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You apply for a job, make it through the initial screening and receive an invitation to interview. You research the company, study the position, prepare examples of your accomplishments and click the interview link expecting to meet a recruiter. Instead, questions begin appearing on the screen, a countdown clock starts running and your webcam begins recording. There is no recruiter waiting on the other side, no introductory small talk and no person reacting to your answers. Your first job interview is with technology, and what once sounded like a futuristic experiment is becoming part of the modern hiring process.

Artificial intelligence is moving rapidly across recruiting, from résumé screening and candidate sourcing to interview scheduling, skills assessments, transcription and early-stage interviewing. According to the Society for Human Resource Management, 51% of organizations reported using artificial intelligence in recruiting in 2025, making recruiting the leading HR function for AI adoption. LinkedIn has similarly reported that 37% of recruiting organizations were actively integrating or experimenting with generative AI, up from 27% just one year earlier. For job seekers, this means learning how to make a strong impression in an environment where the first gatekeeper may be software rather than a recruiter.

Your First Interview May Not Include Another Person

One of the most common forms of technology-assisted interviewing is the asynchronous video interview. Rather than coordinating schedules with a recruiter, candidates receive a link and record responses to questions appearing on their screen. They may receive a limited amount of preparation time and perhaps one or two minutes to provide each answer. Other platforms are becoming more conversational, using AI-powered text or voice systems capable of conducting structured exchanges and, depending on the technology, generating follow-up questions based on previous responses. These systems can also produce transcripts, summarize answers, identify job-related information and organize candidate responses for recruiters who decide who advances.

It is important, however, to distinguish between different forms of AI interviewing technology. Not every platform is analyzing facial expressions, eye movements, voice tone or supposed emotional states, despite popular descriptions that sometimes make AI interviews sound like digital lie-detector tests. Some systems focus primarily on transcripts, responses, competencies or other structured job-related criteria, while facial and emotion-recognition technologies have attracted significant scientific, ethical and regulatory scrutiny. Candidates should therefore pay attention to what an employer actually discloses about its interviewing technology instead of assuming that every blink or hand movement is being converted into a hiring score.

Why Employers Are Putting AI Between Candidates and Recruiters

The economics of recruiting help explain why employers are interested in automation. A recognizable company can receive hundreds or thousands of applications for a single position, particularly when the job is remote or attracts applicants nationally. Recruiters have limited hours to review those applications, conduct screening calls, coordinate calendars and document candidate evaluations. AI offers companies a way to automate portions of that workload while processing substantially more applicants than an individual recruiting team could realistically handle manually.

The potential productivity gains are significant. LinkedIn reported that talent acquisition professionals using generative AI estimated that the technology saved roughly 20% of their workweek, equivalent to approximately one working day. LinkedIn also found that the share of recruiting professionals adding AI literacy skills increased 2.3 times in a single year. Employers can use these technologies for candidate sourcing, résumé review, scheduling, interview transcription and administrative follow-up, theoretically allowing recruiters to spend more time interacting with candidates who advance further into the process.

Standardization is another attraction. Traditional interviewing can be surprisingly inconsistent because candidates may encounter different recruiters, different questions and different levels of interviewer preparation. Even the same recruiter can approach a morning interview differently from an afternoon interview after several hours of meetings. Structured automated systems can present candidates with comparable baseline questions and evaluation criteria, potentially creating greater consistency across a large applicant pool. Consistency, however, should never be confused with fairness because a standardized system can apply the same flawed assumptions to thousands of people just as efficiently as it can apply good ones.

Job Seekers Aren't Convinced They Want AI Making Hiring Decisions

Corporate enthusiasm for AI recruiting has not necessarily translated into enthusiasm among workers. Pew Research Center found that 71% of Americans opposed allowing artificial intelligence to make final hiring decisions, while only 7% supported the idea. Even using AI earlier in the hiring process generated considerable skepticism, with 41% opposing the use of AI to review job applications compared with 28% who supported it. These attitudes suggest that applicants are far more comfortable with technology assisting people than replacing human judgment entirely.

The potential effect on employer reputation may be even more significant. Pew found that 66% of Americans said they would not want to apply for a job with an employer that used AI to help make hiring decisions, with concerns about the loss of human interaction contributing to the resistance. That creates a complicated challenge for companies because the same technology that makes recruiting faster can also make the organization appear less personal. Employers routinely invest significant resources in candidate experience and employer branding, yet an applicant's first meaningful interaction with the company may increasingly involve speaking alone to a webcam.

