How to beat the AI algorithm and get the job of your dreams


Job search, laptop screen and living room with hand of person in home for employment closeup. Computer, networking and typing with recruitment website on display for career or work opportunity.

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ZDNET’s key takeaways

  • AI rejects two-thirds of applicants before a human assessment.
  • Professionals must work with AI and use clever tactics.
  • Focus on business outcomes and add a human touch.

Long gone are the days when your major recruitment concern was beating another human to the job. Today, in the age of AI, candidates must overcome automated hurdles before they even reach the interview process. What’s more, many of these job seekers are using AI-enabled tools to try to game the recruitment process.

Recent research from MyPerfectResume revealed that 73% of employers use AI in hiring decisions, with about two-thirds (65%) saying AI automatically rejects applicants before a person sees them.

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On the other side of the recruitment process, almost three-quarters (73%) of younger people who responded to a survey by education specialist Jisc said they used AI in their job applications, particularly for editing or drafting CVs and writing cover letters.

Jack Capel, director at recruitment specialist Harvey Nash, told ZDNET that AI is now deeply embedded in recruitment, with many organizations using AI tools to screen CVs, identify key skills, and assess how well a candidate’s experience aligns with the role.

“The sophistication of these systems can vary significantly,” he said. “Some still rely on basic keyword searches while others use more advanced models that read for meaning, context, and the ‘how’ behind your work. At the same time, many candidates are using AI to refine or rewrite their CVs, which raises the bar for everyone.”

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What we’re left with is a system in which, at least in some cases, AI systems of varying quality assess and reject applications that are often at least partly produced by other AI tools. To an outsider looking in, human resources has never felt less human, and this technological shift has significant implications for professionals seeking work.

As MyPerfectResume career expert Jasmine Escalera concluded, referring to her firm’s research: “Job-seekers must now navigate a system where visibility depends on how well they align with algorithmic criteria, not just human judgment.”

So, what can you do about the rise of AI in recruitment processes? Three areas are key to securing the job of your dreams: using AI tactically, demonstrating business benefits, and adding a human touch.

1. Work with AI, not against it

While Capel recognized that many candidates are now using AI to refine their CVs, which raises the overall standard of applications, that’s not always the case.

Some applicants make basic formatting and language errors that an overreliance on AI can exacerbate. Capel suggested three key tactics.

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First, avoid formatting mistakes that AI screening tools struggle with. Many AI tools can’t read CVs saved as image files or flattened PDFs.

“This issue often results in a blank reading where the AI cannot extract any text at all. Two-column layouts can also confuse less sophisticated models,” he said.

“Use a standard text-based PDF or Word document and keep the layout simple so the content can be read accurately.”

Second, balance keywords with context. Capel said keyword stuffing remains one of the biggest mistakes candidates make, particularly among IT professionals.

“Listing every tool, language, or methodology without explanation is a red flag for both AI and human reviewers,” he said.

“The strongest CVs combine essential keywords with context that explains how those technologies were used and what impact they had. This approach helps both simple and advanced AI models understand the depth of your experience.”

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Third, use AI to enhance your CV, not write it for you. Capel said many recruiters can quickly spot a fully AI-generated CV. Common giveaways include inconsistent spelling, switching between first and third person, and using identical structures across every job.

“AI is a powerful tool to improve clarity, but your CV should still sound like you,” he said. “Make sure the achievements, tone, and examples reflect your real experience so the person they meet at interview matches the person on the page.”

2. Show repeatable business benefits

Stephen Wood, chief operating officer at Rathbones Asset Management, told ZDNET that the big mistake most people make when writing CVs or letters of interest for roles is that they focus on the tasks they’ve completed rather than the business outcomes they’ve delivered.

Wood suggested a different approach: focus on benefits and repeatability.

“What managers want to know is one, did the thing you do have some material benefits, and what were those benefits? And two, do you have a structured process you can bring to another environment that will help you be successful by delivering similar benefits?”

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Wood said it’s important to add depth to this two-pronged approach and demonstrate your role in delivering business benefits. Don’t leave an employer thinking that the great things you achieved are simply the byproduct of a much greater team effort.

“As a manager, you don’t know someone’s strengths unless a candidate can show demonstrably that they’ve actually got a process to show how they approach things successfully on a day-to-day basis and a structure that means that they can bring this approach into different workplaces,” he said.

“When you see stuff that has a material business benefit, then that stands out for me way above someone’s AI-enabled CV with a load of buzzwords in it.”

That approach resonated with Harvey Nash’s Capel, who said that, for technical roles, it is no longer enough to describe what you built.

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“Hiring managers and AI models are increasingly looking for evidence of commercial awareness,” he said.

“Highlight how your work created value for the business, such as cost savings, efficiency improvements, revenue growth, or enhanced user experience. This mix of technical detail and business impact is becoming essential.”

