目錄
McKinsey & Company is piloting a new type of AI interview in which candidates spend approximately 10 minutes speaking with a virtual AI client while a human interviewer observes their performance. The assessment does not require candidates to demonstrate their ability to use AI tools, nor does AI determine their scores. Instead, it uses scenarios resembling real consulting work to assess whether candidates can understand client needs, respond to differing opinions, and reassess their original recommendations when new information emerges.
In October 2026, The Wall Street Journal reported on the recruitment pilot, followed by coverage from international technology publications including The Verge. Unlike previous AI recruitment methods that screened résumés or asked standardized questions, McKinsey's new approach has AI directly assume the role of a client with whom candidates must communicate. This expands the focus of the assessment to include judgment and adaptability during real-time conversations. For job seekers preparing for consulting interviews, business analyst positions, or recruitment processes at multinational companies, mastering Case Interview techniques is no longer the only area worth practicing. They also need to develop their ability to discuss problems with clients under time constraints.
What Is McKinsey's AI Interview? The 2026 Recruitment Pilot, Process, and Eligible Candidates
Initially Tested at Selected North American Offices, Not Required for All Candidates
According to reports published in October 2026, McKinsey is testing AI virtual client interviews at selected offices in North America, primarily with university students applying for entry-level Business Analyst positions. Candidates interact with an AI character on a laptop for approximately 10 minutes, while a human interviewer observes the conversation to understand how they react and reason when responding to client questions.
The system remains a limited recruitment experiment, with potentially several hundred candidates expected to participate. McKinsey has indicated that the assessment could eventually expand to other positions if the pilot produces the expected results. However, as of October 22, 2026, no official timetable for a company-wide rollout has been announced, and there is no evidence that candidates in all regions are required to participate.
| Item | Publicly Reported Details of the 2026 Pilot |
| Pilot period | Fall 2026 |
| Locations | Selected North American offices |
| Primary candidates | University students applying for entry-level Business Analyst positions |
| Assessment duration | Approximately 10 minutes |
| Interaction format | Conversation with a virtual AI client using a laptop |
| AI's role | Simulating client questions, concerns, and conversational responses |
| Evaluation method | Human interviewers observe and assess candidates |
| Does AI assign scores directly? | No |
| Future plans | Possible expansion to other positions in 2027, depending on pilot results |
The new assessment also does not mean that McKinsey's existing interview process has been replaced. According to its official careers website, consulting positions may still involve personal experience interviews, problem-solving interviews, and the gamified Solve assessment. The specific stages vary by position, so candidates should follow the instructions provided by their recruiters.
A Virtual AI Mayor Raises Concerns to Observe How Candidates Respond
One of the most representative scenarios in the pilot involves AI playing the mayor of a fictional city and discussing where a new stadium should be built. The mayor may ask about the advantages and disadvantages of different locations or raise concerns about transportation, budgets, and urban development, requiring candidates to explain the reasoning behind their recommendations.
Scenarios like this do not necessarily need to have a single correct location predetermined from the outset. Suppose a candidate recommends building the stadium downtown because of convenient transportation and concentrated commercial activity. The mayor then mentions that residents are concerned large events could worsen traffic congestion. The candidate would need to reconsider the original recommendation. Continuing to emphasize the economic benefits of a downtown location without addressing traffic concerns could mean overlooking a constraint that genuinely matters to the client, even if the analytical framework appears comprehensive.
Saleem Zafar, a McKinsey partner involved in designing the assessment, explained to the media that the virtual client was not intentionally designed to be an exceptionally difficult character. The assessment aims to reproduce communication situations that consultants may encounter in their everyday work. Human interviewers observe whether candidates recognize important information raised by the client and whether their subsequent answers genuinely reflect that information, rather than simply following a predetermined analytical framework.
Reports also indicate that the simulation system was designed with the goal of representing approximately 80% of everyday consulting interactions. This figure describes McKinsey's intended scope for the system. It should not be interpreted as an 80% AI scoring accuracy rate, nor does it represent a passing threshold. No publicly available independent research has yet confirmed whether this level of scenario coverage has actually been achieved.
AI Plays the Client but Does Not Determine Interview Scores
AI recruitment is often associated with systems that analyze résumés, score candidates' responses, or even automatically determine which applicants advance to the next stage. McKinsey's virtual client assessment uses a different arrangement. AI provides the interactive scenario, while a human interviewer observes and evaluates the candidate's performance. The AI model itself does not assign scores.
