Job interviews are changing. More companies are using AI video interviews to help screen and assess candidates. This technology is becoming more common in early stages of hiring, especially for high-volume roles.
At the same time, traditional interviews with human recruiters are still used, especially in later stages or for roles that require more conversation and judgment. Companies are now combining both approaches to create hybrid interview processes. According to a recent survey, 62% of job seekers believe AI improves their hiring chances while 60% still demand human interaction at critical stages.
This article explains how AI interviewing works, how it differs from human-led interviews, and where each method fits best.
What is AI interviewing and does it work
AI interviewing refers to the use of one-way interview software to conduct or assist with job interviews. These tools simulate parts of a traditional interview by asking questions, recording answers, and analyzing responses using machine learning.
There are several types of AI interviewing tools:
- Video-based interviews: Candidates record responses to preset questions. AI reviews facial expressions, speech patterns, and word choice.
- Chatbot interviews: Text-based bots conduct real-time Q&A sessions and evaluate written responses with software like Paradox AI.
- Voice analysis tools: Audio-only interviews focus on tone, pace, and language structure.
These AI screening tools use algorithms to score answers based on specific criteria. They look for patterns such as clarity, completeness, and relevance. Some tools also compare responses to ideal benchmarks or past successful hires.
According to a Harvard Business Review study, over 80% of Fortune 500 companies now use some form of AI in their hiring process. Many use it for early screening to reduce time-to-hire and maintain consistency across applicants. Unilever reports 75-90% reductions in recruitment cycles and £1 million annual cost savings through AI-powered video interviewing.
Emotional intelligence is one core strength of human interviewers. It allows them to observe tone, body language, and emotions to better understand how a candidate communicates. This is particularly important given that 93% of communication meaning derives from nonverbal cues which humans process holistically but AI struggles to accurately interpret. This helps when evaluating soft skills such as teamwork, empathy, or leadership.
- 24/7 Availability: Candidates can complete interviews on their own schedule
- Consistent Questions: All applicants receive the same prompts and conditions
- Data-Driven Insights: AI can detect patterns that may not be obvious to human recruiters
How human-led interviews still matter
Conducting human-led interviews remain an essential part of the hiring process. People use experience, intuition, and emotional awareness to understand responses in ways that automated systems cannot.
Emotional intelligence is one core strength of human interviewers. It allows them to observe tone, body language, and emotions to better understand how a candidate communicates. This helps when evaluating soft skills such as teamwork, empathy, or leadership.
Humans are also skilled at recognizing cultural fit. They can adapt questions based on how a conversation flows, and they often follow up based on what they sense a candidate is trying to express.
Interviews with people allow for two-way communication. Candidates can ask questions and build a connection with the interviewer. This helps both sides understand if the job and workplace are a good match.
Key differences between AI and human interviews
AI and human interviews use different methods to evaluate candidates. Each method leads to different types of results and experiences.
- Structure vs. Flexibility: AI interviews follow a fixed script. Human interviews often change during the conversation based on what the candidate says.
- Scale vs. Depth: AI systems can evaluate many candidates at once. Human interviews take more time but allow for deeper conversations.
- Objective vs. Subjective: AI uses data to reduce certain types of bias, such as judging based on appearance. Human interviewers may bring personal opinions, but they can also understand context.
- Efficiency vs. Connection: AI interviews are often faster and allow candidates to respond on their own schedule. Human interviews allow for real-time conversation and rapport building.
In the hiring process, AI is often used in early stages to screen a large number of applicants. Human interviewers usually take over in later stages to assess communication style, cultural fit, and problem-solving ability.
Can AI interviewing reduce bias or make it worse?
AI interviewing can help reduce some types of bias in hiring, but it can also introduce new forms of bias if not carefully managed.
One way AI may reduce bias is by standardizing the interview process. Every candidate receives the same questions, in the same order, with the same evaluation criteria. This removes variation that can come from a human interviewer's mood or preferences.
However, AI recruiting systems rely on training data. If the data used to build the AI system includes biased patterns. For example, favoring certain education backgrounds can mean AI learns those patterns and applies them in its decisions.
Some companies have reported success using AI to increase fairness. For example, structured AI interviews that focus on job-related skills have helped screen large groups of applicants more consistently. In contrast, other systems have faced problems. Amazon discontinued an AI hiring tool after it was found to downgrade résumés that included the word "women's," because the training data reflected biased hiring practices.
Best practices for reducing bias in AI interviews include:
- Diverse training data: Use data from a wide range of candidate backgrounds
- Regular auditing: Review results across different demographic groups
- Human oversight: Include human review in the decision-making process
- Candidate transparency: Provide clear explanations about how AI is used
Efficiency cost and candidate experience
Here is how AI interviewing can help companies.
1. Time savings for smaller teams
AI interviewing systems save time by automating early steps in the hiring process. For teams with limited staff, this reduces the hours spent reviewing applications and conducting initial interviews.
AI tools can reduce time-to-hire by up to 75%. Companies using automated video interviews report saving 20 to 40 minutes per applicant during screening. If a team receives 100 applications per role, this can mean saving dozens of hours per hiring cycle.
Small teams often use AI to manage scheduling, send interview links, and score responses automatically. These tools are most helpful during early screening and skills-based assessments.
2. Cost comparison with traditional interviews
Video interview pricing often requires upfront implementation costs, such as software licenses or API integrations. These can range from $99 to $10,000 depending on the platform and company size. Once in place, the cost per interview is typically lower than traditional methods.
A human-led interview process involves recruiter hours, scheduling time, and follow-up coordination. On average, a traditional interview may cost $50 to $200 per candidate. In contrast, AI-based interviews can cost between $5 and $50 per candidate after setup.
