What does ChatGPT tell candidates about working at my firm?

Your recruiter has finally found the operations director you have spent six months trying to hire. The message goes out on a Tuesday. By Thursday there is no reply, and there never will be. One likely reason: the candidate typed your firm's name into ChatGPT, asked whether it is a good place to work, read the answer and moved on.

For years, employers controlled most of what a candidate saw: the advert, the careers page, the recruiter's pitch, the interview. Candidates could read Glassdoor, but few read all of it. Now an assistant reads it for them, alongside LinkedIn, the trade press, Companies House and whatever your leaders have said in public, and turns it into a few paragraphs. ChatGPT alone has more than a billion weekly users (OpenAI, August 2026), and LinkedIn, where your staff's tenure and your leaders' posts sit, appears in about 11% of AI answers (Semrush, March 2026). The candidate sees the pattern, not the pitch.

This guide covers what the assistants read about you as an employer, which signals do the most damage, how to check the answer and how to change it. Most of the work sits outside marketing: whether the firm is a good place to work, and whether that shows.

#What do candidates ask ChatGPT about an employer?

They ask whether the firm is a good place to work, what the pay and culture are like, whether it is stable and what to expect at interview. The questions change with the moment, and at each one the assistant's summary carries more weight than anything you send.

Before an interview, candidates use the assistants as a briefing service: what the firm does, who leads it, recent news and what to ask. Your own material is one input among many, and often the least trusted, because the candidate knows the careers page was written to attract them. The assistants gather evidence slightly differently, as we explain in which AI assistants people actually use and how they differ, but they reach for similar sources.

#What do the assistants read to form their answer?

They read review sites, forums, LinkedIn, news coverage, public records, your own careers and about pages and anything your leaders have published, and they weigh patterns across those sources more than any single item. Most of it is written by other people: across all topics, earned media made up 84% of the more than 25 million links cited by ChatGPT, Claude and Gemini in Muck Rack's May 2026 analysis.

Forums and LinkedIn matter more than many founders expect: a Peec AI study of 30 million sources, reported by Search Engine Land in March 2026, found Reddit, YouTube, LinkedIn, Wikipedia and Forbes were the most cited domains in AI answers.

Because the assistant looks for agreement between sources, contradictions are what it notices most. A values page that promises balance, next to a run of reviews describing sixty-hour weeks, does not average out. It becomes the story. Correcting facts that are simply wrong is covered in why ChatGPT describes a firm wrongly and how to fix it.

#Which signals do the most damage?

Five signals reliably produce a discouraging summary: a repeated complaint in reviews, visible churn, recent redundancies, a leadership story that reads badly, and a gap between what you say and what employees say.

#A repeated theme in reviews

One bad review is noise. Ten reviews over two years that all mention poor management are a pattern, and summaries are built from patterns. The theme matters more than the star rating.

#Visible churn

LinkedIn shows how long people stay in each role and where leavers go. If a team turns over quickly, a candidate can see it, and an assistant asked about stability may mention it. A candidate reads that as a prediction about their own future.

#Recent redundancies

A restructuring that felt well handled inside the firm reads, from outside, as instability, particularly if the press covered it and the firm never explained it.

#Leadership controversy

Anything a founder has said or done that was reported badly can surface when a candidate asks about the leadership. Silence does not make it go away; a later, plain account of what happened gives the assistant a second source.

#The gap between claims and evidence

"We invest in our people" with no training anyone has mentioned, or "flexible working" with reviews that say otherwise. The gap is the most informative thing the assistant finds, so the summary is likely to carry it.

#Can we ask our employees to leave reviews?

Yes, as long as the request is voluntary, goes to everyone and comes with no reward and no pressure. What the review platforms forbid is paying for reviews, making them a condition of anything, or drafting them for staff; check each platform's current guidelines before you ask.

An open invitation often helps, because satisfied people rarely think to write and unhappy leavers usually do. Ask the whole team rather than a hand-picked few. A sudden run of five-star reviews from the people you chose, after years of complaints, looks like what it is to candidates and to the assistants reading them.

#How do I change what ChatGPT says about my company as an employer?

Fix the causes of the complaints, then give the assistants more true evidence to read: reviews from a wider share of your staff, leaders writing in public and a specific careers page. Check the answer monthly and give it time to move.

You cannot manage your way out of it. The assistants read sources you do not own, and nobody can rewrite three years of reviews or change how long people stay. The underlying work is what makes a firm easier to run anyway: good managers, fair pay, visible progression and candour about the job.

#Step 1: Fix the causes first

Read every review from the last three years, on every site, and list the themes. If management comes up, train, support or replace. If pay comes up, check it against the market and adjust or explain. If direction comes up, tell people where the firm is going, in writing, and repeat it.

#Step 2: Invite honest reviews from everyone

Once the cause is being dealt with, send one invitation to the whole team to leave a candid review, with no reward and no follow-up pressure. Newer reviews that describe the firm as it is now give the assistant something current to set against the old pattern.