This distinction matters when interpreting the assessment as a whole. When AI plays the client, its follow-up questions, the order in which it reveals information, and its conversational responses can still influence how the assessment unfolds. However, responsibility for the final evaluation remains with the human interviewer. Even without automated AI scoring, companies must therefore verify that the virtual character behaves consistently and that different candidates receive reasonable, comparable opportunities to demonstrate their abilities.
Separately, McKinsey disclosed another AI-assisted recruitment mechanism in its AI-Assisted Interview Privacy Notice, which took effect on August 1, 2026. Under that mechanism, an AI chatbot asks candidates about their years of work experience, language abilities, location, and eligibility requirements. It then generates an eligibility assessment based on predefined criteria for review by a human recruiter.
These two mechanisms should not be confused. The first is a virtual client simulation observed by a human interviewer, while the second is an AI recruitment chatbot that may generate eligibility assessments. Although both use AI and retain human involvement, their actual purposes and the role AI plays in the evaluation process are different.
How Does McKinsey's AI Interview Differ from a Traditional Case Interview?
Traditional Case Interviews Emphasize Analysis, While AI Simulations Add Real-Time Client Interaction
McKinsey has long used the Problem-Solving Interview, commonly known as a Case Interview, to assess candidates. In these interviews, an interviewer presents a business scenario, such as whether a company should enter a new market, how to improve operational performance, or whether a particular investment is worthwhile. Candidates must clarify the problem, analyze information, and develop recommendations step by step.
The AI virtual client interview retains the business scenario format but changes the interaction partner from a conventional interviewer to a simulated client. In addition to developing solutions, candidates must pay attention to the other party's level of understanding, priorities, and immediate reactions. When a client suddenly introduces a new constraint, the original analytical approach may no longer be appropriate. How the candidate continues the conversation then becomes an observable behavior.
| Comparison | Traditional Case Interview | AI Simulated Client Interview |
| Interaction partner | Human interviewer | Virtual AI client |
| Primary scenario | Analyzing business problems and proposing solutions | Discussing problems and recommendations with a simulated client |
| Main areas of observation | Logical analysis, problem-solving, and communication | Understanding client needs, real-time responses, and adjusting judgments |
| How information is obtained | Through the case scenario and interviewer guidance | Through conversations with the virtual client |
| Evaluator | Human interviewer | Human interviewer |
| Preparation focus | Case analysis, hypothesis development, and data interpretation | Case analysis combined with dynamic client communication |
The capabilities required by these two interview formats overlap considerably. Traditional Case Interviews also assess whether candidates understand the problem and can explain their reasoning, while AI simulated client interviews cannot entirely exclude analytical ability. The difference is that the new format makes client interaction a more direct subject of observation.
Why Do Companies Want to Use AI to Assess Real-World Job Skills?
Traditional Case Interviews already have a well-established range of preparation resources. Candidates can repeatedly practice market sizing, profitability analysis, cost breakdowns, and other common question types. These exercises help develop analytical skills. However, as response patterns become increasingly standardized, interviewers may find it harder to distinguish between candidates who genuinely understand a problem and those who have become proficient at reproducing practiced answers.
Real consulting work rarely follows such a fixed sequence. A client might initially present a vague problem, add budget and time constraints later, or even change the original objective halfway through a meeting. Consultants must continuously verify information and, when necessary, explain to clients why new conditions affect their initial recommendations.
From a talent assessment perspective, this type of simulation resembles a Work Sample Assessment, in which candidates perform tasks similar to actual job responsibilities so that relevant capabilities can be observed. Research materials from the U.S. Office of Personnel Management (OPM) indicate that properly designed work samples and structured interviews can provide information relevant to job performance. Companies therefore want to understand not only whether candidates can give correct answers but also how they handle realistic situations.
However, a simulation that appears realistic does not automatically have strong predictive validity. Companies still need to validate, using data, the extent to which a candidate's performance during a 10-minute virtual conversation reflects their future job capabilities. Consulting work also involves research, data analysis, teamwork, and long-term project execution, making it difficult for a single short conversation to cover every requirement.
What Skills Does McKinsey's AI Interview Primarily Assess?
Based on publicly available simulation scenarios and explanations from McKinsey partners, the assessment primarily focuses on whether candidates can understand client needs, process new information, and clearly communicate the reasoning behind their judgments. Because McKinsey has not released a complete official scoring rubric, the following preparation areas are derived from public information and the characteristics of consulting work. They should not be treated as formal evaluation criteria.
