How to use AI in Interviewing

Interviews are a critical stage in recruitment, where deeper evaluation and mutual fit are assessed.

However, scheduling conflicts, note-taking, and subjective decisions often slow the process. AI in interviewing simplifies logistics, supporting interviewers with insights, and enhancing evaluation rigor.

7 Ways to adopt AI in Interviewing Process

1. Scheduling and Coordination

AI automates interview scheduling. Instead of exchanging emails to align calendars, an AI assistant accesses participants’ schedules (with permission) and suggests available time slots. Candidates receive a link or message to choose a preferred time. Once selected, the AI books the meeting and sends invites—no manual input needed.

Companies like Mastercard saw 88% of interviews scheduled within 24 hours and reduced coordination time by 85%. AI also handles rescheduling and sends reminders to reduce no-shows. This automation keeps candidates engaged and frees recruiters to focus on interview preparation.

2. Preparing Interviewers with Insights

AI provides interviewers with candidate briefings before the meeting. It compiles resume highlights, screening results, and online profiles into a concise overview. Interviewers enter the conversation informed, without searching multiple sources.

During the interview, AI can transcribe the conversation and suggest follow-up questions. For example, if a candidate mentions a project, AI might prompt, “Ask about the project outcomes.” It also displays key competencies to assess, helping interviewers stay focused and thorough.

3. Structured Interview Questions

AI generates structured interview guides tailored to each role. Recruiters can request behavioural and technical questions for specific positions.

For example: “Describe a time you delivered a project under a tight deadline” or “How do you handle team conflicts?” Each question targets a defined competency. AI also populates scorecards, encouraging consistent evaluations across candidates.

This supports fair, evidence-based assessments and aligns with best practices in structured interviewing.

4. Interview Transcription and Note-Taking

Instead of taking notes manually, interviewers can rely on AI to transcribe conversations (with consent). AI highlights key moments, such as achievements or technical details. After the interview, it can summarize main points and flag concerns.

For example:

Led a team of five on a product launch

Emphasized learning from failures

Lacks direct people management experience

These summaries help interviewers recall details and assist hiring managers who couldn’t attend. AI reduces human error and bias by capturing exact responses.

5. Objective Evaluations and Scorecards

AI supports post-interview assessments. It reminds interviewers to complete scorecards and can pre-fill sections based on transcripts. If asked to rate problem-solving skills, AI highlights relevant candidate responses and suggests a rating.

Some platforms analyse tone and word choice to supplement evaluations, though this raises fairness concerns. More commonly, AI ensures all competencies are addressed and feedback is submitted promptly, reducing gaps in the evaluation process.

6. Reducing Bias and Improving Decision Quality

AI helps standardize interviews and collect consistent data. It checks whether interviewers covered all required topics and flags omissions. Bias dashboards monitor scoring patterns and alert teams to potential issues. AI aggregates feedback into a single view. For example: “Three interviewers praised technical skills; two raised concerns about culture alignment.” This summary helps hiring panels make informed decisions without lengthy discussions. It also advances inclusive hiring.

7. Enhancing Candidate Experience

AI improves the candidate experience through fast scheduling and clear communication. It can send agendas or role descriptions before interviews. Some companies offer AI practice tools, allowing candidates to rehearse and receive feedback.

In one-way video interviews, AI evaluates responses and decides who advances, saving candidates from scheduling live calls early on. Transparency is key—candidates must know when AI is involved and be assured of fairness. Many companies pair AI evaluations with human reviews to maintain trust.

Real-World Examples

AI in interviewing tools has demonstrated real impact. Paradox Olivia conducts text-based interviews and schedules next steps for high-volume roles. It also answers candidate FAQs, boosting engagement.

Metaview records interviews (with consent) and generates transcripts and insights to improve interviewer quality. HireVue offers AI-scored video assessments and structured evaluation forms used by large enterprises. These tools show AI’s role in scheduling, content delivery, evaluation, and feedback.

Compliance and Privacy

Recording or transcribing interviews requires candidate consent and legal compliance. Organizations must follow regulations like GDPR and emerging AI laws when adopting AI in recruitment processes.

Internal guidelines often allow transcription for note-taking but prohibit automated hiring decisions without human review. Maintaining human oversight ensures fairness. AI can inform and recommend, but recruiters or panels must make final decisions.

In conclusion, AI in interviewing streamlines the process by eliminating scheduling delays, structuring conversations, and capturing rich data. Interviewers can focus fully on candidates, knowing AI will record and summarize key points.

These efficiencies shorten the interview timeline, helping companies secure talent faster—often moving candidates through multiple interviews efficiently.