Some hidden costs include platform training, system maintenance, and data storage. Traditional interviews may incur costs from extended timelines and delays in candidate communication.
3. Impact on candidate perception
Candidate reactions to AI interviews vary based on age, familiarity with technology, and role type. Surveys show that younger candidates are generally more comfortable with automated video interview software. These candidates often appreciate the flexibility of asynchronous formats and faster feedback.
Older candidates or those applying for senior roles may prefer human contact and show lower satisfaction with automated systems. Some report feeling disconnected or unsure how the AI evaluates their responses.
How companies present AI interviewing affects candidate perception. When the process is clearly explained and includes follow-up communication, candidates are more likely to view it as fair.
Ethics and compliance for async interviews
Here two major elements to consider with async interviews.
1. Data privacy measures
When using AI to conduct asynchronous (async) interviews, companies are responsible for how they collect, store, and use candidate data. This includes video recordings, audio, written responses, and system-generated analysis.
Before an interview begins, companies need to give clear disclosures to candidates. These disclosures explain what kind of data will be collected, how it will be used, and whether any third parties will access it.
Data retention policies define how long candidate data is stored. Some tools allow employers to set custom retention periods, while others follow default timelines. These policies vary by region and are often guided by privacy laws like GDPR in Europe and CCPA in California.
Interview data is usually stored on cloud platforms. Secure storage includes encryption, access controls, and audit logs to track who accesses the data and when.
2. Regulatory guidelines to watch
Several countries and regions have laws that regulate the use of AI in hiring. These laws cover fairness, transparency, and accountability in automated decision-making.
In the United States, Illinois requires employers to disclose when AI is used to analyze video interviews and to obtain consent from candidates. New York City's Local Law 144 regulates the use of automated employment decision tools and requires bias audits.
The European Union's AI Act classifies AI systems used in hiring as "high-risk." It introduces requirements around transparency, testing, and human oversight.
To monitor regulation changes, companies often rely on legal counsel, regulatory updates from government websites, or industry newsletters focused on HR technology.
How hybrid interviewing combines the best of both
Hybrid interviewing combines AI tools and human interviewers in a single hiring process. It uses automation for specific tasks while keeping people involved where human judgment is needed.
There are three main types of hybrid models:
- Sequential: AI conducts the first stage of interviews. Human interviewers take over in later rounds.
- Parallel: AI and human interviewers assess candidates at the same stage, using different methods.
- Integrated: AI supports the human interviewer during the session by providing real-time analysis.
Choosing which parts of the process to automate depends on the role, the number of candidates, and the type of information being evaluated. For example, AI is often used for high-volume screening or skill-based assessments. Human interviewers are often involved in final rounds or when evaluating communication style.
Several companies have implemented hybrid models. Unilever uses AI to assess recorded video responses for early screening. Human recruiters then evaluate the shortlisted candidates in person. Hilton automates initial interviews for hospitality roles using AI, followed by human interviews to assess service-oriented skills.
Key benefits of hybrid approaches:
- Efficiency Without Sacrificing Quality: Automate screening while preserving human judgment
- Better Candidate Experience: Balance convenience with personal connection
- Reduced Bias: Use multiple assessment methods as checks and balances
- Scalable Yet Personal: Handle volume while maintaining a human touch
Real steps to start balancing AI and human interviews
Here's how to balance both one-way and live interviews.
1. Choose the right tools
AI interviewing tools vary in how they collect, analyze, and report candidate data. Some focus on video responses, while others use chat or voice-based inputs.
Key features to evaluate include the ability to standardize questions, deliver structured feedback, and integrate with other hiring systems. Tools that allow human oversight and offer explainable scoring models support more transparent processes.
When reviewing vendors, ask how the system is trained and tested for bias. Request documentation on their validation studies and inquire whether the platform allows for regular audits.
2. Test on a small scale
Before using AI across every role, companies can begin with a trial. This involves selecting one or two departments or job types for early testing. Entry-level or high-volume roles are often used first.
Clear success metrics help evaluate the trial. These may include time-to-screen, interview completion rates, or alignment between AI scores and human feedback. After the pilot, teams gather input from recruiters, hiring managers, and candidates.
The pilot period usually lasts between four and eight weeks, depending on the hiring cycle. After evaluation, the company decides whether to expand, adjust, or discontinue the tool.
3. Keep a human touch in final rounds
AI tools often support early-stage interviews, but later rounds are conducted by people. This involves designing a clear transition from AI to human interviewers.
Human interviewers may use AI-generated insights to guide their questions or focus on areas that were not covered in the earlier stage. This ensures the final interview includes both structured data and a live interaction.
To maintain consistency, companies may compare AI evaluations with human assessments for similar candidates. This helps confirm alignment and identify any gaps in the process.
Finding your perfect blend of AI and human touch
Creating a balanced interview process involves identifying what works, testing it, and adjusting based on results. This includes reviewing how AI and human-led steps perform at different stages of hiring.
To refine the process over time, organizations often track key data points. These may include time-to-hire, candidate drop-off rates, interview scores, and hiring outcomes.
Candidate feedback is a useful source of insight. Companies can collect it through short surveys after interviews, asking about clarity, ease of use, and overall impression.
FAQs about balancing AI and human interviews
How can small businesses implement AI interviewing on a limited budget?
Small businesses can start with affordable AI screening tools that integrate with existing applicant tracking systems, focusing first on high-volume positions.
What measures help candidates feel comfortable with AI interviews?
Clear explanations about how the AI interview works, practice opportunities, and transparent communication about data usage help candidates feel more at ease.
Which interview stages benefit most from human involvement?
Final-stage interviews, culture fit assessments, and discussions about complex role responsibilities typically benefit most from human involvement.