Clarifying Requirements: Confirm the Client's Decision-Making Objectives Before Recommending a Solution
Suppose the virtual mayor asks, "The city government is considering building a new stadium. Should we choose a downtown location or the suburbs?" Although this appears to be a choice between two options, it is difficult to determine which location is more suitable without additional information. The city government might want to increase tourism revenue, or it might place greater importance on providing public facilities for residents. Different objectives naturally lead to different priorities when selecting a location.
A reasonable approach is to clarify the decision-making objective first, then ask about constraints such as budget, land use, transportation, and expected attendance. The goal is to identify which information is most likely to influence the final decision. This does not mean candidates must investigate every detail before beginning their analysis. Rather, they need to recognize which questions deserve priority so that limited interview time is not spent gathering information with little relevance to the decision.
Similar situations frequently arise in client communication, particularly when clients have not yet fully defined their requirements. The way questions are framed can directly affect the subsequent discussion. If a candidate immediately applies a familiar analytical framework without confirming what the client actually wants to solve, the final recommendation may miss the client's needs even if the reasoning contains no obvious errors.
Managing Hypotheses: Willingness to Revise Initial Judgments When New Information Emerges
Business analysts rarely have complete data at the outset. They must therefore develop hypotheses based on known conditions and progressively revise them as additional information becomes available. An AI simulated client can introduce constraints or raise objections during the conversation, allowing interviewers to observe how candidates address factors they had not initially considered.
For example, a candidate might initially favor building a stadium downtown because of its convenient public transportation and concentrated commercial activity. The mayor then explains that the transportation department has determined the existing roads cannot accommodate traffic volumes during major events. The candidate would need to investigate the severity of the transportation problem and whether shuttle services, public transportation, or road improvements could address it. If the cost of improvements exceeds the budget, the suburban option should be reconsidered rather than continuing to defend the original answer.
This type of adjustment demonstrates the ability to manage analytical assumptions. An initial recommendation may be reasonable under certain conditions, but when those conditions change, the conclusion should be reassessed. In real-world projects, clients rarely provide all relevant information at once. Recognizing which assumptions are no longer valid can affect the quality of subsequent decisions.
Communicating Decisions: Helping Clients Understand Costs and Trade-Offs
When presenting recommendations, candidates need to explain not only which option is preferable but also what must be sacrificed to pursue it. A downtown location might offer better transportation access and commercial development opportunities but involve higher land costs. A suburban location might provide more space for development while requiring additional investment in transportation infrastructure. Both alternatives have advantages and disadvantages, and the choice ultimately depends on the client's priorities.
If an answer emphasizes only the advantages of one option, clients may struggle to understand the recommendation or identify which conditions could change the final decision. A more complete approach is to explain what information is currently available, which assumptions underpin the recommendation, and what questions still need to be resolved.
For example, a candidate could explain to the mayor: "If the city government prioritizes transportation accessibility and surrounding commercial development, downtown currently has an advantage. However, we still need to determine the cost of road improvements. If transportation infrastructure costs exceed the budget, a suburban location may be more consistent with the city's financial constraints."
This response neither pretends that all information has been verified nor avoids making a recommendation simply because uncertainty remains. In consulting, reasonable recommendations generally need to present both the supporting rationale and the limitations, helping clients understand which information should be verified next before moving the decision forward.
Are AI Simulated Interviews Necessarily Fairer? Standardization and Recruitment Bias Still Require Examination
McKinsey hopes to improve consistency across certain interview scenarios by using AI virtual clients. This approach has practical implications worth examining. When human interviewers previously played clients, their questioning styles could differ. Some might frequently challenge candidates, while others might be more willing to provide hints. Even when using the same case, candidates could face different levels of difficulty.
With AI, companies can predefine a character's background, scenario objectives, and scope of interaction, potentially reducing some human variation. However, generative AI responses depend on the conversation itself, so every interaction is not guaranteed to be identical. Companies must therefore examine whether virtual clients provide scenarios of comparable difficulty and whether different candidates have reasonable opportunities to demonstrate the relevant skills.
Human Scoring Does Not Automatically Guarantee Fair Interview Outcomes
Even when AI does not directly score candidates, human interviewers may still be influenced by subjective impressions. For example, two interviewers might interpret the same behavior differently when a candidate clarifies constraints before answering. One might consider it evidence of careful judgment, while another might regard it as a slow response. Without clear scoring criteria, using identical simulation scenarios cannot fully eliminate such differences.