AI Doesn't Automatically Eliminate Hiring Bias

One of the strongest arguments for algorithmic hiring is that computers do not possess the personal prejudices of human recruiters. The problem is that algorithms do not emerge independently of human decision-making. People determine what information systems use, how candidates are evaluated, which historical outcomes become training data and what characteristics supposedly predict job performance. An automated process can therefore reduce some forms of subjective human inconsistency while introducing different risks of its own.

Federal regulators have already made clear that employers remain responsible for discriminatory outcomes even when technology participates in the decision. The U.S. Equal Employment Opportunity Commission has warned that artificial intelligence and algorithmic employment tools remain subject to federal anti-discrimination protections, including protections for applicants with disabilities. Automated assessments can create particular problems when they unintentionally screen out people who could successfully perform a job with a reasonable accommodation.

One EEOC enforcement action demonstrates how automation can magnify rather than eliminate discrimination. The agency alleged that tutoring company iTutorGroup programmed application software to automatically reject female applicants age 55 or older and male applicants age 60 or older, resulting in more than 200 qualified applicants allegedly being rejected because of age. The companies ultimately agreed to pay $365,000 to settle the lawsuit and provide additional relief. The case illustrates a fundamental problem with assuming technology is inherently neutral: automating a discriminatory rule does not transform that rule into an objective hiring standard.

Researchers are also examining whether newer generative AI technologies reproduce demographic disparities. A 2024 study published through the Association for Computational Linguistics tested large language models in simulated hiring decisions and found that under many experimental conditions the models favored applicants with White-associated names over applicants with Hispanic-associated names. Results varied depending on the model, prompt and experimental conditions, which is itself important because it demonstrates how apparently minor technological choices can affect hiring outcomes.

Why Hispanic Professionals Should Pay Particular Attention

For Hispanic professionals, particularly bilingual professionals and people who speak English with an identifiable accent, automated interviewing raises another important question: How accurately does the technology interpret different ways of speaking? Accent bias existed long before artificial intelligence entered recruiting, and decades of workplace research have demonstrated that how someone speaks can influence how others evaluate professional competence even when accent has little or nothing to do with the person's ability to perform the job.

A meta-analysis involving 139 effect sizes and 4,576 participants found that candidates with standard accents were evaluated as more hireable than candidates with non-standard accents, with the disadvantage becoming stronger for positions involving greater levels of communication. A separate 2024 meta-analysis examining 22 studies and 3,615 raters similarly found that applicants using non-standard language varieties were perceived as less competent and less hireable than applicants using standard varieties, even when actual competence was comparable. Research involving Latino candidates makes the issue particularly relevant, with a 2025 study examining personnel selection for an information technology management position finding evidence that Latino accents influenced candidate evaluations.

None of this means Hispanic professionals should attempt to erase their accents or disguise their backgrounds. Speaking English with an accent is not evidence of lower intelligence, weaker expertise or inferior professional ability, and bilingualism can itself be a substantial competitive advantage in an increasingly diverse U.S. marketplace. The practical question surrounding AI interviews is whether automated speech-recognition technology accurately captures what candidates are actually saying, particularly when systems rely on transcripts to organize or evaluate responses.

A 2025 study published in the Journal of Applied Psychology examined automated speech recognition in automatically scored job interviews and found significant demographic disparities in transcription accuracy among commercial speech-recognition systems. Importantly, those transcription differences did not translate into meaningful differences in the automated interview scores examined in the study. That distinction matters because evidence of differences in transcription accuracy should not automatically be transformed into a claim that every AI interview discriminates against accented speakers. It does, however, reinforce the need for employers to validate their systems carefully and for candidates to understand that clear communication now has a technological dimension.

How to Prepare for an AI Job Interview

Trying to “beat the algorithm” is the wrong way to approach an automated interview because candidates rarely know precisely how a particular employer's technology works. A more effective strategy is to communicate qualifications so clearly that both software and the eventual human reviewer can understand the connection between your experience and the position. Preparation should begin with a close reading of the job description, identifying the competencies, responsibilities, technologies and skills the employer repeatedly emphasizes and preparing specific professional examples demonstrating those abilities.

Relevant terminology should appear naturally in those answers, but candidates should avoid simply reciting keywords from the job description. Saying that you possess “leadership, communication, collaboration, strategic thinking and project management skills” does not demonstrate any of them. Explaining that you inherited a struggling six-person team, reorganized responsibilities, introduced weekly progress reviews and delivered a major project three weeks ahead of schedule provides evidence of leadership and project management while naturally incorporating the concepts an employer is seeking.

