AI is both a help, and a hindrance, to talent acquisition

It’s a tough time to be in talent acquisition.

Yes, these professionals have more tools at their fingertips than ever before. And sure, they’re able to put those tools to use in what’s currently an employer’s market. But for all the ways in which AI has supported recruitment strategy, it has also made it that much more challenging to actually execute on it.

AI has changed the way job postings surface in search results, and the ease with which applicants can submit their résumés. It’s reduced some biases, while reinforcing others. And it’s dizzied those tasked with ensuring compliance with lagging legislation and regulations.

In this e-book, you’ll learn about these and other challenges facing talent acquisition teams, and how they can find a path forward.

Chapter One

AI is changing how people look for jobs, forcing recruiters to keep up

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SEO long ruled how job postings show up in search results. Recruiters now must tailor that strategy to AI.

AI is coming for SEO’s job.

Search engine optimization (SEO) has become an important part of recruiters’ playbooks, dictating how they format their company’s careers site, job postings, and recruitment marketing materials so they surface in search engine results.

But as AI tools reshape how people search for information on the internet, SEO strategy is being upended. AI overviews appear on 21% of Google search results according to a November 2025 analysis by Ahrefs, while certain queries, such as those comprising a long string of words or questions, generated an AI overview at least half the time. And 28% of US adults use AI chatbots and AI search engines to search simple information, according to a survey from content marketing agency Claneo. (To get into the accuracy of the answers provided by these tools would, unfortunately, be to open a can of worms.)

Already for consumer products, companies are adapting their product and website copy to accommodate AI-powered search tools. This strategy, derived from SEO, is referred to by several names, most commonly generative engine optimization (GEO).

Recruiters will have to do the same, experts say.

AI search is already affecting job searches: 11.6% of the more than 1,600 US workers surveyed by employment agency iHire in June 2025 said they’ve used AI tools to research potential employers. Recruitment experts expect that share to grow.

“AI shapes what we decide,” Maria Christopoulos Katris, cofounder and CEO of recruitment platform Built In, said during a panel at Talent Acquisition Week in February. That will apply to job seeking in the not-so-distant future, she added. “Think of a world where, in five years, candidates are starting and stopping their search in the LLMs. You should assume they are starting their search, they’re identifying companies to work for, they’re researching you, and they’re making decisions, all without leaving an LLM.”

For employers that don’t tailor their recruitment strategies for AI search tools, experts warned that recruitment costs could increase, as their job postings get ignored or competitors become more frequently recommended by these LLMs. But there are a few areas recruitment teams can focus on to avoid this fate.

Time to clean house

One way recruitment teams can boost visibility with AI tools is by ensuring their ATS and careers site have clean data and clear information about their employer and its jobs.

“Now, if your jobs don’t come out of your applicant tracking system properly, and if your career site isn’t canonically correct, the algorithm will not find your job and you will end up paying more to get them seen,” said Craig Fisher, founder and lead consultant of employer brand and recruitment tech strategy firm TalentNet Media, during another Talent Acquisition Week panel.

An ATS, like all databases, has different kinds of “schema” that define which data should be inputted and how it should be categorized. This may include information such as pay rate and job location. This data also gets picked up by third-party sites like Google, LinkedIn, or Indeed, and other AI tools scanning the internet for job information. It’s important that the information, right down to the metadata level, is accurate and clean, Fisher said, noting that even doing an existing job posting to create a new one can carry over old metadata that can complicate third-party scanning.

“If you’re not buttoned-down at the ATS level, your job ads aren’t even going to get seen,” Fisher warned.

The same goes for a careers site. Job seekers may ask AI tools to recommend employers or jobs. If recruiters want AI to recommend their company, they’ll have to consistently and repeatedly share information about their company—what it does, where it’s based, the average employee tenure and career background—for LLMs. Displaying that information in easily digestible bullet points can help, he added.

“Think of your jobs as eligible inventory, like on a sales site,” he said.

Focus on reputation

Another critical factor that AI tools assess when recommending an employer is reputation.

