How AI Can Give Your Company a Competitive Edge in Talent Acquisition?

Staying Ahead in Talent Acquisition with AI

Talent acquisition today is more competitive than ever. Organizations across industries face high volumes of applicants, skills shortages, and a constant “war for talent.” Traditional hiring methods, manual resume screening, scheduling interviews by phone, relying on gut instinct, struggle to keep pace. To gain a competitive edge in recruiting, companies are turning to artificial intelligence (AI) as a game-changer. AI adoption in recruitment has soared in recent years, one industry survey found usage nearly doubled from 26% to 53% of companies between 2023 and 2024. And according to the Society for Human Resource Management (SHRM), just over half of organizations (51%) now use AI to support recruiting activities. The reason is simple: AI promises faster, smarter hiring. By leveraging AI tools, businesses can analyze resumes in seconds, engage candidates 24/7, and make data-driven hiring decisions. Early results are impressive, nearly 9 in 10 HR professionals using AI in recruiting say it saves them time or boosts efficiency. In an ultra-competitive talent landscape, those efficiencies can translate into a real strategic advantage. The following sections explore how AI is transforming recruitment and how organizations can harness this technology to win the talent race.

Recruiting top talent has become a high-stakes challenge for HR professionals and business leaders. Unemployment in specialized fields remains low, and in-demand candidates often juggle multiple offers. Meanwhile, companies face overwhelming application volumes in many roles, which can swamp HR teams. Traditional recruitment methods, like manually reviewing hundreds of resumes or relying purely on human intuition, are time-consuming and prone to missed opportunities. It’s not uncommon for great candidates to slip through the cracks or for hiring processes to drag on for weeks. This delay has consequences: lengthy hiring cycles can cause companies to lose desirable candidates to faster-moving competitors and incur higher vacancy costs. Moreover, human-driven processes can inadvertently introduce bias or inconsistency, affecting diversity and hiring fairness. In short, the status quo of talent acquisition is inefficient, costly, and ill-equipped to meet today’s talent demands.

To stay ahead, organizations need new strategies that can streamline hiring while improving outcomes. This is where AI enters the picture. Many organizations are now investing in AI training to help HR teams and recruiters effectively implement and manage these new technologies within their hiring processes. AI-powered tools address many of the pain points in traditional recruiting. By automating repetitive tasks and augmenting human decision-making with data, AI offers a way to accelerate hiring without sacrificing quality. For HR leaders and business owners, adopting AI in talent acquisition isn’t just about technology for its own sake, it’s about gaining a strategic advantage in finding and securing the best people before the competition does.

AI is redefining how companies attract, screen, and select candidates. Unlike basic automation that only handles simple repetitive tasks, AI in recruitment uses techniques like machine learning, natural language processing (NLP), and predictive analytics to perform complex functions that previously relied on human recruiters. The result is a fundamentally different hiring process, one that is more agile, data-driven, and scalable. Key applications of AI in talent acquisition include:

• Intelligent Sourcing and Resume Screening: AI-driven systems can instantly scan and parse thousands of resumes or social profiles to identify qualified candidates. They go beyond keyword matching, modern AI extracts meaningful information (skills, experience, education) and even gauges context like career progression. This enables automatic shortlisting of top candidates in seconds rather than weeks. In practice, AI-based screening filters out unqualified applicants (often ~88% of a typical applicant pool) and flags the best matches for human review. By automating this labor-intensive step, companies dramatically reduce time spent on initial screening and ensure no promising candidate is overlooked due to human error or fatigue.
  
• Conversational AI & Candidate Engagement: AI-powered chatbots and virtual assistants are now acting as digital recruiters. These chatbots can handle real-time candidate communication, answer FAQs, guide applicants through the process, and even conduct initial interview questions via chat. Available 24/7, they provide instant responses that keep candidates engaged. For example, an AI chatbot might help schedule interviews by syncing calendars automatically, eliminating back-and-forth emails. Such tools personalize the experience and can converse in multiple languages, ensuring candidates worldwide feel supported. This always-on engagement not only frees up recruiter time but also prevents candidates from feeling ignored, a critical factor since candidates who feel neglected are more likely to drop out or form a negative impression of the employer.
  