The familiar STAR framework of Situation, Task, Action and Result can be particularly effective in automated interviews because there may be no recruiter present to redirect a wandering answer. Candidates are responsible for creating their own structure, which means explaining enough context for the response to make sense while moving quickly toward their individual actions and measurable results. Practicing several stories in advance involving leadership, problem solving, conflict, teamwork, adaptability, failure and achievement can create a flexible library of examples without requiring candidates to memorize scripted answers.

Numbers Can Make Your Answers More Powerful

Quantifying accomplishments is valuable in any interview, but it becomes especially useful when a response may eventually appear as a transcript, summary or structured candidate profile. Saying “I helped increase sales” gives the employer little sense of scale, while explaining that you increased regional sales 18% over nine months by rebuilding the client follow-up process communicates both action and impact. Similarly, “I managed a large budget” is less persuasive than explaining that you managed a $2.4 million annual operating budget and reduced outside vendor expenses by 11%.

Candidates should therefore identify relevant numbers before beginning the interview, including revenue generated, money saved, customers served, employees supervised, projects completed, budgets managed, efficiency improvements or percentage growth achieved. Not every professional accomplishment can or should be reduced to a metric, and candidates should never invent numbers simply to make an answer sound impressive. When legitimate measurements exist, however, they help turn broad claims into evidence that can be understood by both a recruiter and an automated system.

Get to the Point Before the Clock Gets There First

Timed interviews make concise communication especially important because candidates can lose valuable seconds providing unnecessary background before answering the actual question. When asked about handling a difficult client, for example, a candidate does not need to spend the first half of the response explaining the history of the company, department and account. A stronger opening would establish the stakes immediately by explaining that one of the company's largest clients was considering leaving after a service failure and that the candidate was responsible for rebuilding the relationship.

The candidate can then explain what happened, what action was taken and what result followed. This approach is not about speaking unnaturally fast or reducing every answer to a sound bite. It is about making the central point clear before adding supporting detail, a communication skill that remains valuable far beyond automated interviews. Executives, managers, customers and colleagues also tend to appreciate professionals who can explain complicated situations without forcing listeners to search for the point.

Don't Turn Yourself Into a Human Chatbot

There is an emerging irony in the modern job market. Applicants increasingly use generative AI to write résumés and cover letters, employers use AI to screen those applications, candidates use AI to prepare interview answers and employers may use another AI system to analyze the responses. Taken too far, recruiting risks becoming a conversation between machines with human beings serving mainly as intermediaries.

Candidates should resist the temptation to respond by sounding more robotic. Generative AI can be extremely useful for interview preparation because it can generate practice questions, compare a résumé with a job description, identify weaknesses in an answer and simulate difficult interview scenarios. Memorizing AI-generated responses word for word, however, can strip away the personal experiences and individual judgment that make a candidate memorable. Hiring managers ultimately need people who can solve problems, manage conflict, build relationships, persuade customers, lead colleagues and make decisions when circumstances are unclear, so the strongest interview material still comes from experiences the candidate actually lived.

The better strategy is to prepare stories rather than scripts. Candidates should develop several professional examples involving leadership, difficult decisions, teamwork, setbacks, customer relationships, innovation and measurable achievement, then practice adapting those experiences to different questions. This produces answers that are structured enough for an automated process while remaining authentic enough to resonate with the human being who may eventually review them.

Your Camera and Microphone Are Now Interview Equipment

Technical preparation has become part of professional preparation. Candidates should position the camera near eye level, use adequate front lighting, choose a quiet environment and test their microphone, webcam and internet connection before beginning. Unnecessary applications and notifications should be closed so an incoming message or software alert does not interrupt a timed response. These details are not about gaming artificial intelligence; they are about removing technical distractions that can prevent a candidate's qualifications from coming through clearly.

Audio deserves particular attention when speech-to-text technology may be involved. Candidates should speak naturally but clearly, avoid rushing through responses and allow brief pauses between major ideas. Professionals with accents should not attempt to manufacture an unfamiliar accent or suppress their identity for the machine. The objective is intelligibility, not linguistic conformity. Candidates should likewise avoid becoming obsessed with whether an algorithm is measuring every blink, smile or hand movement unless the employer specifically discloses that type of analysis. Excessive self-monitoring can make an otherwise confident professional appear uncomfortable and unnatural.

AI Interviews Are Becoming a Legal Issue Too

The rapid expansion of automated hiring is forcing lawmakers and regulators to confront questions about transparency, privacy and discrimination. New York City's Automated Employment Decision Tools law requires certain automated employment decision systems to undergo bias audits before employers use them and requires covered employers to make specified information about those audits publicly available. The law also includes notice requirements designed to give candidates greater awareness when automated tools participate in employment decisions.