“The scariest part for an employer is when you’re being compared. You’ve spent all this time and money and effort getting to an offer stage with a candidate, and they go into an LLM and say, ‘Should I take this offer or that offer?’” Katris said. “Your reputation, and what the LLMs think about you, is going to drive that decision.”

Employers don’t have a lot of control over reputation, as it’s primarily informed by LLMs’ analysis of information from sources outside their career site, like Glassdoor and Reddit, Katris said. But employers can still try to influence it.

“The more you’re investing in controlled sources, the more your controlled sources will drive your reputation and ultimately what candidates see and hear about you,” she said.

To start, employers should try to understand how they’re showing up in LLM and search results by asking the tools questions about their work culture, career growth, perks and benefits, and so on. This could help recruiters understand their company’s strengths and weaknesses.

Then, Katris said, they should create LLM-friendly content that addresses candidates’ top goals or concerns, and post it to sites that LLMs scan.

“If you haven’t written a lot of positive, proactive content about your company in a controlled way on a third-party trust and site, what you will see is that everything that shows up here is from reviews that have been posted across the digital footprint,” Katris said. “And so, effectively, what you’re saying is, ‘We’re good with our former ex-employees describing our employer brand.’”

Recruitment teams will have to do this frequently to see results.

“The brands that will be cited and mentioned the most are the ones that are consistently refreshing their content,” Katris said.

Chapter Two

Overwhelmed by applications, recruiters turn to AI pre-screening tools to winnow down applications

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The majority of talent acquisition pros plan to revamp their recruitment tech stacks to support the pre-screening process.

If life’s a beach, then recruiters are currently caught in a riptide.

Despite the recent hiring slowdown creating more competition in the job market, recruiters have confoundingly discovered that separating the wheat from the chaff has only gotten more difficult.

Nearly three-fourths (72%) of global companies are experiencing a scarcity of qualified talent, according to ManpowerGroup’s 2026 Global Talent Shortage report. That’s only slightly down from the 74% that said the same in 2025.

The data suggests that challenge is acutely felt by recruiters. Two-thirds of recruiters reported getting more applicants per role last year, according to an Employ survey of talent acquisition (TA) pros. Despite the uptick in candidate volume, nearly half (46%) of respondents reported a lack of quality candidates, and listed improving the quantity and quality of candidates, and hiring speed as their top three priorities.

To help reduce strain, TA pros are revamping their recruitment tech stacks, with a focus on using AI to better identify top candidates in the pre-screening process, or when sourcing candidates, reviewing résumés, or conducting initial screening calls.

The same survey from Employ reported that 67% of TA pros plan to invest more in AI-powered recruiting tools, while 37% plan to do the same for sourcing tools and 27% with video-screening tools. Similarly, a January LinkedIn survey found that 66% of recruiters planned to increase their AI use for pre-screening interviews; 70% said they believed it would help them have more valuable conversations with candidates.

To the rescue

AI-powered recruiting tools can include sourcing tools that identify top candidates, chatbots that assist candidates with applications, and tools that evaluate and rank applicants based on job-related criteria, or even conduct initial screening interviews, which could help TA pros save time, Andrew Chimka, senior director of product management at LinkedIn, told HR Brew. And because these tools can conduct initial screenings, the first human interaction can be spent having an in-depth conversation about the job and candidate’s experience, instead of basic information.

“That’s what I think gets people excited, is building that connection with candidates beyond just running through a standard script with everyone collecting the same information,” Chimka said.

Job seekers will similarly benefit from these tools, he said. For example, anyone who’s applied for a job in the last decade may recall being advised to style keywords related to the job in white and a tiny font on their résumé for the applicant tracking systems’ rudimentary scanners. However, because these newer tools use natural language to analyze and summarize applications, candidates should instead dedicate energy to explaining their work background.

“AI makes the process a lot more human, which is exciting. I think it’s a rare place that AI is making things more human,” Chimka said, adding that “there’s a little bit of a re-education, both on the seeking and hiring side of like, just describe what you do, share the skills and things that you have, which I think is great.”

