With AI enabling jobseekers to apply for dozens of roles at the same time, employers are facing unprecedented volumes of applications. Here’s how five organisations are adapting their hiring processes to meet this challenge.

Artificial intelligence (AI) is rapidly creeping into the recruitment process, reshaping both how candidates apply and how employers assess talent.

Recent research from Robert Walters shows over half of jobseekers in Australia and New Zealand now apply for 20 or more roles at a time, often using AI tools to instantly generate or tailor resumes and cover letters. 

Robert Walters reports that some job ads now receive over 600 applications in the first 24 hours, yet only around one in 10 applications are truly relevant to the role.

As AI becomes more sophisticated and widespread, this challenge will only intensify; Gartner predicts that by 2028, one in four candidate profiles worldwide will be fake.

That puts significant pressure on recruiters to balance speed with scrutiny. Many are doing so with the help of automation – Gartner notes that half of candidates believe AI is already screening their applications. 

However, without careful use of AI in recruitment, the process risks devolving into AI-generated resumes being screened by AI filters with little human oversight, creating inefficiency rather than reducing it.

In response, many organisations are developing innovative approaches that combine human judgment with responsible use of technology to manage the high volume of applications and maintain fairness.

Here are five strategies HR, leaders and recruiters say are helping them adapt to the surge of AI use in hiring.

1. Taking a nuanced approach to AI red flags

To avoid spending too much time on irrelevant applications, some recruiters are making use of software designed to flag AI-generated content.

“To tackle this common issue, we created a multi-layered filter,” says Pankaj Khurana, Vice President of Technology and Consulting at recruiting firm Rocket. “The first layer focuses on outcomes and results. 

“[For example], a statement like ‘responsible for cloud migration’ lacks meaning. In contrast, ‘moved 20+ applications to AWS and cut hosting costs by 30 per cent’ indicates that the candidate achieved tangible results. This kind of detail is much harder for a generic AI prompt to generate.”

The second layer looks at the consistency of skills, he explains. When skills don’t logically fit together or there are no examples of their application, that raises an alert in the system.

While this filter has cut down review times significantly, he warns against treating tools like this as definitive. AI checkers are far from foolproof, and taking their word that an application is irrelevant might lead employers to dismiss qualified candidates without giving them the chance to contextualise any inconsistencies in their application.

“[When the system] raises a red flag, I don’t immediately discard those resumes – I tag them for further review,” says Khurana. “A recruiter can then explore the conversation to determine if the candidate truly has that skill set.”

By combining automated checks with human judgment, recruiters can reduce wasted effort without overlooking strong candidates who may just need to clarify their experience.

One way to monitor the effectiveness of your screening process is to trial red-flag filters alongside existing processes, then track whether flagged candidates truly underperform at later stages. This kind of evidence base can help refine hiring criteria while avoiding the risk of excluding strong applicants too early.

“You can’t get a true sense of a person’s character from a piece of software. Those qualities are what make a team great, and that assessment should always be done by a human.” – Kevin Heimlich, Chief Executive Officer, The Ad Firm.

2. Putting skills into action

In response to the increase in AI-generated resumes, some employers are shifting away from what candidates claim on paper and focusing instead on how they demonstrate their abilities.

“We saw the surge [in AI-generated applications] early,” says Austin Rulfs, Founder of mortgage broking firm Zanda Wealth. “A junior analysis position received 420 applications in 72 hours, and approximately 60 per cent of them read like AI-templated copy.”

In response, Rulfs redesigned the process around a practical demonstration of skills.

“Applicants take a 20-minute test that reflects the job: they balance 30 transactions, indicate three irregularities and draft an 80-word report,” he says.

The success of the task is measured against three main criteria: clarity, ownership and sense of risk – all of which are difficult for AI to replicate at a human level.

“The results are clear,” says Rulfs. “Interviews per hire at the site dropped from 12 to six, and [candidate] quality doubled. The number of offers accepted increased by 22 per cent. And the rate of probation failures decreased to [from 14 per cent] to four per cent after 12 months.”

While work sample tests aren’t a new idea, placing them earlier in the recruitment process can help weed out non-serious applicants before they absorb too much recruiter time.

3. Adding human signals

Another way recruiters are responding to the rise in AI applications is by looking for ways to introduce more human touchpoints in the early stages of the hiring process.

“[We’ve seen] an increase in AI-generated applications that look human on the surface but feel generic once you read closely,” says John Beaver, Founder of Desky. “To address this, we ask candidates to send a short video or audio introduction that is no longer than a minute. 

“It is a simple step, but it quickly shows who has genuine enthusiasm. I remember two applicants who had almost identical resumes. One spoke with energy and clear purpose while the other sounded robotic and disengaged.”

Others report using five- to fifteen-minute ‘micro-interviews’ in the initial stages of recruitment to evaluate candidates’ communication skills, enthusiasm and ability to think on their feet before moving them into longer interview rounds.

These early touchpoints help employers balance efficiency with meaningful human connection during the hiring process.

4. Leaning on trusted networks

Desky has also adjusted its hiring strategy for the age of AI by leaning more heavily on trusted networks.

“Employee referrals have helped us tap great talent,” says Beaver. “Last year, about 40 per cent of our hires were referred.

“These referrals bring candidates who are qualified and were also vouched by a person on our team, which reduces wasted time on generic [AI-generated] job board submissions.”

For employers looking to reduce their reliance on high-volume job boards, initiatives like employee referral bonuses, internal mobility schemes and targeted outreach through professional networks all broaden access to candidates whose skills and backgrounds are already validated.

That said, employers should be mindful of the risk that heavy reliance on referrals could unintentionally narrow the talent pool if not complemented with other sourcing strategies.

“Referrals bring candidates who are qualified and were also vouched by a person on our team, which reduces wasted time on generic [AI-generated] job board submissions.” – John Beaver, Founder of Desky

5. Choosing the right areas to automate

For many employers, managing the increasing volume of job applications is becoming near-impossible without the help of some form of automation. The challenge lies in how to apply AI in ways that save time without undermining the quality of hiring decisions.

“Before, our recruiters spent so much time just sifting through resumes, sending out emails to schedule interviews and answering the same basic questions over and over again,” says Kevin Heimlich, CEO at The Ad Firm.

“Now, we have AI chatbots that can handle all that initial communication. They send out confirmations, answer common questions about the job and get interviews scheduled on the calendar without any manual effort from our team. 

“It lets recruiters focus on the things that actually matter – they can spend their time building real relationships with candidates, digging into their experience during interviews and finding out if they’re a good fit for our company.”

While AI supports efficiency at the front end, Heimlich stresses the importance of knowing where technology’s limits lie.

“It’s great for things like interview scheduling or screening resumes for basic requirements. You can set it up to look for specific keywords or certifications, which saves a tonne of time,” he says.

“[But we] never use it to screen for things like personality or cultural fit. We have found that you can’t get a true sense of a person’s character from a piece of software. Those qualities are what make a team great, and that assessment should always be done by a human.”

Instead of overhauling an entire recruitment system, HR teams can start by identifying any repetitive processes that drain recruiter time, and piloting an automated tool in those areas. 

Given the speed at which AI is transforming recruitment, hiring practices may also need to be revisited more often to keep pace with evolving technologies and candidate behaviours. What will remain constant is the need to strike a balance between efficiency and fairness.

Want to learn more about how to use generative AI to enhance your HR processes? This short course from AHRI covers the fundamentals of GenAI technology, including how to apply it to various HR tasks, be strategic in positioning AI’s role in the HR value chain and address privacy and fairness concerns.