Illinois has enacted protections specifically involving AI-analyzed video interviews. Under the state's Artificial Intelligence Video Interview Act, employers considering applicants for Illinois-based positions must notify candidates before an interview when artificial intelligence may be used to analyze their video, explain generally how the technology works and what characteristics it evaluates, and obtain consent before using AI to analyze the interview. Applicants can also request deletion of their video interviews, after which employers generally must delete the recordings within 30 days and instruct other parties that received copies to delete them as well.

Illinois also imposes demographic reporting requirements in circumstances where employers rely solely on AI analysis of video interviews to determine which candidates advance to in-person interviews. These requirements reflect a broader change in the debate surrounding workplace AI. The question is no longer simply whether companies will use artificial intelligence in hiring, but what employers must disclose, how automated systems should be audited, what information can be collected and how much decision-making authority organizations should delegate to software.

The Human Interview Could Become More Important

The growth of AI recruiting does not necessarily mean human interviews will disappear. Automation could instead make later human conversations more consequential. If software increasingly handles résumé screening, scheduling, initial assessments, transcription and standardized questions, recruiters and hiring managers can theoretically spend more time evaluating qualities that are difficult to reduce to structured data, including judgment, curiosity, creativity, leadership, interpersonal chemistry and the ability to navigate ambiguity.

Tomorrow's successful job seeker may therefore need two complementary skill sets. The first is digital interview fluency: understanding how to communicate concisely through structured platforms, becoming comfortable speaking to a camera without immediate feedback and recognizing that parts of the application process may be interpreted by software before a person reviews them. The second is intensely human: storytelling, persuasion, emotional intelligence, relationship building and the ability to demonstrate sound judgment. Professionals who become proficient at both will be better positioned for a labor market where technology determines more of who gets through the door but people still determine much of what happens after they enter.

The New First Impression

A candidate's first meaningful interaction with an employer may now happen before either person speaks to the other. A résumé can be parsed by software, qualifications can be organized algorithmically and interview responses can be recorded, transcribed and summarized before a hiring manager decides whether to schedule a conversation. That can make the hiring process feel as though control has shifted entirely toward technology, but candidates still control how thoroughly they research the opportunity, how clearly they explain their experience and how effectively they connect their accomplishments to an employer's needs.

The objective should not be to trick an algorithm into approving an application. Candidates should instead make their professional value difficult for either technology or people to misunderstand by using specific examples, measurable accomplishments, relevant terminology and clear communication. Artificial intelligence may be changing who, or what, conducts the first interview, but the qualities behind a strong candidacy remain remarkably human: preparation, experience, credibility, judgment and the ability to demonstrate that you can solve problems that matter.

Sources

• An, H., Acquaye, C., Wang, C., Li, Z., & Rudinger, R. (2024). Do large language models discriminate in hiring decisions on the basis of race, ethnicity, and gender? Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics, 386–397. Association for Computational Linguistics.

• Hickman, L., Langer, M., Saef, R. M., & Tay, L. (2025). Automated speech recognition bias in personnel selection: The case of automatically scored job interviews. Journal of Applied Psychology, 110(6), 846–858.

• Illinois General Assembly. (2026). Artificial Intelligence Video Interview Act, 820 ILCS 42. Illinois General Assembly.

• LinkedIn Talent Solutions. (2025). The future of recruiting 2025. LinkedIn.

• New York City Department of Consumer and Worker Protection. (2026). Automated employment decision tools. City of New York.

• Pew Research Center. (2023). AI in hiring and evaluating workers: What Americans think. Pew Research Center.

• Schulte, N., et al. (2024). Do ethnic, migration-based, and regional language varieties put applicants at a disadvantage? A meta-analysis of biases in personnel selection. Applied Psychology, 73(4), 1866–1892.

• Society for Human Resource Management. (2025). A new era of recruiting: Becoming the best strategic talent advisor. Society for Human Resource Management.

• Spence, J. L., Hornsey, M. J., Stephenson, E. M., & Imuta, K. (2024). Is your accent right for the job? A meta-analysis on accent bias in hiring decisions. Personality and Social Psychology Bulletin, 50(3), 371–386.

• U.S. Equal Employment Opportunity Commission. (2022). U.S. EEOC and U.S. Department of Justice warn against disability discrimination in employers' use of artificial intelligence tools. U.S. Equal Employment Opportunity Commission.

• U.S. Equal Employment Opportunity Commission. (2023). iTutorGroup to pay $365,000 to settle EEOC discriminatory hiring suit. U.S. Equal Employment Opportunity Commission.