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Recruiting 2.0: A Guide to AI-Driven Talent Acquisition

People are a company’s greatest competitive advantage. But organizations of all sizes struggle to source talent at scale—especially for technical roles. Despite record layoffs in the technology sector in 2023, 70% of leaders report facing a digital skills shortage.

Now, recruiters are turning to AI-powered tools to make hiring faster and smarter. A full 96% of HR professionals believe AI will have a significant impact on talent acquisition. These tools are an increasingly popular choice for tasks ranging from talent sourcing and screening, to recruitment workflow optimization and objective skill-based testing. 

In this guide, we survey the current state of AI recruiting—from AI technologies and their applications, to the challenges associated with implementation. 

What is AI-driven recruitment?

In AI-driven recruitment, recruiters leverage AI-enabled software and other technology to connect with the best talent as quickly as possible. This allows them to fill talent gaps more efficiently. 

Despite misconceptions, AI isn’t a replacement for human recruiters. It instead acts as a support system at each stage of the hiring process, augmenting human insight with increased agility and factual accuracy. In the process, it also saves time and money, contributing to a higher recruitment ROI. 

Companies of all sizes, including enterprises, have already adopted AI recruiting processes at scale. For example, IBM Watson Talent’s AI is an all-in-one platform for candidate screening and assessment. Domino’s deployed Pandologic’s automated job advertising application to reduce cost per applicant by nearly 86%. 

As of 2023, 99% of Fortune 500 companies have already implemented some aspects of AI technology in their hiring processes, as have 65% of recruiters. 

Common applications of AI in recruitment 

AI recruiting tools have driven considerable impact for recruiters—streamlining processes and analyzing large volumes of data in a fraction of the time humans would take. Here are some of the most popular use cases for AI in recruiting.

Faster hiring

67% of hiring professionals say AI saves them time during the hiring process. AI tools are useful for handling the minutiae of everyday recruiting tasks, such as: 

  • Writing job descriptions and outreach messages
  • Analyzing and shortlisting suitable CVs based on career track, skill sets, and other pre-specified criteria
  • Helping candidates self-schedule interviews by showing them available time slots and sending out auto-reminders
  • Making transcripts of virtual interviews 
  • Consolidating feedback across hiring teams and functional orgs 

The cumulative effect of all this help? Freeing up recruiter and people operations time for more high-value activities, like in-depth interactions with the candidate or strategic optimizations. 

Better screening

More than half of recruiters say shortlisting candidates from a large applicant pool is the hardest part of recruiting. AI vastly speeds up this process with resume-screening algorithms that look beyond mere keyword recognition. By identifying patterns—e.g., types of projects worked on, tenure across positions, or leadership roles held—AI synthesizes insights into candidate suitability for the role in question. Eightfold AI, for instance, highlights characteristics—like validated skills and likely skills—to paint an accurate picture of a candidate’s potential. 

Better technical assessment

AI-powered assessment tools present an efficient way to objectively evaluate a candidate’s strengths. The best tools can even personalize the difficulty level of the questions based on the candidate’s detected skills;, for example, does this via a tech-focused candidate assessment solution. 

Smoother candidate experience

AI-powered chatbots can answer questions and help candidates through each step of the hiring process. These chatbots use conversational AI powered by Large Language Models (LLMs) to learn about a candidate’s skill sets and guide them towards the best open roles, reducing time-to-hire and application drop-off rate. 

Electrolux Group, for example, implemented Phenom’s AI-powered platform to provide targeted job recommendations to candidates, enable auto-scheduling of interviews, and conduct one-way interviews that recorded text and audio/video responses. As a result, the company saw a 51% decrease in incomplete applications, a 9% decrease in time-to-hire, and 78% increase in time saved through AI scheduling. 

Reduced bias

68% of recruiters believe AI can help to remove unintentional bias and evaluate candidates more fairly. Recruiters can leverage these tools to implement a standardized screening process that shortlists candidates based on demonstrated ability alone, while generative AI can flag exclusionary language or hidden biases in hiring-related content. Textio does this excellently, by providing instant recommendations for bias-free language choices in job descriptions and performance reviews. 

Core technologies used in AI recruiting

While new applications continue to be developed, these are some of the most common AI recruitment technologies available. 


Natural language processing is a useful tool for understanding candidate strengths and attributes based on their choice of words. It can identify points of interest in interview transcripts or text-based responses and provide recommendations for further discussion.