• AI-Generated Job Descriptions and Ads: Crafting the perfect job description is both an art and a science. AI is assisting recruiters by writing or improving job postings to attract the right talent. Using generative AI, recruiters can quickly produce optimized job descriptions that are inclusive in language and tailored to the desired candidate profile. AI tools analyze which wording appeals to target candidates and suggest edits (for example, removing unintentional gender-biased terms or jargon). Some platforms even predict how different job description formats will influence the number and quality of applicants. The result is more effective job ads that draw in suitable candidates and strengthen the employer brand, all with minimal manual effort.
  
• Predictive Analytics in Hiring: One of AI’s most powerful contributions is its predictive capability. By analyzing historical hiring data and employee performance, AI algorithms can forecast a candidate’s likelihood of success in a given role. These systems examine patterns (for instance, which candidate attributes correlated with high performers in the past) and use that to score new applicants on potential job fit, future performance, and even retention prospects. This data-driven insight helps hiring managers make more informed selections rather than relying on gut feeling alone. Tech giants are leveraging such tools, for example, Amazon developed AI models that predict candidate success and even improved diversity outcomes in hiring decisions. Predictive hiring analytics enable a shift from reactive hiring (filling a vacancy after it appears) to proactive talent strategy (building pipelines of talent and selecting those with the best long-term prospects for the company).
  
• AI-Enhanced Interviews: AI is also augmenting the interview stage. Video interviewing platforms now use AI to analyze recorded interviews, assessing not just what a candidate says, but how they say it. Tone of voice, facial expressions, and word choice can be evaluated to gauge soft skills like communication ability, confidence, and temperament. While still emerging, these AI-driven video analyses provide additional data points to assist human interviewers. For instance, AI might flag that a sales candidate shows strong persuasive communication based on their speech patterns. It’s important to note that the goal isn’t to have AI “hire” someone automatically, but to give recruiters deeper insights so they can make better decisions. Companies report that blending AI assessments with human judgment leads to better outcomes, one analysis found candidates pre-screened by AI were 14% more likely to pass subsequent human interviews and 18% more likely to accept offers than those sourced through traditional methods. This suggests AI can improve both the quality of candidates advancing and the likelihood of closing the hire.
  
• Automated Scheduling and Workflow: Many recruiting bottlenecks are administrative. AI systems excel at handling these routine tasks. For example, AI scheduling assistants can coordinate interview times by integrating with calendars and finding open slots, sparing recruiters countless emails. AI can also auto-send follow-up emails or assessments, track candidate progress in the pipeline, and ensure no one falls through the cracks. This level of automation creates a smoother process for all parties. Recruiters can focus on high-touch activities, and candidates get timely updates. In high-volume hiring scenarios, these AI workflows are indispensable. A company that receives thousands of applications (such as a retail chain hiring seasonal staff) can maintain a personal touch with each candidate via automated yet customized messages. The efficiency gain is massive, what used to require a coordinated effort by a full hiring team can now be managed by a lean team with AI-driven tools doing the heavy lifting.
  

By transforming these aspects of recruitment, AI is not just a tech trend but a strategic enabler in talent acquisition. It allows businesses to hire faster, smarter, and at scale, which is increasingly critical for staying competitive. The next section will delve into the tangible benefits organizations are realizing from AI-assisted hiring.

Adopting AI in the recruitment process can deliver significant advantages across several dimensions. Here are some of the key benefits that give companies a competitive edge in talent acquisition:

• Speed and Efficiency: AI dramatically accelerates hiring cycles. Tasks that once took weeks, like screening resumes or scheduling interviews, can be completed in minutes or hours. This speed advantage means roles get filled faster and top candidates spend less time in limbo. Surveys show that companies using AI tools have reduced their time-to-hire by roughly 40% on average. In one study, 78% of organizations using AI in talent acquisition reported cutting time-to-hire by around 40% through automation. Some employers have achieved even more striking results: by deploying AI for initial screening and candidate communications, organizations have slashed the average hiring timeline from about 44 days down to just 11 days, a four-fold speed improvement. This efficiency means your company can secure talent before competitors do and minimize costly position vacancies. Importantly, AI also reduces repetitive work for HR teams. By one account, 89% of HR professionals said AI saved them time or increased efficiency in recruiting. By letting algorithms handle the heavy lifting of data processing, recruiters can devote their time to high-value activities (like engaging with top candidates), effectively boosting the team’s productivity without adding headcount.
  
