GROW: Using Artificial Intelligence to Screen Human Intelligence

Ethan  Bernstein; Paul  McKinnon; Paul  Yarabe

Case | HBS Case Collection | August 2017

GROW: Using Artificial Intelligence to Screen Human Intelligence

by Ethan Bernstein, Paul McKinnon and Paul Yarabe

Abstract

Over 10% of all 2017 university graduates in Japan used GROW, an artificial intelligence platform and mobile app developed by Tokyo-based people analytics startup IGS, to recruit for a job. This case puts participants in the shoes of IGS founder and CEO Masahiro Fukuhara, a first-time entrepreneur, as he considers the varied ways the "big data" he is collecting is being used--and whether some uses promised more meaningful (or less potentially misleading) impact than others.

After briefly introducing IGS, Fukuhara, and GROW, the case outlines exactly how GROW works, starting with a mobile app to assess competencies and personalities of candidates and ending with artificial intelligence (machine learning) to produce high-quality recommendations to companies about whom they should hire. The case then articulates precisely how three companies--airline ANA (All-Nippon Airways), global conglomerate Mitsubishi Corporation, and advertising/media company Septeni--use GROW in very different ways to manage talent recruiting, screening, hiring, placement, and development.

The case asks students to consider two questions: (1) Which of the three companies' approaches to using people analytics for talent acqusition and development is most appealing (or most concerning)?; and (2) Should Fukuhara turn on the most advanced part of the artificial intelligence engine, allowing GROW not just to provide recommendations to clients about whom they should hire, but also (based on performance and attribute data of previous hires) to overrule clients' specifications (or biases) about the competencies they should be targeting in their ideal hires?

Accompanying the case are the (anonymized) data one of these companies used to make their hiring decision, so that students can experience first-hand the opportunities and challenges of using people analytics in hiring. The case also provides an accessible yet thorough explanation of the key aspects of artificial intelligence (supervised, unsupervised, and reinforcement machine learning).

The case is well-suited to courses in Managing Human Capital, People Analytics, Talent Development, Organizational Behavior, or General Management.

Keywords: Talent and Talent Management; Recruitment; Selection and Staffing; Human Resources; Information Technology; Financial Services Industry; Air Transportation Industry; Advertising Industry; Manufacturing Industry; Technology Industry; Japan;

Language: English

Format: Print

12 pages

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Citation:

Bernstein, Ethan, Paul McKinnon, and Paul Yarabe. "GROW: Using Artificial Intelligence to Screen Human Intelligence." Harvard Business School Case 418-020, August 2017.

Each individual who enters an organization will, at some point, leave. And yet most future leaders spend significantly more effort learning about recruiting than departures, despite the sensitivity and challenges associated with the latter. This note is intended to help address that imbalance. This note provides a roadmap for managers and employees to follow when instituting or facing layoffs. It aims to help managers consider the broader implications that should be taken into account when conducting layoffs, including how to structure and execute them effectively; how to minimize the negative impact on laid-off employees, “surviving” employees, the firm’s reputation, and the community; and what alternatives any firm contemplating layoffs should consider.

From the outside, DISCO—a Japan-based manufacturer of precision tools for semiconductor production devices—appeared to be a rather ordinary company that had achieved rather extraordinary success: it had simultaneously achieved 70% global market share, had lifted its profitability from 15% to 30% over the past seven years, and was consistently selected as a "Best Workplace" in Japan. The secret to DISCO’s success, according to CEO Kazuma Sekiya, lies in its truly individualized management of human capital, which they called the P-Will (Personal Will) system.
P-Will was a proprietary managerial accounting system based on Will, a currency that enabled internal market transactions. At DISCO, every hour of labor and every good was associated with a price in Will. Employees were expected to act like independent business owners; they used the P-Will system to manage their own revenue contributions and business expenses in Will. Four times a year, the balances of individual P-Will accounts were converted to real currency and paid out as bonuses.
In the last couple of years, about 10 companies, including other companies in the semiconductor industry, had visited Sekiya wanting to replicate DISCO’s enviable success and introduce the P-Will system at their companies. However, none of these companies had actually implemented it. Seeing off another guest who had visited DISCO, Sekiya asked himself: Why don’t other companies adopt the P-Will system? What were the conditions under which P-Will would work—and not work?

Case | HBS Case Collection | October 2018

Safecast: Bootstrapping Human Capital to Big Data

Ethan Bernstein and Stephanie Marton

On March 11, 2011 at 2:46pm, a 9.1-on-the-Richter-scale, six-minute long earthquake unleashed a tsunami that ravaged the Tohoku region of Japan, damaging the Fukushima Daiichi Nuclear Power facility and releasing sufficient radioactive material into the air and ocean to make it one of only two “level 7” nuclear disasters in history (second only to Chernobyl).
But just how much radioactive material had escaped was not clear. A tense time was made worse by sporadic disclosures of fragmented information. Those who had power or cell service, mostly friends and family outside the region, were glued to their television and smartphone screens, but, by definition, no one could see the radiation they feared.
Frustrated by their own desires to know what should have been knowable, three technologists—Sean Bonner, Pieter Franken and Joi Ito—founded non-profit Safecast around a volunteer-centered, open, citizen-science, “crowdmapping” model to monitor radiation levels. With the help of thousands of volunteers, by 2018, Safecast had become not just the “go to” source of information on radiation issues in Japan and elsewhere, but also the vanguard example of citizen science.
Yet as Safecast’s dataset strengthened exponentially, the sustainability of its financial model weakened. The same open, crowd-based model that made the founder’s data collection sustainable was still financially unsustainable. Was there a business model that could sustain the organization financially without undermining their volunteer-based operations, and if so, what would it look like?

Keywords: citizen science; creative commons; Open Data; open architecture; volunteer-based organization; Fukushima Daiichi Nuclear Power Facility; 311; nuclear; radiation; crowdsourcing; bgeigie; geiger counters; kickstarter; sustainability; sustainable business and innovation; Design; Energy Generation; Social Entrepreneurship; Human Capital; Innovation and Invention; Crisis Management; Organizational Structure; Organizational Design; Technology; Business Model; Energy Industry; Technology Industry; Japan; North and Central America; Europe;

Citation:

Bernstein, Ethan, and Stephanie Marton. "Safecast: Bootstrapping Human Capital to Big Data." Harvard Business School Case 419-033, October 2018. View Details

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GROW: Using Artificial Intelligence to Screen Human Intelligence

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