#Step 3: Put leaders in public as experts

When an assistant assesses a firm, it looks at who leads it. Leaders who write plainly about their subject and are quoted in the trade press are read as credible, and their firms as stable. A workable rhythm for each leader is one useful LinkedIn post a week and one longer article a month. That matches what the assistants cite: in Semrush's March 2026 LinkedIn study, articles made up 50% to 66% of LinkedIn citations, those of 500 to 2,000 words were cited most, and around three-quarters of the cited post authors had posted five or more times in four weeks. Individual members accounted for 59% of LinkedIn citations in ChatGPT search and Google AI Mode, more than company pages, and the median cited post had only 15 to 25 reactions, so a large following is not required.

The same writing gets the firm named when buyers ask who to hire, so it pays twice. Our Reputation That Wins service runs this for founders, in their own voice, and whether you can wait a year before acting on AI search explains why starting early matters.

#Step 4: Make your own pages specific

Replace adjectives with facts. Publish salary bands if you can. Describe how progression works, with an example, and what the job is like on a hard week. Candidates and assistants both trust specific, slightly unflattering detail over polish. Awards and accreditations help too, when they are real and named, as whether awards and accreditations make ChatGPT more likely to recommend you sets out.

#Step 5: Respond to reviews like a person

A short, non-defensive reply to a critical review, from a named leader, shows both the candidate and the assistant that someone is listening. The review stays, and the pattern around it changes.

#How do we check what the assistants say about us as an employer?

Run a fixed set of candidate questions each month in ChatGPT, Gemini, Claude, Copilot and Perplexity, each in a fresh chat, and record both the answer and the sources each assistant cites. Answers vary from run to run, so treat a theme as real only when it repeats.

Five questions are enough to start: is [firm] a good place to work; what is the culture like at [firm]; what is the pay like for a [role] at [firm]; what should I know before interviewing at [firm]; and how does [firm] compare with [competitor] as an employer. Add them to the fixed list of 20 to 50 buyer questions we recommend tracking in how to find out whether ChatGPT is recommending your firm.

The sources column is the useful one: if one old thread or news story keeps appearing, you know which evidence needs an answer.

#Does this affect the people we already employ?

Yes. Current staff ask the assistants the same questions, about their pay and about whether to stay. When they leave, they update LinkedIn and perhaps Glassdoor, and the next candidate sees a slightly worse picture. The loop runs both ways: a firm people describe well keeps its people.

For recruitment firms, where candidates are also part of the product, this cuts twice as deep. We cover being recommended to employers in why ChatGPT recommends other recruitment agencies, and what we do for recruitment firms is set out on our recruitment page.

#What to do this month

1. Read every review from the last three years, on every site, and write down the recurring themes in one line each.
2. Take the most damaging theme to the leadership team with a plan to fix what causes it.
3. Run the five candidate questions above in ChatGPT, Gemini, Claude and Perplexity, in a fresh chat, and save each answer with the sources it cites.
4. Send one invitation to the whole team to leave an honest review, with no reward and no pressure.
5. Rewrite the careers page so that every sentence states something specific and true, and remove every claim you could not evidence.
6. Agree a posting rhythm with each leader: one useful post a week and one longer article a month.

#Frequently asked questions

#Does Glassdoor affect what AI says about my company?

Yes, in our experience. When an assistant searches the web for an employer, review pages are among the first things it finds, and a theme repeated across Glassdoor, Indeed and Reddit is far more likely to reach the answer than any single review. Glassdoor is still one source among several, so a good rating will not outweigh a pattern of departures on LinkedIn or a critical news story.

#Can I get negative Glassdoor reviews removed?

Not because they are negative. Review sites remove reviews that break their published guidelines, and you can report one that you believe does, but a fair, critical account of working at your firm will normally stay. The better route is a calm reply from a named leader, a fix for the cause, and newer reviews that describe the firm as it is now.

#Should we publish salary bands on our careers page?

We think so, if you can. Candidates ask the assistants about pay, and if you say nothing, the answer comes from salary estimates on review and job sites, which may be out of date or wrong for your roles. A published band, even a wide one, gives the assistant an accurate figure and saves time for candidates who would never accept it.

#Our firm went through redundancies last year. Is that permanent damage?

No, but it needs an account. If the only public record is a press story or a run of LinkedIn updates, the assistants will describe instability. A plain statement from the founder about what happened, why and what has changed gives them a second source and a more complete picture, and older coverage fades: Muck Rack found citations of journalism drop sharply after six months.

#Is any of this a privacy problem for us or our staff?

Mostly no, with one caveat. The assistants read public sources, such as reviews people chose to write, profiles people chose to publish and news that was reported. But the staff names, photos and profiles you publish on your own careers and team pages are personal data, and UK GDPR applies to how you handle them, as the ICO's employment guidance sets out. Tell staff what you publish and why, keep it current, and take a profile down when someone leaves or asks.

#How long before the answer changes?

Assistants that search the web live, such as ChatGPT search, Perplexity, Copilot and Google's AI features, can reflect new reviews, articles and pages within weeks. Answers drawn from a model's own memory change only when the model is retrained, which takes months. The underlying pattern in reviews takes longest of all, because it moves only as fast as the firm does.

The candidate who did not reply on Thursday will never tell you why. The assistant will, if you ask it. If you want help turning your leaders' expertise into the public presence that candidates and buyers both trust, Reputation That Wins is built for that, and a free Growth Review is a good place to start.

#Sources