Has AI Made Age Discrimination Easier to Hide in Hiring?

There’s one question that keeps coming up, unprompted, from senior professionals and career-seekers well-placed enough to notice: is AI making age discrimination more of an issue for senior level candidates?

It’s not a question I can answer straightforwardly. Because age discrimination is not new to the AI era. And it’s not like there’s a neat statistic I can point to that would objectively disprove (or admit to) discrimination – whether enabled by hiring technology or not.

But, thoroughly anecdotally, I am hearing from senior candidates who seem perfect for a role on paper, but who get passed over for it – with the job still being advertised weeks later. What’s happening here? Is it age? Is it overzealous keyword-matching? Or is there a different flavour of interference at play?

I recall old CV-crafting advice given to over 50s: strip out your graduation dates, trim the meat of your expertise to the last decade or two, and downplay senior titles that you’ve held over time.

But AI-powered applicant tracking systems (ATSs) don’t need to ask a candidate’s age. I’d wager if it were given an age-stripped CV and free rein to guess the person’s age, it would likely do a decent job: from career length, historic job titles, terminology used – they can all give the game away. The old advice likely wouldn’t fool the new system.

So let’s discuss it, because though AI absolutely has a place in hiring, I can’t help but feel that the human element has to come back in, or biases could keep happening quietly, unflagged, undiscussed.

The Problem with Assumed Shelf Life

Before we even get into the AI-ATS part of the argument, we need to address some of the possible logical fallacies that inform age discrimination in hiring.

Older candidates may be discounted due to an assumed shorter in-role “shelf life”, with unspoken concerns around retirement not far from the hirer’s mind. On the flip side, younger candidates are often considered for their youth – considered a safe bet for a nice, long tenure.

However this is all assumption. You could just as easily assume that older employees, regardless of seniority, are likely to want to hold onto a role due to job searches getting harder with age. And you could equally argue that younger leaders are more likely to be go-getting job-hoppers with more opportunities open to them.

Unfortunately there isn’t a solid piece of data we can use to shatter either illusion across all leadership roles. However, one peer-reviewed 2025 study, “CEO tenure: Evidence from U.S. public firms” tracked nearly 4,900 CEO tenures across US public firms from 1992 to 2018. It found that older CEOs are less likely to be dismissed than younger ones, with a substantial number remaining in post beyond age 66.

There’s also some interesting Korn Ferry data from 2020 which examined the age and tenure of individuals holding common C-suite titles. CEOs were oldest on average and had the longest tenure; CMOs were the youngest and had the shortest tenure.

These data points don’t map neatly over the assumptions we’re referring to. But they certainly illustrate that the immediate knee-jerk reaction about age – and youth – in leadership is actually more complex than at first glance.

I’d also add that tenure is shaky ground to work with already – companies restructure, go through rounds of layoffs, and go bust whether those employed like it or not. No level of seniority is immune to that.

Is Age Really the Issue?

Hiring any senior executive or leader comes with significant cost concerns, regardless of their age. After all, seniority equals cost in terms of higher salary and benefits package.

Yet when you add in increased age as a variable, cost concerns become harder to separate from assumptions around tenure and adaptability. Conflating seniority and cost can therefore sometimes be a method of age discrimination by the back door.

The solution here is to address issues like cost, tenure, and adaptability directly, rather than leaning into assumptions. If cost is a concern, address that concern internally – even if that reflects in role compensation. If tenure commitment is a concern, look at the applicant’s track record and ask them about their future plans. If adaptability is a concern, test adaptability during hiring.

Age doesn’t stop someone from being adaptable and affordable – nor does youth guarantee it.

Accumulated Capability vs. Recent Terminology

Some recruitment and selection tools, particularly those incorporating AI-powered screening or ranking, can introduce another layer of complexity into hiring decisions. The concern is how candidates are assessed when automated systems are used to interpret their experience and suitability.

