Danone Empowers Campus Recruitment with AI Interviews, Saving HR Over 2,000 Hours per Hiring Campaign

What this page covers

• An overview of Danone’s use of AI interviews in campus recruitment
• Danone’s company profile and key facts about its global and China markets
• Key challenges in candidate management and assessment in Danone’s campus recruitment
• The AI interview system and solution design built by Moseeker for Danone
• Results of the AI interview project in efficiency, fairness, and candidate experience
• An overview of related cases from other companies such as Mars, Perfetti Van Melle, and Carlsberg

Danone Empowers Campus Recruitment with AI Interviews, Saving HR Over 2,000 Hours per Hiring Campaign

Danone uses Moseeker’s AI interview system to automate campus recruitment from screening to ranking, improving efficiency and fairness while significantly reducing HR hours.

• The AI interview system covers the full process of candidate screening, assessment, and ranking.
• A unified competency assessment model improves objectivity and fairness in evaluation.
• Automation reduces manual HR effort in initial screening and interview scheduling.
• In a single campus recruitment campaign, AI interviews can save HR over 2,000 working hours.

Employer profile

This section introduces Danone as a multinational food and beverage company, including its business areas, global and China market presence, and basic information such as factories and workforce size.

• Danone operates in the food and beverage business.
• Its business focuses on three major areas: Specialized Nutrition, Essential Dairy & Plant-Based products, and Waters & Beverages.
• Danone operates in more than 120 markets worldwide.
• Danone entered the China market in the late 1980s, and China is now its second-largest market globally.
• The company has 10 factories and about 8,000 employees in China, with a total global workforce of about 96,000.
• Danone’s China business contributed about 11% of its global sales in 2023.

Key challenges

This section explains the issues Danone faces in campus recruitment, including pressure from managing large candidate volumes, inconsistent evaluation standards, and the risk of cheating in online interviews.

• Manual initial screening for large-scale candidate pools is time-consuming, labor-intensive, and inefficient.
• The number of human interviewers is limited and dispersed, making it difficult to cover all candidates.
• Interviewers in different regions have inconsistent understandings of sales capability and willingness, leading to differences in evaluation standards.
• Centralized manual evaluation can introduce subjective bias, making it hard to maintain a unified nationwide hiring standard.
• In some online interview scenarios, candidates may use tools to cheat.
• Campus recruitment involves complex needs in candidate volume and geographic distribution that traditional approaches struggle to balance.

Solution

This section describes how the AI interview system jointly built by Moseeker and Danone integrates with the ATS, automates the process, builds a competency assessment model, and performs region-based dynamic ranking.

• The AI interview system integrates with Danone’s in-house ATS processes and data, enabling interviews to be initiated and managed within the existing system.
• The interview process runs fully automatically, allowing candidates to complete interviews independently 24/7.
• The system uses intelligent scheduling to automatically assign tasks, reducing manual operations.
• A customized competency assessment model was built based on Danone’s historical interview data and divided into five competency dimensions.
• Each question is scored from three aspects to form a multidimensional evaluation.
• All regions use the same algorithm but conduct separate dynamic rankings and generate visual performance reports.
• The system integrates seamlessly with the company’s ATS, enabling HR to complete screening, evaluation, and ranking on the existing platform.
• Moseeker quickly completed private deployment and launched the project.
• Even when interviewers define their own questions, the system can still deliver automated and personalized competency assessments.
• A unified evaluation model helps reduce human bias and mitigate cheating.

Project results

This section presents quantitative and qualitative outcomes of the AI interview system in screening efficiency, alignment with human evaluation, consistency, and candidate experience.

• The AI interview pass rate was set at 35%, helping Danone screen out about 65% of candidates.
• When used to eliminate candidates, AI evaluations match human evaluations at over 90%.
• When used to select top candidates, AI evaluations match human evaluations at over 85%.
• The project required AI-to-human evaluation alignment to exceed 80% as a baseline.
• The AI algorithm applies the same standards to all candidates to ensure consistency.
• Using unified standards and text semantic analysis, the system provides HR with clear evaluation rationales.
• About 60% of candidates provided positive feedback, indicating a good experience.
• About 20% of candidates said they would highly recommend this AI interview approach.
• Overall, the project improved campus recruitment efficiency and reduced HR workload.
• The customized private AI assessment model performed well, with evaluation quality close to human judgment.

More case overviews

This section uses visuals and brief descriptions to guide users through related cases on recruitment and AI interviews at Mars, Perfetti Van Melle, and Carlsberg.

• Mars hosts tens of thousands of candidates each year for campus recruitment, facing large-scale interview challenges.
• Moseeker partnered with Mars to build an automated and intelligent recruitment workflow to shorten cycles and reduce costs.
• Moseeker built a localized recruitment management system for Perfetti Van Melle using Moseeker ATS.
• The system deeply integrates the global platform, mainstream domestic recruitment channels, and Offer and Onboarding processes, while strengthening data compliance management.
• Moseeker built an automated AI interview system for Carlsberg, supporting immediate interview creation and invitation sending after application submission.
• The Carlsberg solution uses general and dedicated competency models to conduct multidimensional AI assessments of candidates.

Fact index