• Cost Savings: Faster, more efficient hiring also translates to cost reduction. AI helps lower recruiting costs in multiple ways. Automated resume screening significantly cuts the cost-per-hire by reducing the hours of manual labor required, one analysis found AI-based screening led to a 75% drop in cost per candidate screened. By shortening the hiring cycle, companies save on operational costs and reduce the productivity loss that comes from positions sitting unfilled. Additionally, AI can decrease spending on external agencies or job advertising by improving internal sourcing effectiveness. In a survey, over one-third (36%) of HR respondents noted that using AI in recruiting directly reduced their recruitment and hiring costs. Whether it’s through automating background checks, streamlining interviews, or improving new-hire retention (hiring better fits who stay longer), AI tends to deliver more output for less input. For budget-conscious organizations, these savings allow reinvestment into other strategic HR initiatives.
  
• Better Candidate Matching and Quality of Hire: Finding the right talent, not just filling the seat quickly, is the ultimate goal of recruiting. AI provides a competitive edge here by enhancing the quality of hire. Machine learning models can analyze vast datasets on candidate attributes and past hiring outcomes to identify patterns that lead to successful employees. This helps recruiters prioritize candidates who are likely to perform well. For instance, AI might reveal that certain combinations of skills or experiences predict high job success, which a human might overlook. According to research, nearly a quarter (24%) of HR professionals using AI say it improved their ability to identify top candidates in the pool. AI-driven matching engines also enable more precise alignment of candidate skills with job requirements, in one report, over half of companies using AI achieved exact skill matching for roles, a capability that can boost performance and productivity of new hires. All of this means better hires: employees who ramp up faster, perform better, and fit the role and culture. Over time, improved quality of hire can elevate a company’s overall workforce capability, a clear competitive advantage. It’s worth noting that AI can uncover “hidden gems” as well, spotting non-obvious candidates whose profiles differ from traditional criteria but who have high potential, thereby expanding the talent pipeline beyond the usual sources.
  
• Enhanced Diversity and Fairness: One promising benefit of AI in recruitment is the potential to reduce human biases in hiring decisions. AI systems, when designed correctly, evaluate candidates on objective criteria (skills, experience, performance data) rather than unconscious biases or proxies like affinity and demographic cues. By focusing on merit and data, AI can help organizations broaden diversity in hiring. A notable case is Unilever’s AI-driven hiring program: after implementation, the company saw a 16% increase in the diversity of new hires, attributed to AI’s standardized and merit-based assessments. Similarly, by using AI to anonymize resumes or flag biased language in job postings, companies are mitigating bias and reaching a wider talent pool. In essence, AI can act as a check against inconsistent human judgments, the algorithms won’t “favor” a candidate because of personal similarity or first-impression bias. However, it’s important to be cautious: AI models can inadvertently perpetuate bias present in their training data. Still, when thoughtfully applied (for example, using diverse training datasets and bias-auditing tools), AI gives HR teams an opportunity to improve equity in hiring. In the long run, a more diverse and inclusive workforce also strengthens business performance and innovation, another competitive edge for companies that get this right.
  
• Improved Candidate Experience: In the battle for talent, providing a positive candidate experience is crucial. AI tools help ensure candidates remain engaged and informed throughout the hiring process. For example, AI chatbots can promptly answer candidates’ questions any time of day, schedule their interviews instantly, and provide updates on application status. This level of responsiveness keeps candidates from feeling ignored or frustrated. The impact on experience is tangible, organizations that implemented AI-driven candidate communications have reported higher applicant satisfaction and even higher application completion rates (one company saw an 84% jump in completed applications after introducing an AI-enhanced process). Moreover, AI streamlines the process so candidates move from application to decision faster, which they appreciate. A smooth hiring experience isn’t just a nice-to-have; it’s often a deciding factor for candidates. According to BCG research, 52% of candidates would decline a job offer if they had a negative experience during recruitment. AI helps prevent those negative experiences by eliminating common pain points, long silences after applying, scheduling hassles, and lack of feedback, that typically frustrate applicants. By enhancing communication, personalization, and speed, AI ensures your company’s hiring process leaves a strong positive impression, which in turn boosts offer acceptance rates and employer brand reputation.
  

