Automated interviews save hours and deliver better data. But how does it work exactly and is it fair?
Door Ingmar van Maurik · Founder & CEO, Making Moves
What are AI pre-interviews?
An AI pre-interview is an automated interview where an AI system takes over the first screening conversation. The process works as follows:
AI asks role-specific questions based on the job profile and desired competencies
The candidate responds via text or video, at a time that suits them
AI analyzes the answers for content, structure, relevance, and competencies
A score, summary, and feedback are automatically generated for the recruiter
It's important to emphasize what an AI pre-interview is not: it doesn't replace the final conversation with a human. It replaces the first screening call — that 15-30 minute phone call recruiters conduct dozens of times per week, with predictable questions and a highly repetitive nature.
Why now?
The technology behind AI pre-interviews has made an enormous leap in the past two years. Thanks to improved Natural Language Processing (NLP) and Large Language Models, AI can now understand nuanced answers, interpret context, and even detect implicit signals. Where earlier systems only searched for keywords, modern systems understand the meaning behind the words.
The problem AI pre-interviews solve
Recruiters spend an average of 60-70% of their time on the initial screening process: reading CVs, phone screening, and conducting first interviews. For an average company making 100 hires per year, that's:
500-1,000 phone screenings per year
250-500 hours on screening calls
Inconsistent evaluations due to fatigue and unconscious bias
Weeks of waiting time for candidates before they speak to a recruiter
This is time recruiters could spend on what truly adds value: relationship building, employer branding, and strategic consultation with hiring managers.
How the analysis works
AI analyzes answers across multiple dimensions, much deeper than a human recruiter can in a first screening call:
Content analysis
Does the candidate actually answer the question? — many candidates give socially desirable but vague answers
Are concrete examples provided? — STAR method compliance
Is there relevant experience? — not just claims, but evidence and context
Are results mentioned? — impact and measurable outcomes
How deep is the knowledge? — surface-level or truly lived experience?
Structure analysis
Is the answer structured? — logical flow, clear argumentation
Is it concise or too lengthy? — communicative effectiveness
Is there a common thread? — coherence across different answers
Does the candidate stay on topic? — maintaining focus and relevance
Competency extraction
Which competencies emerge? — both explicit and implicit
How strong are the signals? — one example vs. consistent pattern
Are there gaps? — competencies that don't come up despite the questions
Self-awareness — how realistic is the candidate's self-reflection?
Motivation and cultural fit
Why this role and this company? — intrinsic vs. extrinsic motivation
What drives the candidate? — values and priorities
How does this fit the career path? — logic and ambition
Communication style — does it match the company culture?
The benefits in practice
For the organization
80% less time on initial screening calls — recruiters focus on the top 20% of candidates
Structured data instead of gut feeling — every candidate evaluated on the same criteria
Comparable scores for all candidates — objective ranking based on content
Available 24/7 — candidates complete the interview when it suits them, even evenings or weekends
Scalability — whether you screen 10 or 1,000 candidates per month, quality remains consistent
Faster time-to-hire — no more waiting on recruiter schedules
For candidates
No waiting time — start the pre-interview immediately after applying
Own pace — complete the interview when you're sharp, not when the recruiter has time
Fairer process — everyone gets the same questions and is evaluated on the same criteria
No travel time or stress — from your own environment, without the pressure of a live conversation
Direct feedback — many systems give candidates insight into their results
The numbers
Companies implementing AI pre-interviews report:
Metric
Traditional
With AI pre-interviews
|--------|------------|----------------------|
Screening time per candidate
30-45 min
5 min review
Candidate satisfaction
3.2/5
4.1/5
Time-to-shortlist
2-3 weeks
2-3 days
Screening consistency
40-60%
90%+
Shortlist diversity
Baseline
+30-40%
Is it fair?
This is rightfully the most important question organizations ask. The answer is nuanced: fairer than human screening, provided it's well-built and continuously monitored.
Why it's fairer than humans
AI pre-interviews evaluate content, not:
How someone looks — no halo effect from attractiveness
Accent or speaking style — no discrimination based on origin
Personal chemistry — no similarity bias
Time of day — no fatigue effects
Order of interviews — no contrast effects
The risks and how to mitigate them
But AI isn't automatically fair. The risks are real:
Training data bias — if the model is trained on historical data containing bias, it reproduces that bias. The solution: [validation for adverse impact](/artikelen/valid-reliable-assessment) and regular bias audits.
Language barriers — candidates for whom the language isn't their native tongue may score lower on language proficiency. The solution: score on content, not grammar.
Accessibility — not everyone is equally comfortable with technology. The solution: offer alternative options.
Transparency — candidates must know they're interacting with AI. The solution: clear communication upfront.
A good system has built-in fairness monitoring that continuously checks whether certain groups are systematically evaluated differently.
The technology behind it
A good AI pre-interview system requires multiple layers of advanced technology:
Natural Language Processing (NLP)
The core of the system: understanding human language in all its nuances. Modern NLP can:
Analyze sentiment and tone
Extract topics and themes
Assess answer quality across multiple dimensions
Detect implicit information — what isn't being said?
Competency frameworks per role
The system needs to know what it's looking for. This requires:
Role-specific competency profiles — what makes a good developer, sales manager, or finance analyst?
Weighted criteria — which competencies are critical, which are nice-to-have?
Adaptive questioning — follow-up questions based on previous answers
Scoring models trained on successful hires
This is where your own system makes the difference. Instead of generic models, you can:
Train models on your top performers — what made them successful?
Apply [continuous calibration](/artikelen/continuous-validation-hiring) after every hire
Measure and improve predictive validity over time
Recognize company-specific patterns that generic tools miss
Validation against actual performance
The ultimate proof: do pre-interview scores actually predict job performance? With your own system, you can measure this by correlating pre-interview scores with 6-month and 12-month performance reviews.
Integration into the hiring funnel
AI pre-interviews deliver the most value as part of an integrated funnel:
3. Pre-assessment — validated tests on cognition and personality
4. AI pre-interview — deeper screening on competencies and motivation
5. Human interview — only with the top 15-20% of candidates
6. Offer — based on complete data, not gut feeling
In this flow, the AI pre-interview completely replaces the traditional phone screening call while delivering better data.
Key takeaways
AI pre-interviews aren't the future — they're the present for forward-thinking organizations. The technology is mature, the benefits are proven, and the candidate experience is often better than traditional processes.
The keys to success:
Integrate it into a broader [AI hiring system](/ai-hiring-system), not as a standalone tool
Validate continuously against actual performance
Monitor actively for fairness and bias
Communicate transparently to candidates about the process
Want to discover how AI pre-interviews fit into your recruitment process? Schedule a conversation and we'll show you how it works.