LinkedIn recently began testing AI interviews with its LinkedIn Hiring Pro tool, an AI agent for small businesses. Small orgs don’t always have a dedicated TA pro—or even an HR pro—and often leave hiring to someone else in the business, Chimka noted. This new offering aims to allow employers and job seekers more flexibility. Employers can input the questions they want the interviewer to ask, and the answers they want to hear, and the AI will compare the candidates’ answers to that criteria. Job seekers can practice and interview at a time that works best for them, instead of having to fit into someone else’s schedule. While it’s early days, 80% of candidates who have completed an interview with the tool have given it a positive rating.

That positive response is critical, Chimka noted. Tools that help improve candidate experience will leave candidates with a better impression of a company, something that will “pay dividends long term,” he said.

Compliance considerations

More and more tools are becoming available to recruiters in the initial screening stages. But with more tools comes more scrutiny. While employers have used TA tools for over 20 years, compliance around these tools has become more difficult, partly because “everybody’s paying attention” to recruiters using AI, Danielle Ochs, an attorney and equity shareholder at law firm Ogletree Deakins San Francisco office, and co-chair of the firm’s technology practice group, told HR Brew.

Two of the most prominent lawsuits currently affecting the TA tech space focus on pre-screening solutions. The first, ongoing since 2023, alleges that Workday’s AI recruitment screening tools violated multiple federal civil rights laws, including the Age Discrimination in Employment Act. The second, filed in California earlier this year, alleges that some of Eightfold AI’s résumé screening and candidate scoring tools violated the Fair Credit Reporting Act and a similar California law.

In addition to these lawsuits, several states and cities—including in California, Illinois, and New York City—currently or will soon have in place other laws regulating how AI is used in the hiring process. For employers, the risks “really depend on the details of how the tool is being used,” Ochs said. If a tool is determined to be an automated employment decision-making tool—essentially, a tool that makes decisions with little to no human involvement—employers could be [subject to] one of these laws, Ochs said.

TA teams should work with an internal group to develop an AI strategy plan that covers which tools will be used, how they will be vetted, and how decision-making within the hiring process will happen. They must also ensure employees using the tools are trained on the company’s policies and limitations, Ochs said.

When considering different solutions, TA leaders should ensure that there is still a human making decisions around which candidates to advance and ultimately hire.

“All of these tools are just doing what you instruct them to, and if you feel like they’re not putting you in control, then it’s a very easy way to say, ‘This isn’t a great tool for me,’” Chimka said.

He also recommended TA pros look at brands with which their company has a preexisting relationship, though Ochs encouraged piloting and auditing all tools for compliance risk, regardless of the brand. “Even if you know the tool is widely available and even if it’s widely used and easily accessible, you still need to do your own individualized assessment of the regulatory impact on you, Ochs said.

Employers can also work with tool-vetting experts to assess potential solutions, she noted. It’s important employers negotiate terms and conditions around indemnity, about representations and warranties, and about testing and auditioning the tools with vendors before they are used, she said.

Employers should never assume that a vendor will be the [only] party found liable for solutions that are found to be breaking the law, Ochs warned.

“Employers are responsible for any tool that they bring in the workplace. It’s no different than if they bring in a chainsaw and say, ‘Hey, use this chainsaw to cut down trees,’” she said. “They may be held responsible, at some level, if something goes wrong with the tools.”

Chapter Three

Well, AI still hasn’t solved bias in hiring

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Understanding AI’s impacts on bias remains important as utilization and use-cases grow.

The promise of AI in talent acquisition (TA) has long been to reduce time to hire and assist recruiters in making better, fairer decisions at scale.

As HR and TA teams deploy more AI tools for sourcing, screening, and interviewing candidates, HR pros are learning that AI can reduce, reinforce, or even obscure bias, depending on how it’s trained and used.

Human recruiters are increasingly making decisions based on insights from AI across the globe. While fewer marquee headlines are addressing how AI tools are impacting biases in hiring, it’s still a critical issue for HR teams and the vendors that are working to deploy this technology.

