)

184 / 227 (81.06%)

180 / 217 (82.95%)

146 / 169 (86.39%)

Recommendations

176 / 246 (71.54%)

142 / 227 (62.56%)

167 / 217 (76.96%)

102 / 169 (60.36%)

Top 3 recommendations

122 / 246 (49.59%)

107 / 227 (47.14%)

148 / 217 (68.20%)

75 / 169 (44.38%)

#1 recommendations

47 / 246 (19.11%)

62 / 227 (27.31%)

98 / 217 (45.16%)

62 / 169 (36.69%)

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

From October 2026 to November 2026, New York Life's mention count fell from 180 to 146, while its recommendation count fell from 167 to 102. The recommendation rate was 76.96% and 60.36% respectively.

The November sample shows lower mention and recommendation counts than October, and the recommendation rate also eased. The first-place rate fell back from its October level while remaining well above the earlier months. These are separate monthly samples, so the movement describes what was recorded, not why it happened.

The practical reading is that October's peak should not be treated as a stable baseline. Proposed work should track recommendation and first-place rates month by month against a documented scorecard, so the client can see whether later changes reflect real movement or sample variation.

AI recommendation trend

Observed recommendation rate (%), Jul 2026 to Nov 2026

0%20%40%60%80%Jul 2026Sep 2026Oct 2026Nov 202671.54%Jul 2026: 71.54%62.56%Sep 2026: 62.56%76.96%Oct 2026: 76.96%60.36%Nov 2026: 60.36%
  • New York Life60.36%

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

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Step 1. Measure

Questions This Section Answers

  • What should the agency measure and verify first for New York Life?

AI answers contained 0 conflicting claims about New York Life: 0 critical, 0 high, 0 medium, 0 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.

No conflicting claims about New York Life were recorded in the captured answers, so there is no immediate fact-check queue to work through. That is a clean starting position, but it is also a thin one: the absence of disputes does not confirm that every statement in circulation is accurate.

The agency would build a baseline scorecard and a scoped fact-review register, listing the claims worth checking against primary sources and assigning each an owner and evidence requirement. This gives the client a defensible record before any correction work begins.

Step 2. Make the website consistent

Questions This Section Answers

  • Which recorded claim differences about New York Life should be verified before proposed page edits?

No results are available for this part of the analysis.

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.

This part of the analysis returned no results, so there are no disputed topics to brief against yet. Rather than guess at page problems, the agency would first confirm which entity, date and source each potential claim refers to, since different dates can make two statements look contradictory when they are not.

Only after that verification would page briefs be drafted, and only for claims the client can support with current primary-source evidence. Ambiguous matches would stay out of the correction queue.

Step 3. Align the external narrative

Questions This Section Answers

  • Which cited third-party pages are relevant to fact review for New York Life?

Cited page selected for review

Citation appearances in all captured answers

Answers mentioning the company

Conflicting claims linked to this page

https://guardianlife.com/annuities/companies

8

5

0

https://nerdwallet.com/insurance/life/best-life-insurance-companies

166

3

0

https://allianzlife.com/

7

3

0

https://policygenius.com/life-insurance/best-life-insurance-companies/

112

2

0

https://nerdwallet.com/insurance/life/best-whole-life-insurance

24

2

0

https://jdpower.com/sites/default/files/file/2026-03-04/2026012%20U.S.%20Life%20and%20Annuity%20Distribution%20Partner%20Experience%20Study%20.pdf

5

2

0

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 are mostly third-party comparison and industry sources, and none carries a linked conflicting claim. Citation frequency varies widely across them, but a citation does not show that a page influenced an answer, and the pages' current contents still need checking.

The agency would review the most relevant sources first, confirm what they currently say about New York Life, and prepare evidence-backed requests only where a genuine gap exists. Publishers control their own content, so requests are proposals, not corrections the client can order.

Step 4. Optimize priority company pages

Questions This Section Answers

  • Which observed New York Life 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://newyorklife.com/articles/senior-whole-life-insurance

19

0

0

https://newyorklife.com/about/our-strength/what-rating-agencies-say

7

1

1

https://newyorklife.com/

6

0

0

https://newyorklife.com/products/insurance/whole-life

3

0

0

https://newyorklife.com/articles/life-insurance-no-medical-exam

2

0

0

https://newyorklife.com/articles/what-is-guaranteed-life-insurance

2

0

0

https://newyorklife.com/newsroom/2026/record-financial-results-for-2025

2

0

0

https://newyorklife.com/articles/life-insurance-annuity

1

1

0

The company's website was cited 49 times across 15 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 are cited across the captured answers, but most of the selected pages appear in no benchmark answer that mentions or recommends the company. The agency ratings page is the clearest exception, appearing in both a mentioning and a recommending answer.

Page briefs would use approved company facts and real audience questions, and would check existing coverage before proposing anything new. Pages with no recorded answer citations are candidates for review, not automatic rewrites.

Step 5. Publish around buyer questions

Questions This Section Answers

  • Which observed buyer questions support content briefs for New York Life?

Question observed in the research

Benchmark answers to this question

Company mentions

Company recommendations

Which is the best annuity company?

6

6

6

Which annuity service provider is best?

6

6

5

What is the highest rated annuity company?

6

6

4

Which insurance company offers the best annuity?

5

5

4

Who is the best annuity company?

5

5

4

Who is the number one annuity company?

5

5

4

Who are the top annuity providers?

4

4

4

Who are the top 5 annuity companies?

5

5

3

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

The recorded questions are almost all variants of the same buyer intent: which annuity company is best, highest rated or number one. New York Life is mentioned in every one of these observed questions and recommended in most, though not all.

The agency would turn these into buyer-question content briefs, checking what the site already covers before proposing new pages. The observations show what answers said, not search demand, so briefs should be framed around answering the question well rather than chasing volume.

Our 90-day action plan

Questions This Section Answers

  • How should the agency sequence the proposed New York Life 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, the scoped fact-review register and the page and source shortlist, each with an owner and review status. The second month produces approved page briefs and justified publisher requests, with confirmed edits, no-change decisions and responses recorded separately.

The final month delivers buyer-content briefs and a fresh scorecard. Completed work is reported separately from measured answer outcomes, so the client can see what was done without treating it as proof of improved visibility.

Methodology

Questions This Section Answers

  • How are New York Life 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 169 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 5403 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

Jul 2026

246

6

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

Sep 2026

227

6

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

Oct 2026

217

6

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

Nov 2026

169

6

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

Reliable results for August 2026 are unavailable. This is a gap in the history, not a result of zero.

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

The monthly samples differ in size and composition, so comparisons show how recorded results changed, not why. Recommendation requires both endorsement and positive wording, and placement counts sit inside recommendations rather than adding to them. Company counts cannot be summed into distinct answers because one answer can cover several companies.

Cited pages and disputed claims still need independent verification, and the exact collection dates for the original answers are unconfirmed. August results are unavailable, which is a gap rather than a zero.

Our first three priorities

First, confirm the facts behind any claim the client wants to defend, using current primary sources, and keep ambiguous matches out of the queue. Second, brief the company pages that already appear in answers, starting with the agency ratings page, and review uncited pages before changing them.

Third, build buyer-question content around the best-annuity-company intent that dominates the observed questions, checking existing coverage first. A monthly scorecard should track recommendation and first-place rates alongside completed work, without presenting either as proof of cause.

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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.

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