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Carolyn Moir-Grant
With over 30 years of experience at Allstaff, Carolyn has been a guiding force in shaping the agency’s reputation as a trusted recruitment partner.
If you, one of your hiring managers, or a future employee asks an AI tool for a list of “top companies to work with” or “the best companies to work with in your industry”, do you know what answers they’re getting, or why?
In most cases, the answers differ significantly from person to person. Sometimes that variation can be explained by location or prior search behaviour. Other times, there is no obvious reason at all.
From an employer’s perspective, this creates a challenge. Tracking this type of visibility is currently near impossible, and likely will be for some time. However, what is possible is improving the likelihood that your organisation appears, and that when it does, the information being presented is accurate, fair, and aligned with reality.
That starts with understanding how AI tools decide what “top” and “best” actually mean.
Job seekers and stakeholders are increasingly using AI tools to answer questions such as:
The critical point for employers is this: The answer an AI tool gives one person is very unlikely to be the same answer it gives another, even when the question appears identical.
That variation depends on the wording of the prompt, the tool being used, and how that tool interprets relevance, authority, and “quality”.
There is a growing conversation around how brands should measure and manage AI visibility. This is particularly relevant in recruitment, where perception directly affects attraction, application quality, and retention.
At Allstaff, we have been researching how three widely used AI systems respond to common employer-related questions, where they source their information from, and how they decide which companies to surface, especially when terms like top and best are used.
The tools used in this research were:
Job seekers are changing how they search. While platforms like Google Jobs still play a central role, many people now start by describing the type of employer they want to work for, rather than searching for a specific role.
AI tools respond to these prompts by generating lists that appear authoritative, but are inherently variable. Depending on how the tool has been trained, what data it has access to, and whether it pulls live information, the same prompt can produce entirely different results.
This makes traditional ideas of rankings or visibility unworkable. The strategic focus instead should be on information accuracy and consistency, regardless of how the question is phrased.
When asking an AI tool about a company, role, or employer reputation, nearly every response differs in at least four ways:
Historically, users have trusted Google to provide relatively stable results. With AI-generated answers, that consistency no longer exists. Asking the same question twice, even within the same tool, can result in different outputs.
For employers trying to attract talent, this unpredictability introduces risk.
A simple internal exercise highlights this clearly: ask several people in your organisation to use the same AI prompt and compare the results. The differences are often striking.
AI tools are designed to generate unique responses. However, when those tools are repeatedly pulling from the same credible, well-structured sources, the variation becomes less damaging.
Employers who actively manage their digital footprint, across their website, job listings, company profiles, and third-party platforms , give AI tools a stronger foundation to work from. That doesn’t guarantee control, but it significantly improves the odds that what job seekers read is something you recognise and stand behind.
To understand this in practice, we tested three different company types:
Each question was asked in exactly the same way across all three AI tools, with location included.
For Company A, the responses varied significantly.
One tool highlighted negative themes such as long hours and weak management support, while also noting the importance of context. Another summarised individual reviews without interpretation. A third suggested that turnover was not unusually high, but varied by franchise or local management.
Across the tools, information was pulled from combinations of:
The key takeaway for employers is not whether one answer was “right”, but that AI confidently constructs a narrative from fragmented data.
Again, the tools differed in approach.
Some presented balanced summaries, others highlighted negative experiences such as pay concerns or shift changes. In several cases, the AI introduced themes that were not explicitly asked for, based on what it inferred job seekers might care about.
Notably, the tools often structured the answers into pros and cons, even when the prompt didn’t request that format. This is an important signal: AI is actively shaping the story, not just reporting it.
Pay data showed the greatest inconsistency.
Figures varied between tools, were often averaged across job boards, and frequently mixed contract types and roles. Some tools included caveats; others did not.
For job seekers, those caveats are rarely the focus. The number itself becomes the takeaway, accurate or not.
For Companies B and C, where limited public information was available, all three tools acknowledged the lack of official data and then proceeded to infer salaries and working conditions using sector averages, nearby listings, or loosely related sources.
This is a critical point for employers. Silence does not equal neutrality. When information is missing, AI tools will still generate an answer.
The most revealing insight came when we tested how AI defines top compared to best.
Using the same detailed prompt for a warehouse role in Paisley, each tool returned completely different companies when asked for the top employers. When a single word was changed to best, the results changed again.
The rationale behind those choices varied:
In some cases, the AI attributed best not to the company, but to the role itself, a subtle but important distinction.
For employers, this means that language matters, and that AI is interpreting value through multiple lenses simultaneously.
AI is already influencing how job seekers shortlist employers. It is shaping first impressions long before applications are submitted, interviews are booked, or conversations begin.
You cannot control AI outputs. But you can influence the inputs.
That means taking ownership of:
This is no longer a future-facing issue. It is happening now, quietly, in the background of the hiring process.
At Allstaff, we see this shift daily. The way candidates ask questions, the assumptions they arrive with, and the expectations they carry into conversations are already being shaped by AI-generated summaries not just job ads or careers pages.
That’s why we’re actively researching how AI tools interpret employer information, where they source it from, and how subtle changes in language can materially affect how a business is positioned. Not to chase rankings, but to understand how employer reputations are being formed when no one from the organisation is in the room.
For the employers we work with, this insight matters. It helps ensure that the conversations candidates are having before they apply are grounded in accurate, representative information and that the story being told about the business aligns with reality.
The question for employers is no longer whether AI is talking about your company.
It’s whether the picture it’s building reflects the organisation you’ve worked hard to create.
If you’re curious how AI tools may currently be presenting your organisation to potential candidates, a conversation can often surface things that aren’t immediately obvious. At Allstaff, we’re actively exploring how these systems shape employer perception, so the businesses we work with are better informed about how they’re being seen, before it starts affecting who applies.