“When AI first came out, you heard a lot of conversation around bias and hallucination and all that, and now you hear less about those things and more about all the new tech and all the possibilities,” Daniel Chait, Greenhouse cofounder and CEO, said. But he cautioned that just because the online discourse has moved on from concerns around bias or other AI-related risk, doesn’t mean that HR pros and vendors aren’t still thinking about it.

Some of the historic data training AI tools are flawed and biased. (Newsflash: humans can be biased.) Since the models are being trained in the context of the human-led hiring process, sometimes it reproduces the same flawed outcomes, but at scale.

Chait suggested that the emerging use-cases, models, and frameworks can deliver amazing unlocks for HR and TA teams, but deploying them should include a pause to understand both the risks as well as the benefits.

But it’s not just data. It’s also you

“Bias emerges not only from the data itself, but also from the dynamic interplay between human behavior and machine learning systems,” according to behavior and motivation scholars Grace Chang and Heidi Grant in the Harvard Business Review.

A 2025 University of Washington study looked at how human-in-the-loop AI use can impact recruiters’ decisions. It found that recruiters who reviewed applicants using AI LLM tools with bias built into the models “mirrored” the inequitable choices of the AI up to 90% of the time. But when recruiters made decisions without AI or with unbiased AI, they chose white and non-white candidates equally, the study found.

“There is a bright side here,” said Kyra Wilson, a UW Information School doctoral student and lead author of the study in a press release. “If we can tune these models appropriately, then it’s more likely that people are going to make unbiased decisions themselves. Our work highlights a few possible paths forward.”

As vendors improve the models and their training, the humans who rely on it (and their bias trusting the technology) could, indeed, produce more fair and unbiased outcomes.

“We’re absolutely still thinking about this topic, putting a lot of effort and resources behind it,” Chait said. “We have paid a lot of attention to the recent industry lawsuits against Workday and Eightfold and our legal team is providing a lot more oversight and input and advice to our product development teams as we build and launch AI solutions to make sure that we’re not exposing ourselves or our customers to these kinds of risks.”

HR leaders and TA vendors are closely watching a lawsuit against Workday involving AI and bias in hiring. The federal lawsuit alleges Workday’s AI-powered candidate screening tools disproportionately overlooked older applicants and those from other protected groups.

Workday argued their tools don’t make final hiring decisions and don’t disparately impact job applicants, but the litigation highlights a growing reality of possible legal problems for HR teams using AI tools that may unintentionally create bias in the hiring process.

A separate lawsuit filed against Eightfold AI highlights different concerns amid allegations that the company’s AI-powered talent intelligence platform can produce biased outcomes in candidate recommendations during the screening processes through its use of secret “dossiers.” The plaintiff claims these are akin to credit reports and background checks, only without the protections to consent to or correct the reports.

These cases signal an increase in compliance scrutiny of AI tools, and in vendors requiring HR teams and customers to consider how they’re using the tools and what their impacts could be, whether negative or positive.

What’s HR to do?

Chait told HR Brew that TA pros looking to use recruiting and hiring software that deploys AI should talk to vendors and understand their AI-use and priorities. AI is capable of doing some really amazing things to improve the hiring process, but each new feature comes with risks, and Chait said it’s good to understand a vendors’ approach to those risks.

“Don’t just take their word for it,” he said before recommending looking for regularly published audits by well-respected third parties.

He also suggested working with vendors tuned into the evolving compliance landscape. They don’t just need to deliver on current compliance requirements, but plan to incorporate emerging laws and regulations as they’re developed, he said.

A sunny future. The entire hiring process could be reimagined in the future. While bias can occur in the résumé-screening process and when winnowing candidate pools for interviews based on résumés and applications, Chiat pointed out that it’s actually human capacity that’s the limiting factor when deciding who gets an interview. But as AI-enabled hiring processes improve and evolve, everyone could get a fairer shot, because AI—not humans who require time and salaries—could be conducting interviews with all applicants, and no one is screened out.