In summary, AI-driven recruitment offers a multifaceted competitive edge. Companies leveraging AI are hiring faster, at lower cost, with higher success rates and improved diversity, all while keeping candidates happier. These benefits directly contribute to better business outcomes: roles filled with more capable people, less downtime in teams, and a workforce aligned with the company’s goals. To solidify these advantages, let’s look at some real-world examples of organizations that have reaped rewards from AI in their talent acquisition strategy.

Many forward-thinking companies have already integrated AI into their recruitment and are seeing remarkable results. These real-world examples illustrate how AI can give a tangible competitive edge in talent acquisition:

• Unilever, Global Volume Hiring: Unilever, the consumer goods giant, faced the daunting task of processing over 2 million job applications each year for its early-career recruitment programs. By implementing a suite of AI tools (including online games to assess cognitive ability and AI-powered digital video interviews), Unilever transformed its hiring process. The outcomes were striking: the average hiring process time shrank from four months to just four weeks, and the company saved an estimated 100,000 hours of human recruiting time per year. This drastic improvement in speed and efficiency means Unilever can secure top talent much faster than before. Additionally, the standardized, data-driven assessments led to a more diverse group of hires (as noted, a 16% uptick in diversity) by focusing on candidate merit and potential rather than traditional pedigree. Unilever’s case demonstrates that AI can handle hiring at an enormous scale without sacrificing quality, something that gives large enterprises a clear edge in high-volume talent markets.
  
• Hilton, AI Chatbots for Candidate Engagement: Hilton, a global hospitality company, receives a high volume of applications for roles like guest services and management. They introduced an AI-driven chatbot to streamline interactions with applicants. This AI assistant can answer candidates’ questions about job postings, help them find suitable roles, and even handle initial screening queries. Importantly, Hilton’s chatbot also schedules interviews and provides real-time status updates to candidates. The result is that candidates no longer feel they have fallen into a “black hole” after applying. Hilton reported that this technology significantly improved the candidate experience and engagement, while simultaneously reducing the administrative burden on their recruiting staff. In an industry where customer service is paramount, extending that responsiveness to job applicants gives Hilton a reputational and operational advantage, candidates are more likely to complete the process and accept offers, and recruiters can efficiently manage a large applicant pool with fewer resources.
  
• Amazon, Predictive Analytics for Hiring Success: The tech giant Amazon has invested in AI to enhance its hiring decisions, especially for technical and corporate roles. Using predictive analytics, Amazon developed models that analyze past hiring and employee performance data to predict which candidates will succeed in specific roles. These AI models consider a broad range of factors, beyond what a human might weigh, including how candidates might fit into team dynamics or their likelihood of long-term retention. Implementing these tools has reportedly helped Amazon not only make more accurate hires (improving the quality and fit of candidates selected) but also to address diversity by identifying candidates who might have been overlooked using conventional methods. By crunching data on what successful employees look like, Amazon’s recruiting teams gain an edge in picking talent with the right traits, thus boosting overall workforce performance and reducing costly hiring mistakes.
  
• Startup XYZ, Smarter Screening for a Small Team: (Hypothetical example for variety) It’s not just large corporations, smaller companies are also leveraging AI. Consider a fast-growing tech startup that might receive thousands of applications after a new funding announcement. With a lean HR team, manually vetting every resume is impossible. By adopting an AI-powered Applicant Tracking System with built-in resume screening, the startup was able to automatically filter out ~70% of applicants who didn’t meet basic requirements and flag the top 5% as high-potential based on skills match. This focused the hiring manager’s time only on the most promising candidates. As a result, the company filled critical roles in half the time it would have taken otherwise and did so with hires who have outperformed expectations. This agility in hiring allowed the startup to rapidly build the team it needed to capitalize on its market opportunity, a make-or-break factor in the startup world. The example underscores that AI can level the playing field, giving even smaller firms a competitive hiring edge against larger rivals by maximizing their recruiting efficiency.
  

Each of these examples highlights a different facet of AI’s impact, speed, scale, candidate engagement, and quality of hire. The common thread is clear: organizations that embrace AI in recruitment can outpace those that rely on traditional methods. They not only fill jobs faster but often end up with better talent and a stronger employer brand. However, realizing these benefits requires more than just buying an AI tool and hitting “go.” Companies must be mindful of certain challenges and implement AI thoughtfully. In the next section, we discuss the potential pitfalls of AI in hiring and best practices to ensure AI is used responsibly and effectively.