In years gone by, a candidate would get their resume looked at by a human hiring manager who would make a direct judgement on their fitness for the role. Any mismatch in details like preferred terminology would be easily mentally resolved by the hirer or cleared up at initial interview.

But nowadays, hiring is different. Candidates don’t just have to appeal to the hiring manager’s internal criteria anymore. I’m concerned that there’s an added layer of abstraction wherein the hiring manager’s criteria are refracted (and perhaps even distorted) through the lens of the AI and automated pattern-matching – which could leave an ideal candidate automatically rejected due to something as simple as a phrase mismatch.

When the skill of “SEO optimising” your CV becomes more important to your job search than your hard-earned years of experience, that’s a problem.

So where does age discrimination come in? Well, I fear that ATS may enable bias towards more recent, “trendy” technologies like AI and automation, which could deprioritise older candidates whose CVs may include transformation around less recent technology trends like big data and the cloud – which, to be clear, were just as meaningful at the time.

This irony is worth naming: someone who’s navigated several waves of tech change and disruption could hypothetically rank below someone whose resume simply has the newer, shinier words on it.

Now I’m not saying “throw your ATS in the trash and return to the old days”. And I’m certainly not saying that this is the case for all ATS systems in all use cases. But when we know about the potential complexities they may introduce, we can start to unpack and address them.

Where ATS & AI Can Earn Their Place

AI-powered screening tools can be particularly useful in high-volume recruitment, typically for entry-level to lower-management roles, where clearly defined criteria can help identify potentially suitable candidates.
It’s great at filtering against distinct, firm criteria like qualifications, right to work, baseline experience, software proficiency, and filtering out obvious poor fit candidates. And when you’re inundated with hundreds of CVs for an entry level position, identifying genuine, promising contenders is a real, practical problem worth solving for.

Personally I think this is a legitimate, sensible use of the technology. Though, as I’ve argued before, its use still needs a healthy dose of human oversight so good candidates aren’t missed and automated bias doesn’t creep in.

Where ATS & AI Should Be Kept at Arm’s Length

Recruiting for a board-adjacent senior leader is a completely different animal than most other hiring. As professionals ascend the leadership ladder, they move away from a world focused on firm criteria like tech stack proficiency, and towards a world where softer, less quantifiable skills like leadership, people management, board reporting, and networking matter far more.

These are intangible skills that can’t be reduced to rigid keyword-matching or tick-off-able criteria. Overusing pattern-matching tech here means that great candidates could get filtered out before a human ever assesses them. Chiefly because of the abstract nature of these skills, but also because they can easily be fabricated in a CV. Anyone can say they are a supportive, adaptable leader on paper – but these qualities can only really be proven by meeting the individual and speaking to them about their leadership habits.

To gauge human intangibles, you need human judgement.

Where Good Executive Search Comes In

As careers get longer and less linear, experienced contextual judgement matters. There’s so much at stake with a senior hire too; business strategy, cultural expectations, internal politics; this demands that someone put in the time and effort to gauge each candidate thoroughly.

It would be dishonest of me to say that any human-led search is totally immune to bias. A well-run executive search process should introduce greater scrutiny into hiring decisions. That means challenging assumptions, assessing candidates in context and ensuring that potentially strong leaders aren’t overlooked simply because their experience doesn’t fit a predefined profile. It doesn’t eliminate bias, but it creates more opportunities to recognise and challenge it.

The Closest Thing to a Remedy

Bias isn’t going anywhere. People have biases. AI can have biases too. I don’t have a cure for that.

But the nearest thing to a solution that I can offer to any candidate or hiring manager, aside from working with an executive search firm of course, is to network, network, network.

Creating a personal brand and becoming a known quantity to good people is how you get bias working for you rather than potentially against you. Familiarity bias is a strong decision driver – people prefer options they’re already familiar with. So if you’re looking for your next opportunity, becoming a familiar face in the rooms that matter can work wonders.

You can’t switch bias off. You can just make sure it’s pointed – positively – in your direction.