Research on structured interviews from the U.S. Office of Personnel Management indicates that developing evaluation criteria through job analysis and standardizing questions and scoring rubrics can improve consistency among evaluators. For AI simulated interviews, companies must therefore examine not only whether the system is stable but also whether human interviewers evaluate observable behaviors instead of relying too heavily on overall impressions.
Furthermore, short, real-time conversations may make it easier for people who think quickly and communicate fluently to demonstrate their strengths. Whether these characteristics adequately represent overall consulting capabilities still requires validation through research on assessment validity. Someone who excels at extended research and analysis may not necessarily perform exceptionally well during a brief conversation. If companies hope to improve talent selection through simulated interviews, they need to understand what new information the assessment provides and whether it disproportionately increases the weight assigned to certain abilities.
Language Background, Disabilities, and Data Privacy Must Also Be Considered
As interviews become more dependent on AI-powered voice interactions, the system's ability to recognize different accents, speaking speeds, and communication styles will also influence the user experience. Non-native speakers and some candidates with disabilities may face unnecessary communication barriers if the model cannot accurately understand their responses.
The U.S. Equal Employment Opportunity Commission (EEOC) has previously emphasized that employers using AI recruitment tools must still comply with applicable employment anti-discrimination laws and provide reasonable accommodations when required. This does not mean that McKinsey's system has already encountered such problems. Rather, companies should verify whether candidates from different backgrounds face unreasonable technical limitations before expanding deployment.
Data governance is another area requiring clarification. In its separate privacy notice concerning AI-assisted recruitment, McKinsey explained that its chatbot may process conversation transcripts, AI-generated summaries, eligibility assessments, and related audio data. However, that notice describes an AI eligibility assessment mechanism. It cannot be assumed that the 10-minute virtual client assessment uses exactly the same data processing procedures. Candidates should therefore confirm whether conversations will be recorded, how their data will be used, and which privacy policies apply before formally participating.
The AI Risk Management Framework developed by the U.S. National Institute of Standards and Technology (NIST) also emphasizes the importance of considering validity, reliability, transparency, accountability, fairness, and privacy when evaluating AI systems. Applied to recruitment, the real question is whether the assessment system as a whole is reasonable, rather than simply whether the AI model can carry out a conversation successfully.
How to Prepare for McKinsey's AI Interview: Practice Real-Time Communication with Simulated Clients
Preparation for this type of assessment can build on traditional Case Interview training, but it should include additional practice interacting with clients. Rather than asking AI to generate a complete model answer, candidates can have it play a client who provides incomplete information and asks follow-up questions. This allows them to practice clarifying requirements, developing recommendations, and adjusting their judgments when new conditions arise, all within a limited timeframe.
This approach does not require a specialized interview platform. General-purpose conversational AI tools can be used to create realistic practice scenarios. Candidates who already use Notion to organize work materials can also record each practice case, the main problems encountered, and areas for improvement, making it easier to compare their performance over time.
Create Case Scenarios and Have AI Reveal Client Requirements Gradually
Practice cases can begin with common business problems such as urban development, product pricing, customer churn, or supply chain delays. Start with a simple background scenario, then have AI provide information gradually as the conversation progresses. Candidates do not need to know all the conditions beforehand or prepare a fixed answer for every case. Doing so could turn the exercise into another form of memorization.
For example, a virtual mayor could be evaluating potential locations for a new stadium. Initially, the AI provides only two possible sites, leaving the candidate to clarify the decision-making objectives and constraints independently. If the candidate asks about the budget, AI provides the financial conditions. If transportation is discussed, it introduces findings from the transportation department. This allows the conversation to develop according to the questions actually raised.
The following prompt can be used directly for practice:
Act as the mayor of a fictional city evaluating potential locations for a new stadium. Conduct a simulated client meeting with someone applying for a business consulting position. Reveal only part of the information at a time, and ask reasonable follow-up questions based on the candidate's responses. Do not disclose all the conditions at once. During the discussion, introduce new information about budgets, transportation, residents' opinions, or other factors that could influence the decision when appropriate. If the candidate fails to address an important constraint, raise concerns from the client's perspective, but do not directly provide the optimal answer. This is a 10-minute practice session. After the session, provide specific feedback on requirements clarification, hypothesis management, real-time responses, and decision-making explanations.