Video interview analysis 

AI-powered sentiment analysis can pick up important non-verbal cues about the candidate during a virtual interview or video screening. Factors like body language, tone of voice, expressions, and hand gestures often indicate a lot about the candidate’s temperament and personality. AI can also point out opportunities for interviewer improvement, such as by indicating points where follow-up questions can be asked.

Automated sourcing 

AI tools can scan job boards, social media platforms, and industry-specific community forums to identify candidates who match pre-selected job requirements.

Predictive analytics 

AI can deliver insights about a candidate’s potential based on their resume, skill sets, social media presence, and interview performance. It can also identify indicators of how they’re likely to stay on the job.  

Challenges in AI recruiting

For all the improvements it offers, AI recruiting comes with unique considerations. Some of the challenges recruiters leveraging these technologies face are well documented. 

Learned biases

An AI application is only as good as the data models that train it. If a company or industry has a history of hiring biases—whether conscious or unconscious—the application can pick up these biases and replicate them at scale. In 2018, Amazon’s recruitment algorithm was reported to favor male candidates in recruitment data and mistook “being male” as an indicator of success. It began systematically screening out applications from women— perpetuating the same male bias in technical roles that the program had sought to counteract. 

Inaccurate sentiment analysis 

AI’s interpretation of facial cues and body language during virtual interviews can be misleading—introducing the possibility for bias toward differently abled candidates or those from diverse backgrounds. 

For instance, an AI tool might note that a candidate who sits stiffly and shows no visible emotion has less of a positive attitude. But the candidate in question might have a physical or neurodevelopmental disability limiting the expressions they can show or might come from a culture where displaying emotion during the interview process is discouraged. 

Lack of personalization

Many AI recruiting tools don’t offer customization options for their technical assessments. By using them to screen candidates, companies might end up shortlisting people who have general technical proficiency but not the specific skill set that the company is looking for. 

Privacy risks 

AI recruiting tools use sensitive candidate data to analyze candidate suitability. Without mature data policies and practices in place, sharing proprietary applicant data with AI tools can incur high security risks.

Costly implementation 

While many AI recruiting tools are affordable, integrating them with the rest of the tech stack can be challenging. This is especially true for large enterprises using on-prem or legacy technology. 

Lack of accountability 

One concern with an AI tool driving processes like resume screening or preliminary testing is the lack of insight into its decision-making process, especially when selecting or deselecting a candidate. If AI screens out a candidate before any human recruiter views the candidate’s application, new oversight strategies are required to assess the decision and determine whether the AI acted appropriately.

Difficulty in sourcing talent

While AI recruitment tools are adept at screening profiles on job boards, they lack the ability to nurture and develop sustainable talent pipelines. The technical talent gap urges many companies to identify early-career talent who can join the workforce with job-ready skills. This is a task best suited to internal teams or external partners equipped to source and train young graduates before they enter the job market. For example, CodePath partners with the world’s top brands to source and develop tomorrow’s tech leaders. Learn more about our programs for employers here

Implementing an AI-backed recruiting approach

Despite these drawbacks, AI can be a useful and efficient aid to talent acquisition as long as recruiters have an objective view of its capabilities and shortcomings.. Companies planning to invest in AI recruiting tools should keep the following points in mind. 

Vet AI vendor compliance practices  

With a vast range of AI recruiting tools on the market, recruiters need to exercise due diligence when choosing. Questions to ask AI vendors include where they get their data from, what data compliance practices they abide by, who can access the data, and what data security measures they have in place. 

Prioritize functionality to maximize ROI

To get the most ROI, companies should invest in AI solutions that have the most impact at the lowest cost. For instance, it makes sense to invest in tools that help candidates schedule interviews, since the primary criteria there are speed and convenience. 

Understand AI’s limitations 

AI cannot and should not ever be a replacement for human judgment. While AI can rank candidates based on resume points or interview performance, recruiters should only use the data as a guide and not as a substitute for their own impression of the candidate. By setting clear guidelines around where exactly AI can contribute, recruiters can benefit from the convenience that it brings without compromising on empathy, intuition, and human fairness. 

The future of technical recruiting has already arrived

Talent acquisition is increasingly trending toward a hybrid model—one where AI and human recruiters collaborate to drive intelligent, timely hiring decisions. 

But AI-enabled talent teams still need a plan for developing a consistent pipeline of early-career talent. CodePath partners with colleges and universities to train CS students in the crucial technical and soft skills they need to graduate ready to make an impact.  

Read How CodePath Prepares Students For Success