📌 Key facts
- What: This thesis examines how AI use in recruiting relates to efficiency gains from the company perspective. The thesis can be conducted as a structured literature review or as an experimental study, focusing on outcomes such as time-to-first-touch, stage cycle times, time-to-decision, recruiter workload, and recruiting process quality.
- When: Start anytime soon. Applications are open!
- How to apply: Send your CV, transcript of records, and max. 5 sentences why this topic interests you (more details below)
- 📌 Key facts
- 💡 Background
- 🦾Who We Are
- 🎯 Topics of Interest and Potential Outcomes
- ossible research questions
- Possible theoretical perspectives
- Possible method
- 🎓 Profile
- 📝 How to Apply
💡 Background
Artificial intelligence is increasingly used in recruiting to support or automate parts of the hiring process, such as screening, candidate ranking, interview scheduling, communication, or decision support. Organizations often expect AI-based recruiting tools to make hiring faster, more scalable, and more consistent. However, it remains unclear under which conditions AI actually improves recruiting efficiency and how such efficiency gains should be measured.
This thesis examines the company-side perspective on AI in recruiting. The goal is to develop a theoretically grounded and practically useful overview of how AI can affect recruiting efficiency, where in the recruiting funnel such effects are most likely to occur, and which organizational conditions shape whether AI creates real process improvements.
🦾Who We Are
I am a PhD student and Senior Consultant at McKinsey & Company. My research interest lies broadly in the use of AI, including its adoption, effective use, and its impact on companies. I have an educational background in Management
🎯 Topics of Interest and Potential Outcomes
ossible research questions
The thesis may address questions such as:
- How can AI improve recruiting efficiency across different stages of the hiring process?
- Which recruiting stages are most likely to benefit from AI support?
- How should efficiency gains in AI-supported recruiting be measured?
- Under which organizational or technological conditions does AI improve recruiting efficiency?
- What are the potential trade-offs between recruiting speed, decision quality, and human oversight?
Possible theoretical perspectives
Depending on the student’s interest, the thesis may draw on theories and frameworks such as:
- AI-based work augmentation
- task–technology fit
- sociotechnical systems theory
- process automation and organizational efficiency
- human–AI collaboration in decision-making
- technology implementation in HRM
Possible method
The thesis can be conducted as either a structured literature review / conceptual thesis or, depending on the student’s interest and methodological fit, as an experimental study.
In the literature-based version, the student would systematically review and synthesize academic research on AI-supported recruiting, with a particular focus on company-side outcomes such as efficiency, process speed, recruiter workload, decision quality, and recruiting funnel performance. The goal would be to develop a conceptual framework explaining when and how AI can create efficiency gains in recruiting.
Possible outputs include:
- a structured overview of the current literature on AI in recruiting from a company-side perspective
- a conceptual framework of AI-enabled recruiting efficiency
- a mapping of recruiting stages and relevant efficiency outcomes
- a synthesis of measurable indicators, such as time-to-first-touch, stage cycle times, time-to-decision, time-to-hire, recruiter workload, and process throughput
- practical implications for organizations implementing AI-supported recruiting tools
Alternatively, the thesis may include an online experiment, for example with recruiters, HR professionals, managers, or suitable proxy participants. In such an experiment, participants could evaluate different recruiting process scenarios, such as a traditional human-led process versus an AI-supported process. The experiment could test how AI use affects perceived efficiency, perceived decision quality, trust in the process, perceived risks, or willingness to implement AI-supported recruiting.
Possible experimental manipulations include:
- traditional recruiting process vs. AI-supported recruiting process
- AI support in early screening vs. later selection stages
- low vs. high human oversight
- low vs. high transparency about AI use
- efficiency-focused vs. quality-focused framing of AI
This experimental option would allow the student to complement the literature review with empirical evidence on how organizational decision-makers perceive the efficiency potential and risks of AI in recruiting.
🎓 Profile
- Reliable, structured, and self-driven working style
- Strong academic record and analytical mindset
📝 How to Apply
If you are interested, please contact @Maximilian Rink by submitting your CV, grade report, preferred starting date & short motivation statement (max. 5 sentences) Please also indicate which kind of thesis (= outcome) you are interested in.