American Addiction Centers Appears in 53.0% 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

  • American Addiction Centers is mentioned in 52.97% of benchmark answers, but only 4.18% of answers recommend it.
  • The main factual issue is a small set of low-priority founding-year conflicts that should be verified against primary records first.
  • External pages such as Wikipedia and BBB are frequent citations and should be checked before any correction requests are made.
  • Buyer questions cluster around insurance acceptance and online recovery, making them useful topics for new content briefs.

Answer Capsule

In November 2026, American Addiction Centers appeared in 241 of 455 AI answers included in the Drug Rehab Centers benchmark (52.97%). It received 19 recommendations (4.18%), with 18 Top 3 placements and 12 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

American Addiction Centers recorded 241 mentions (52.97%) and 19 recommendations (4.18%) across 455 answers included in the benchmark. Top 3 placements numbered 18; first-place recommendations numbered 12. Framing among its mentions was 22 positive, 218 neutral and 1 negative.

Rank

Company

Mentions

Mention rate

Recommendations

Recommendation rate

Top 3

Top 3 rate

#1

#1 rate

1

Hazelden Betty Ford

162

35.60%

30

6.59%

29

6.37%

19

4.18%

2

American Addiction Centers

241

52.97%

19

4.18%

18

3.96%

12

2.64%

3

The Recovery Village

124

27.25%

16

3.52%

15

3.30%

2

0.44%

4

Recovery Centers of America

52

11.43%

9

1.98%

8

1.76%

1

0.22%

5

Caron Treatment Centers

21

4.62%

7

1.54%

5

1.10%

0

0.00%

6

Banyan Treatment Centers

29

6.37%

1

0.22%

1

0.22%

0

0.00%

6

Gateway Foundation

10

2.20%

1

0.22%

1

0.22%

0

0.00%

8

BrightView

34

7.47%

0

0.00%

0

0.00%

0

0.00%

8

Footprints to Recovery

0

0.00%

0

0.00%

0

0.00%

0

0.00%

8

Phoenix House

10

2.20%

0

0.00%

0

0.00%

0

0.00%

American Addiction Centers appears in more answers than any competitor listed, yet affirmative recommendations are comparatively rare. That gap is the central business issue: being named is not the same as being endorsed, and the two measures should be tracked separately rather than blended into one visibility story.

Framing is overwhelmingly neutral, with positive wording far outnumbering negative. Neutral framing is not a problem to fix in itself, but it suggests the brand is treated as a reference point rather than a preferred choice. Proposed work should test whether clearer, verifiable differentiators shift endorsement without assuming they will.

What changed since last month

Questions This Section Answers

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

Metric

Aug 2026

Oct 2026

Nov 2026

Mentions

86 / 127 (67.72%)

82 / 121 (67.77%)

241 / 455 (52.97%)

Recommendations

6 / 127 (4.72%)

3 / 121 (2.48%)

19 / 455 (4.18%)

Top 3 recommendations

0 / 127 (0.00%)

3 / 121 (2.48%)

18 / 455 (3.96%)

#1 recommendations

0 / 127 (0.00%)

1 / 121 (0.83%)

12 / 455 (2.64%)

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

From October 2026 to November 2026, American Addiction Centers' mention count rose from 82 to 241, while its recommendation count rose from 3 to 19. The recommendation rate was 2.48% and 4.18% respectively.

The November sample is much larger than the two earlier months, and the recorded mention and recommendation totals rose alongside it. Because the monthly samples differ, the movement should be read as a change in what was recorded, not as evidence that anything caused the brand to perform better.

The earlier months also show that recommendation counts can move independently of mention counts. That pattern argues for watching both measures over time and resisting a single-month narrative. Proposed work should keep monthly comparisons descriptive and flag sample differences beside any trend statement.

AI recommendation trend

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

0%1.25%2.5%3.75%5%Aug 2026Oct 2026Nov 20264.72%Aug 2026: 4.72%2.48%Oct 2026: 2.48%4.18%Nov 2026: 4.18%
  • American Addiction Centers4.18%

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 Addiction Centers?

AI answers contained 2 conflicting claims about American Addiction Centers: 0 critical, 0 high, 0 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.

Only two conflicting claims were recorded, both rated low priority, and both concern the founding year. That is a small, contained fact-checking workload rather than a broad accuracy crisis, so the sensible response is a focused review rather than a sweeping audit.

A visibility comparison against the listed competitors would help the client see where endorsement is thin relative to mentions. Pairing that with a short claims list lets the team separate reputation work from factual cleanup, which are different tasks with different owners.

Step 2. Make the website consistent

Questions This Section Answers

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

Review topic

Severity

Recorded AI claim A

Recorded AI claim B

founding year

low

founded 2004

founded 2007

founding year

low

founded 2011

founding date inconsistent (2004 vs 2007)

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 topic is narrow: the founding year appears with several different values across recorded answers. The right first step is to confirm the entity and the correct date from primary company records before treating any answer as wrong.