This is an independently designed practice scenario. It does not represent McKinsey's official question bank, nor does it mean the actual assessment will use the same follow-up questions. If the AI tool cannot reliably manage the conversation's duration, candidates can set a separate timer rather than relying too heavily on the model to determine when the session has ended.
How Should You Allocate Time During a 10-Minute Practice Session?
In a time-limited simulated interview, candidates should avoid two common problems: rushing to provide an answer immediately or spending so much time clarifying details that they never present a concrete recommendation.
During practice, candidates can spend approximately two minutes clarifying the client's decision-making objectives and main constraints. They can then organize the important information and compare possible options before using the latter half of the session to present an initial recommendation and address client concerns. As the session approaches its end, they should explain which judgments currently have reasonable supporting evidence and which conditions still require further verification.
This arrangement is simply a suggested time-management approach for practice, not an official response procedure prescribed by McKinsey. During actual client conversations, the discussion may change direction at any point. If new constraints emerge earlier than expected, candidates must adjust their analytical approach accordingly.
Rather than strictly controlling the number of seconds spent on each stage, it is more valuable to practice advancing the discussion far enough to make a preliminary recommendation despite time limitations. Even when complete information is unavailable, candidates can clearly communicate their current judgment and identify the assumptions that most urgently need to be tested.
Use Notion to Record Practice Issues Instead of Relying Only on AI Scores
After each practice session, AI can generally provide evaluation feedback, but its scores do not necessarily have an objective basis for comparison. Even when two simulations use identical prompts, their difficulty levels and follow-up questions may differ. Therefore, numerical ratings generated by AI should not automatically be treated as quantitative measures of actual interview ability.
A more useful approach is to record specific problems that occurred during the session. For example, did the candidate confirm the client's decision-making objectives before making a recommendation? Did they overlook important constraints? Did they reconsider their assumptions when new information emerged? Did they explain the trade-offs between different options at the end?
When creating a practice database in Notion, candidates can use the case topic, practice date, main problems, and adjustments for the next session as basic properties. Recording only the behaviors most in need of improvement after each session helps avoid spending excessive time completing unnecessarily complicated scoring sheets.
Suppose a practice session reveals that a candidate quickly developed a comprehensive stadium location recommendation but failed to confirm whether the city's budget included transportation improvement costs. The next session could focus on how to clarify financial constraints. Such records provide clearer direction for subsequent preparation and make it easier to identify recurring problems.
Include Human Mock Interviews to Avoid Becoming Overly Accustomed to AI Responses
AI can repeatedly simulate different clients, but its judgments about response quality may not always align with those of human evaluators. Some models may favor answers with clear structures and complete wording without necessarily assessing how natural the conversation feels. They may also fail to recognize when an explanation is logically sound but does not actually address what the client wants to know.
For this reason, human observation remains valuable during preparation. Candidates can ask friends, colleagues, or mentors familiar with business cases to play the client and assess whether their recommendations are easy to understand, their responses address the questions, and they overlook any concerns raised during the conversation.
If AI and a human evaluator provide different assessments of the same response, it is better to review the actual conversation than simply compare which evaluator gave the higher score. The purpose of practice is to improve observable communication behaviors. There is no need to turn every simulation into a competition for points.
Can You Use ChatGPT During McKinsey's Official Interview? Preparation and Assessment Rules Are Different
McKinsey's official careers website clearly distinguishes between using AI to prepare before an interview and using AI during a formal assessment. Candidates may use ChatGPT or other generative AI tools to improve how they present their experience on a résumé, practice interview questions, or understand business analysis concepts. However, they must not use AI to fabricate personal experiences, nor may they use unauthorized tools to generate answers in real time during an official interview.
The same rules apply to AI virtual client interviews. Even though McKinsey provides an AI character as part of the assessment, this does not mean candidates may separately open a generative AI tool to analyze the client's problem or generate responses in real time. Unless the recruitment team explicitly permits it, candidates must continue to comply with the general restrictions on tool usage during formal interviews.
In its Assessment Integrity Expectations, McKinsey also states that candidates must not record interviews, take screenshots, or use unauthorized applications, websites, or AI tools without permission. Formal assessments generally require candidates to use a single screen, refrain from receiving assistance from other people or tools, and follow the interviewer's instructions regarding note-taking and equipment use. Candidates who require reasonable accommodations due to a disability should confirm the arrangements with their recruitment contact in advance.