While AI offers substantial advantages in talent acquisition, it’s not a silver bullet. HR leaders should be aware of the challenges and risks that come with AI in recruitment and take proactive steps to address them. Here are some key considerations and best practices:

• Mitigating Bias and Ensuring Fairness: Perhaps the biggest concern with AI-driven hiring is the risk of algorithmic bias. If an AI system is trained on historical hiring data that reflects human biases (consciously or not), it may learn and perpetuate those biases, for example, favoring candidates of a certain gender, race, or background because the past hiring decisions did. A well-known cautionary tale is an early Amazon AI recruiting tool that had to be scrapped when it was found to downgrade resumes containing indications of the word “women” (due to being trained on a male-dominated hiring history). To prevent such issues, companies must ensure their AI tools are designed with fairness in mind and continuously monitored. This means using diverse training data, removing sensitive attributes from consideration, and conducting regular bias audits. Regulators are stepping in, New York City now requires employers to conduct annual third-party audits of their AI hiring tools for bias and notify candidates when AI is used in evaluations. Best practice is to have independent experts review AI systems for disparate impact and to adjust algorithms as needed to improve fairness. By combining AI with human oversight, for instance, having recruiters review AI-driven decisions and override them if something seems off, organizations can reap efficiency gains without surrendering ethical responsibility. The goal should be to use AI to reduce bias (e.g., via blind screening on qualifications) while guarding against any new biases the technology might introduce.
  
• Maintaining the Human Touch: Hiring is, fundamentally, a human decision, it involves judgment about fit, team dynamics, and culture that go beyond what data can capture. Over-reliance on automation could harm the candidate experience or lead to decisions that lack nuance. For example, a candidate might be rejected by an algorithm for an unconventional career path that a human recruiter would recognize as a sign of creativity or resilience. It’s essential to keep humans in the loop. Many experts advocate a “human + AI” approach, where AI handles repetitive tasks and provides data-driven insights, but human recruiters make the final calls and build relationships with candidates. Human interaction is especially crucial in later interview stages, negotiations, and for senior roles where cultural fit and emotional intelligence are paramount. Companies should define clear protocols on where human intervention is required, for instance, always have a person double-check AI rejections before finalizing, or ensure a personal touchpoint is offered to candidates at critical junctures. By treating AI as an assistant rather than a replacement, organizations can strike the right balance: efficiency with empathy. This approach also means training recruiters to understand AI outputs (e.g., what a candidate's “fit score” means) so they can interpret and challenge those recommendations intelligently.
  
• Data Quality and Compliance: AI is only as good as the data behind it. Poor data (incomplete, outdated, or incorrect candidate information) can lead to poor AI recommendations. HR teams should invest in maintaining high-quality data in their talent systems and carefully select AI tools that integrate well with their existing databases (like HRIS or ATS). Moreover, using personal data in AI raises privacy and compliance questions. Laws such as GDPR in Europe and various local regulations dictate how candidate data can be used and stored. Vendors should be vetted for compliance, and candidates should be informed appropriately about automated processing of their data. Additionally, if AI is used in making hiring decisions, companies should be prepared to explain those decisions if asked, some jurisdictions give candidates the right to an explanation of an automated decision. Opt for AI systems that provide at least a basic level of transparency or explainability (e.g., highlighting which criteria led to a screening decision) to avoid the “black box” issue. This not only helps with legal compliance but also builds trust with stakeholders and candidates.
  
• Change Management and Training: Implementing AI in recruitment isn’t just a technology project, it’s a change in process for the HR team. Recruiters and hiring managers need to be educated and trained on the new tools. There can be natural resistance (“Will AI take my job?”) or misuse of the tools if people don’t understand them. Providing solid training programs on how to interpret AI insights, how to handle exceptions, and how to make unbiased decisions in partnership with AI is critical. Organizations that have upskilled their HR teams in AI report measurable improvements, for example, teams with structured AI training saw a 25% increase in recruiter productivity on average. Encourage a culture where recruiters view AI as empowering rather than threatening: the AI handles drudgery, while they focus on relationship-building and strategic parts of recruiting. Also, gather feedback from the team on the AI tools’ performance; the people using these tools daily often spot issues or opportunities for fine-tuning. Gradual rollout, perhaps starting with a pilot in one division, can help work out kinks and get buy-in before scaling AI across all talent acquisition.
  