Once the fact is confirmed, a page brief can state the founding year plainly and consistently wherever the company describes its history. If the records themselves are ambiguous, the honest outcome is a documented no-change decision rather than a correction request.

Step 3. Align the external narrative

Questions This Section Answers

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

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/American_Addiction_Centers

4

3

2

https://bbb.org/us/tn/brentwood/profile/drug-abuse-information/american-addiction-centers-inc-0573-37076606

4

4

1

https://growthsharematrix.com/blogs/owners/americanaddictioncenters

2

0

1

https://community.thriveglobal.com/f-a-t-e-from-addict-to-entrepreneur-with-michael-t-cartwright-of-american-addiction-centers-and-michael-g-dash/

1

0

1

https://portersfiveforce.com/blogs/brief-history/americanaddictioncenters

1

0

1

https://wikiwand.com/en/American_Addiction_Centers

1

0

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.

Several cited pages relate to the company, and a few are linked to conflicting claims. A citation does not show that a page caused an answer, so the review should start with what each page currently says, not with an assumption of error.

The Wikipedia entry and the Better Business Bureau profile are the most frequently cited and the most closely tied to company mentions, which makes them reasonable starting points. Proposed work is a source-by-source check followed by evidence-backed requests only where a genuine inaccuracy is confirmed.

Step 4. Optimize priority company pages

Questions This Section Answers

  • Which observed American Addiction Centers 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://americanaddictioncenters.org/stimulants/cocaine/how-long-in-system

14

14

0

https://americanaddictioncenters.org/rehab-guide/celebrity-high-profile

10

10

0

https://americanaddictioncenters.org/about-us

7

5

0

https://americanaddictioncenters.org/neurontin-abuse/gabapentin-cause-withdrawal-symptoms

7

4

0

https://americanaddictioncenters.org/addiction-medications/gabapentin/how-long-in-system

6

2

0

https://americanaddictioncenters.org/withdrawal-timelines-treatments/post-acute-withdrawal-syndrome

6

0

0

https://americanaddictioncenters.org/

4

4

0

https://americanaddictioncenters.org/insurance-coverage/optum

4

4

4

The company's website was cited 217 times across 115 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 site is cited widely, but the selected pages mostly appear in answers that mention the company without recommending it. The insurance coverage page is the clear exception, appearing in recommending answers as well.

That contrast is worth exploring: coverage and payment questions may be where the site earns endorsement. Proposed page briefs should use approved facts and audience questions, and existing coverage should be checked before any new page is proposed.

Step 5. Publish around buyer questions

Questions This Section Answers

  • Which observed buyer questions support content briefs for American Addiction Centers?

Question observed in the research

Benchmark answers to this question

Company mentions

Company recommendations

rehabs that accept optum

2

2

2

Is there an online addiction support?

2

1

1

online alcohol recovery

1

1

1

online recovery programs

1

1

1

rehab centers that accept humana

1

1

1

rehabs accepting cigna

1

1

1

rehabs covered by compsych

1

1

1

rehabs covered by guidewell

1

1

1

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

The recorded questions cluster around insurance acceptance and online recovery options. These are practical, decision-stage questions, and the company already appears in the answers to several of them, sometimes with a recommendation.

That makes them good candidates for content briefs that answer the question directly and verifiably. Because these are recorded answer observations rather than search demand, existing pages should be reviewed first and new pages proposed only where a genuine gap is confirmed.

Our 90-day action plan

Questions This Section Answers

  • How should the agency sequence the proposed American Addiction Centers 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 a review status. This keeps early work auditable before any outreach begins.

The second month produces approved page briefs and justified publisher requests, with confirmed edits, no-change decisions and responses recorded separately. The third month adds buyer-content briefs and a fresh scorecard. Completed work will be reported separately from measured answer outcomes, with no claim of guaranteed visibility gains.

Methodology

Questions This Section Answers

  • How are American Addiction Centers 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 455 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 715 captured answers and 3561 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

Aug 2026

127

6

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

Oct 2026

121

6

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

Nov 2026

455

6

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

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

Recommendations require both endorsement and positive wording, and placement counts sit inside recommendations rather than adding to them. Answers can name several companies, so company totals do not sum to distinct answers. Sentiment describes answer wording, not customer experience.

Monthly samples differ in size and composition, so comparisons show what was recorded, not why. Cited pages and disputed claims still need independent verification before any correction is proposed.

Our first three priorities

First, confirm the founding year from primary company records and resolve the entity question before any correction is considered. This is a small, bounded task that removes ambiguity from later page work.

Second, review the most-cited external pages for current accuracy, beginning with the encyclopedia entry and the business profile, and prepare requests only where a confirmed error exists. Third, build page briefs around insurance acceptance and online recovery questions, checking existing coverage first. Each step should be tracked as completed work, separate from any future change in answer visibility.

/ 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