Remote candidates should also be aware that some meeting applications, browser extensions, or desktop programs may have AI transcription, meeting summarization, or automatic recording features enabled by default. Before an official interview, it is advisable to review these settings to avoid unintentionally violating recruitment rules.
For everyday AI-assisted practice, candidates should use fictional cases or publicly available information instead of uploading internal documents from their current employer, actual client data, or unauthorized business information. Simulated interviews require sufficiently realistic work scenarios, but practicing requirements clarification and decision-making communication does not require access to genuinely confidential information.
Will AI Simulated Interviews Become a New Direction for Corporate Recruitment?
McKinsey's pilot introduces another potential role for AI in recruitment. Previously, companies more commonly used AI for résumé screening, eligibility verification, and organizing interview data. Now, they are beginning to experiment with AI as an interactive participant in simulated work situations, allowing human evaluators to observe how candidates complete tasks.
This approach has some appeal for positions involving frequent client interaction. Through similar simulated scenarios, companies can observe how candidates understand requirements, handle differing opinions, and explain decisions to stakeholders. However, whether AI can genuinely improve assessment quality remains to be established. Beyond evaluating whether a model can converse naturally, companies must determine whether different candidates face comparable scenarios, whether human scoring is consistent, and whether assessment results effectively reflect actual job capabilities.
As of October 22, 2026, McKinsey had not published comprehensive evaluation data sufficient to assess the pilot's effectiveness. It would therefore be premature to conclude that AI simulated client interviews will completely replace traditional Case Interviews. It is also unclear whether other companies will adopt the same approach.
For job seekers preparing for McKinsey's AI interview, the current priority should remain traditional preparation for case analysis, problem-solving, and personal experience interviews, supplemented by timed client conversation exercises. When facing unfamiliar situations, candidates should first clarify the problem that genuinely needs to be solved, establish reasonable hypotheses, reconsider their judgments when new constraints emerge, and explain the rationale and trade-offs behind their recommendations. These are also communication and decision-making skills frequently required in real consulting work.
FAQs
McKinsey began piloting an approximately 10-minute AI virtual client conversation in fall 2026. AI plays the client while a human interviewer observes the candidate's performance. The assessment primarily examines how candidates clarify requirements, address concerns, and adjust their judgments.
No. Public reports indicate that the pilot is limited to entry-level business analyst recruitment at selected North American offices. It is not yet a standard requirement for every position or region worldwide.
In this virtual client assessment, AI simulates client interactions but does not directly assign scores. A human interviewer is responsible for observing and evaluating candidates. This differs from the separate AI recruitment chatbot used for eligibility assessments.
No. The 80% figure describes the scope of everyday consulting scenarios that McKinsey intends its virtual client system to cover. It does not represent scoring accuracy, a pass rate, or a passing threshold. The level of scenario coverage has also not been independently verified through publicly available research.
Traditional Case Interviews generally involve a human interviewer guiding the analysis of a business problem. AI simulated client interviews allow candidates to engage in real-time conversations with virtual stakeholders, providing a more direct way to observe how they listen to client requirements, respond to new information, and explain trade-offs. Both formats emphasize analytical and communication skills.
Use fictional business cases to practice timed client conversations. Focus on confirming decision-making objectives, asking about constraints, checking assumptions, comparing options, and recommending next steps. After each session, record specific problems and supplement AI practice with feedback from human mock interviews.
Generally, no. McKinsey permits AI-assisted preparation before interviews but prohibits unauthorized generative AI or real-time answer-generation tools during formal assessments. Candidates must follow the instructions provided for their recruitment process and by their interviewers.
Yes. You can create a practice database containing case topics, dates, requirements clarification, hypothesis management, responses to questions, and adjustments for the next session. Fictional or publicly available cases are recommended. Do not upload confidential client or employer information without authorization.
Not necessarily. AI may reduce some differences in role-playing scenarios, but companies still need to examine scenario difficulty, speech recognition, the experiences of candidates from different backgrounds, consistency in human scoring, and assessment validity before determining whether the process is fairer.
Publicly available information is currently insufficient to confirm the complete recording and retention rules for this 10-minute pilot. McKinsey has a separate privacy notice covering AI-assisted recruitment, but its provisions cannot automatically be applied to the virtual client assessment. Candidates should refer to the privacy information provided for their specific recruitment process.