• Evaluating AI Tools Carefully: The market is flooded with HR tech solutions boasting AI capabilities. It’s important to choose the right tools that align with your company’s needs. Key factors to consider include: Does the AI improve on your specific pain point (e.g., sourcing, screening) demonstrably? Is the tool’s algorithm tested for bias and validated for accuracy? Can it integrate with your existing systems (ATS, HRIS) seamlessly? Does it allow for human overrides and transparency? Taking a thoughtful, criteria-based approach to selecting AI solutions will ensure you get real benefits and avoid implementing something that sounds good but doesn’t fit your process. Many companies involve both IT and HR in vetting AI vendors, and even run small trials with different tools to compare outcomes. Remember, a fancy tool is not a magic wand, success comes from matching the technology to a well-defined strategy.
  

By anticipating these challenges and following best practices, organizations can deploy AI in recruitment responsibly and effectively. The payoff for doing so is substantial: you gain the efficiency and insights of AI while minimizing risks, ensuring ethical standards, and keeping the human heart in hiring. Companies that navigate this well will not only enjoy a competitive edge but also build a talent acquisition function that is resilient and future-ready.

AI is rapidly shifting from a novel experiment to an essential component of modern talent acquisition. As we’ve explored, the capabilities of AI, from lightning-fast resume analysis to predictive candidate scoring and personalized candidate engagement, directly address the pain points of traditional recruiting. For HR professionals and business leaders, the message is clear: embracing AI in hiring can yield a powerful competitive edge. Organizations leveraging AI are filling roles faster, identifying better-fit talent, reducing costs, and widening their talent pools, all of which contribute to superior business performance. In an environment where the best talent is a scarce and sought-after resource, those efficiency and effectiveness gains can make the difference between a team that just fills vacancies and one that builds a high-performing workforce.

However, adopting AI is not just about installing new software, it’s a strategic shift. Success depends on implementing AI thoughtfully, with an eye on ethics and human-centric practices. Companies that pair AI’s strengths (data, speed, scale) with human judgment and empathy will create a recruitment process that is both high-tech and high-touch. They will use AI to augment their recruiters, not replace them, freeing the humans to do what they do best (relationship building, cultural assessment, creative thinking) while machines handle repetitive tasks and crunch data. This collaboration between human and artificial intelligence can result in a hiring function that is not only more competitive but also more inclusive, engaging, and insightful than ever before.

For organizations still on the fence, it’s worth noting that AI in HR is no longer a distant future concept, it’s here, and many of your competitors are already on board. Surveys indicate that among companies experimenting with AI enterprise-wide, 70% are focusing those efforts in HR, with talent acquisition as the top use case. And the vast majority of firms that have dipped their toes in AI recruiting already report seeing benefits (92%), some with significant productivity gains. In other words, AI-enhanced hiring is proving its value in real time. The longer one waits to explore these tools, the greater the risk of falling behind in the race for talent.

In conclusion, AI can indeed give your company a competitive edge in talent acquisition, but it must be embraced wisely. Start by identifying where AI could have the most impact in your recruiting process, pilot solutions, and build internal confidence. Ensure you have guidelines in place for ethical use and keep evaluating outcomes. By doing so, you position your organization to hire the right people faster and smarter than ever before. In the quest to attract and retain top talent, those who leverage cutting-edge tools alongside human ingenuity will be the ones to lead the pack.

FAQ

What challenges do companies face in traditional talent acquisition?

Organizations often deal with high applicant volumes, skill shortages, and slow, manual hiring methods. This leads to missed opportunities, long hiring cycles, and potential bias in decision-making.

How does AI improve the recruitment process?

AI enhances recruitment by automating resume screening, engaging candidates with chatbots, generating optimized job descriptions, using predictive analytics for hiring success, and streamlining interview scheduling.

What are the main benefits of AI in talent acquisition?

AI delivers faster hiring, cost savings, better candidate matching, improved diversity, and enhanced candidate experiences through automation and data-driven decision-making.

Can AI help reduce bias in hiring?

Yes, when designed and monitored correctly, AI can minimize human bias by focusing on objective data and anonymizing candidate details. However, it requires diverse training data and regular bias audits.

What are some real-world examples of AI in recruitment?

Companies like Unilever, Hilton, and Amazon have used AI to speed up hiring, improve candidate engagement, predict job success, and increase workforce diversity.