3. Add a human touch

Louise Newbury-Smith, head of UK&I at technology specialist Zoom, recognized the rise of AI in recruitment processes and said her organization, like so many others, uses AI to help analyze applications.

She told ZDNET that professionals who want to beat the AI algorithm and get the job their heart desires with a company like Zoom must ensure they’re answering the exam question, tightly connecting their capabilities to requirements: “You need to be truly looking at your skills match to make sure that you get through to the next stage.”

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Newbury-Smith encouraged people to think outside the box and add a human touch by reaching out to recruiting managers.

“Don’t forget the human connection. If somebody is applying for a role, should they apply to that role through the standard recruitment process, or should they look at who that role is important to, and then reach out directly and get themselves noticed?” she asked.

“You’ve got to show something of your personality in these processes. People want to know who you are and what’s important to you. So, yes, follow the recruitment process, but also think about where you can add value as well.”

Capel echoed similar sentiments, suggesting that, in a crowded labor market, your individuality is a differentiator.

“A simple personalized message to the recruiter or hiring manager on LinkedIn to say you have applied and why the role interests you can help you stand out from hundreds of applicants,” he said.

“This approach signals genuine intent and separates you from generic, AI-assisted applications. The CV is only the starting point. Thoughtful human connection can take you further.”





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TL;DR

Meta stripped NameTag facial recognition code from its AI app one day after WIRED exposed it on 50 million phones. Meta says no decision has been made.

Meta removed nearly all traces of an unreleased facial recognition system from its smart glasses companion app on Friday, one day after WIRED reported that the software had been quietly embedded in an app installed on more than 50 million phones. The feature, which Meta internally called NameTag, was designed to convert faces captured by the company’s Ray-Ban smart glasses into unique biometric signatures and compare them against a database stored on the user’s device. WIRED also found that faces the system failed to recognise were cropped, indexed, and stored locally for future processing.

Andy Stone, Meta’s vice president of communications, told WIRED on Monday that the feature is “purely exploratory,” adding that no final decision has been made on what to do with it. That characterisation sits uneasily with the evidence WIRED documented. The version of Meta AI published the day of WIRED’s Thursday report contained several code libraries explicitly named for face recognition, a process for running the NameTag recognition pipeline, and a “Person recognised” alert the app would have shown if someone were identified.

Friday’s release stripped all of it out, along with a folder where the app would have stored the cropped images and biometric signatures of unrecognised faces. Meta did not answer WIRED’s questions about why the code was removed or whether the changes were planned before the story was published. A few fragments remain in the latest version, including an internal debug menu label and a dormant link meant to open a recognised person’s profile, pointing to parts of the system that are no longer there.

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The gap between Meta’s public statements and the code WIRED found is the central tension. Before the Thursday report, Stone dismissed the findings by writing that the company could not answer questions about how the system would work because “the feature does not exist.” Andrew Bosworth, Meta’s chief technology officer, called the reporting “incredibly misleading” and “absolutely dishonest.” Yet the code was functional enough to include three AI models, one to detect faces, another to crop them, and a third to encode them as biometric data, all embedded in the companion app for a product already at the centre of a mounting privacy crisis.

Meta declined to answer ten questions WIRED posed before publishing, including whether it had already created the database of face profiles NameTag uses, how long the app retains photographs and biometric data of unrecognised people, and whether that data would ever be sent back to Meta’s servers. The company also did not respond to questions about whether it was building NameTag for blind or low-vision users, or to criticism from privacy advocates who warned the system could let stalkers and abusers identify strangers in public.

NameTag first surfaced in February, when The New York Times, citing internal Meta documents, reported that the company was developing face recognition for its smart glasses and considering a launch as early as this year. One internal memo reportedly described releasing the feature during a “dynamic political environment” when privacy and civil liberties advocates would be distracted by other concerns. WIRED subsequently found that much of NameTag’s machinery had been built into the Meta AI app as early as January, months before any public acknowledgement, adding another layer to the company’s pattern of shipping first and disclosing later when it comes to its smart glasses.

Kade Crockford, director of the technology for liberty programme at the American Civil Liberties Union of Massachusetts, said the removal does not undo the original decision to ship the code and pointed to it as evidence that consumer privacy needs stronger legal protection than Congress has been willing to provide. The Massachusetts House of Representatives last week unanimously passed a consumer privacy bill that, if enacted as written, would impose strong enforcement provisions including a private right of action allowing aggrieved users to sue. “State lawmakers need to do their job and step up to protect consumer privacy,” Crockford said.

Meta’s sneaky tactics in slipping the face-recognition code into its smart glasses show exactly why data privacy bills need the teeth of strong enforcement,” Crockford added. “Companies like Meta prioritise their bottom line, so lawmakers need to speak in the only language its C-suite understands.” Whether a code removal prompted by investigative reporting constitutes a victory or merely a tactical retreat depends on what Meta does next, and on whether the regulatory pressure building on both sides of the Atlantic produces enforceable consequences before the feature quietly returns under a different name.



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