GROW: AI-Powered Recruitment and Talent Screening Case Study

GROW: Using Artificial Intelligence to Screen Human Intelligence

This case study explores the application of artificial intelligence (AI) and people analytics in the recruitment process, focusing on the Japanese startup IGS and its AI platform, GROW. Developed by IGS, GROW is a mobile app and AI platform designed to screen job candidates by assessing their competencies and personalities. It then utilizes machine learning to provide recommendations to companies on potential hires.

The GROW Platform and its Functionality

GROW's process begins with a mobile application that gathers data on candidates' competencies and personalities. This data is then fed into an AI engine, specifically machine learning algorithms, to generate high-quality recommendations for companies seeking to hire new employees. The platform aims to streamline and improve the recruitment process by leveraging data-driven insights.

Case Studies of GROW's Implementation

The case study details how three different companies in Japan utilized GROW in distinct ways:

All Nippon Airways (ANA): An airline that uses GROW for talent recruiting, screening, hiring, placement, and development.
Mitsubishi Corporation: A global conglomerate that employs GROW for similar talent management functions.
Septeni: An advertising and media company that also integrates GROW into its talent acquisition and development strategies.

These examples highlight the versatility of GROW across different industries and organizational structures.

Key Questions and Ethical Considerations

The case study poses critical questions for participants to consider:

Comparative Appeal of Approaches: Which of the three companies' methods of using people analytics for talent acquisition and development is most appealing, or conversely, most concerning?
AI Overruling Client Specifications: Should IGS's founder, Masahiro Fukuhara, enable the most advanced feature of the AI engine? This feature would allow GROW to not only provide recommendations but also to override client specifications (or potential biases) regarding the desired competencies in ideal hires, based on data from previous successful hires.

These questions delve into the ethical implications of AI in hiring, particularly concerning bias, fairness, and the balance of power between AI recommendations and human judgment.

Data and AI Explanations

To provide a hands-on experience, the case includes anonymized data from one of the companies, allowing students to engage directly with the opportunities and challenges of people analytics in hiring. Furthermore, the case offers a clear and accessible explanation of key AI concepts, including:

Supervised Machine Learning: Learning from labeled data to make predictions.
Unsupervised Machine Learning: Finding patterns in unlabeled data.
Reinforcement Machine Learning: Learning through trial and error via rewards and penalties.

Target Audience and Course Suitability

The case study is well-suited for various academic and professional development courses, including:

Managing Human Capital
People Analytics
Talent Development
Organizational Behavior
General Management

Product Details

Product Number: 418020
Pages: 12
Publication Date: August 25, 2017
Source: Harvard Business School

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