“I do think that the promise of these technologies is so great that if you do it from a principled perspective,I think we can achieve all kinds of good stuff,” Chait said. “I think we can achieve better experiences, better decisions, faster, more efficient processes, and increase fairness and transparency at the same time.”

Chapter Four

AI governance really matters amid evolving compliance landscape

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Lawmakers and regulators have struggled to keep up with emerging AI uses and risk.

There’s a famous saying you’ve probably heard about building the plane while flying it, but for AI governance pros, there’s no hangar in sight. It seems like building AI(rplane) governance systems will continue to occur on the fly.

As AI tools inside the workplace evolve from experimentation and beta testing to a core part of everyday infrastructure, an ongoing challenge faces the pros charged with guiding deployment and use, and managing the technology’s risk. While organizations push forward with AI tools and new processes, the legal and regulatory environment remains laggard, fragmented, and often fluid, making governance a complicated task.

“What our clients are dealing with is—in some ways—very similar to what they’ve been dealing with for the past three years, which is uncertainty,” said Proceptual founder and CEO John Rood, who helps companies with AI governance and compliance efforts. “Not only do we not know what government, at what level, will pass what legislation with any reasonable certainty, we also don’t know if legislation is passed, it will actually be put into effect.”

Lagging

AI legislation and regulation lags significantly behind development and deployment, according to Rood.

State-level efforts in places like Illinois and Texas are continuing to evolve. Colorado’s marquee AI governance law has been undergoing changes and revisions since its adoption. The European Union AI Act has also faced delays and revisions ahead of enforcement.

The resulting persistent uncertainty means companies and their compliance and legal teams lack clarity on what rules will exist and how compliance and enforcement will be pursued.

Enforcement

Even where rules do exist, enforcement is far from settled. Rood pointed to a recent Cornell University study indicating abysmally low participation in New York City’s Local Law 144, which requires employers using Automated Employment Decision Tools for hiring or promotions in NYC undergo bias audits, share results publicly, and notify candidates of their use. Only 5% of NYC companies that were hiring listed audit results, and another 4% complied with transparency notice requirements.

Rood suggested that even those results may be skewed towards compliance, noting that there’s been little enforcement momentum on the part of the city.

Vendors

Deployers are asking vendors to carry more weight as uncertainty persists, HR and enterprise customers are increasingly asking their vendors to help them both understand compliance and provide them with stronger governance, transparency and risk controls in order to play fairly.

“There’s an evolving expectation in the vendor and in the vendor-implementer relationship, where the implementers or deployers of AI systems are pushing a little bit harder on vendors than they have in past years,” Rood said.

Lawsuits against vendors like Workday and Eightfold AI have also raised questions about accountability when AI systems potentially (and allegedly) produce biased or discriminatory outcomes.

What’s HR to do?

Rood pointed to established frameworks from both the National Institute of Standards and Technology (NIST) and International Organization for Standardization (ISO) as a good place to get a compliance and governance strategy that can mitigate risk.

“What we advise clients on now…is to really think about a broad compliance program companies need to be implementing—either the NIST AI Risk Management Framework or ISO 42001 or both—because ultimately that’s going to capture 95% plus of any foreseeable regulation,” he said.

ISO 42001 is a certifiable international standard focusing on formal management systems governing AI use. The NIST AI RMF is a voluntary US-based framework that offers guidance, but no formal certification. Both are aligned with where Rood suggests the eventual compliance landscape may land.

“The actual mechanisms of both the frameworks are like 90% the same,” he said. “ISO tends to be a little bit more process driven. Whereas NIST is more values driven. But functionally…there’s not a lot of meaningful distinctions that really change the way that an organization would implement their governance frameworks based on those differences.”

Governance aligned with either (or both) the NIST AI RMF and ISO 42001 is a good first step, but Rood also recommended layering good governance standards and controls based on prominent frameworks for specific industries and incorporating company-specific risks and corporate and employer values as well.

AI has transformed talent acquisition, and it will continue to do so time and time again. But the disruption alone will not determine the future of the field—its fate will be shaped by the response of talent acquisition teams. For more on how that’s playing out, check out HR Brew’s recruitment and tech coverage.