OL 620 Module 2: Analyzing AI in Human Resources Management

This report analyzes the integration of Artificial Intelligence (AI) within Human Resources (HR), focusing on an article discussing the use of AI in HR. The student examines how AI is impacting recruitment, data analysis, and overall workforce management. The report compares the strategies presented in the article with the HR practices in the student's workplace, highlighting the use of AI for data sorting and analysis, while maintaining human oversight in key decision-making processes. The author, acting as an HR leader, suggests that AI can improve data management and suggest recruitment patterns but emphasizes the importance of human judgment, experience, and qualitative skills in decision-making. The report concludes by acknowledging the growing trend of AI adoption in HR, citing examples like DBS Bank's Jobs Intelligence Maestro (JIM) and suggesting a balanced approach to AI implementation in HR to optimize efficiency and maintain a human-centric approach. The report uses the course materials and critical thinking skills to make a reflective analysis of the current issues in strategic human resources.

Running Head: BUSINESS MANAGEMENT
Business Management
Name of Student:
Name of University:
Author’s Note:

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Artificial intelligence is the one of the fastest growing technological advancements in
today’s world and is increasingly playing a bigger role in several industries across the world,
replacing human workforce with far more efficient and precise results. In the field of Human
(HR) Resources as well, Artificial Intelligence (AI) has made its mark as a growing number of
leaders are considering an optimized combination of human and digital efforts for managing
the workforce of their organisations (Bhardwaj, 2019). In this essay, I will try to analyse the
article “Can Artificial Intelligence Help Answer HR’s Toughest Questions?” by Peter
Cappelli and Prasanna Tambe.
According to the article, the incorporation of AI involves the use of certain algorithms,
which generally look back at the pre-installed data from the past. However, following such an
algorithm may not always work out as the best hiring strategy as trends and systems are
changing at a rapid pace. The task of human resources often deals with questions regarding
fairness, which is essentially a morally subjective concept, and fairness does not always
comply well with optimization and machine learning (Cappelli & Tambe, 2019). With the
introduction of AI in the scope of human resources, companies are seeing a growing influx of
data analysts in HR (Nicastro, 2018). Data scientists have always faced difficulty in gathering
data on employee performance and hiring and analyzing it, therefore the ability to
successfully incorporate that into AI is another hurdle.
Another important discussion in this article is in reference to the difference between
the traditional HR practices and the new methods developed by AI. In the old days, candidates
had to undergo a number of rounds like IQ test, aptitude test, personal interviews, etc. They
were marked according to different criterion. The hiring manager would consider the different
scores and weigh them based on his own scores and discretion and make a judgment (Cappelli
& Tambe, 2019). The modern digital systems however take into account such scores, along

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with a number of other factors. They then align this information in a pre-defined model or
algorithm and generate one particular score to determine if the candidate should be rejected or
accepted (Mathur, 2019). While this may be more efficient, easier or faster in nature, I would
strongly argue that it does not lay emphasis on the individual key driving factor(s) that
determines the choice of the candidate. In simpler words, AI cannot tell you the main reason
that distinguished that candidate from the others, and this is where human experience cannot
be substituted.
In the organisation where I work, human resource management is handled by
experienced HR executives who analyse work performance and applicant data to determine
future results and decisions. The data is fed to a software system that segregates and sorts the
data into systematic categories, which greatly assists executives to look into the various
aspects separately while considering applicant experience, achievements, abilities, knowledge
and the like. An artificial chat-bot is available to applicants online which answers queries
regarding the company, its policies and job profile. However, the decision of selection is still
very much under the discretion of the HR executives as the company policy believes in a
‘human’ experience when it comes to handling core matters of human relations. While
machines are capable of making no errors, the human subjectivity and rationality are more
important when it comes to taking decisions.
Personally, I believe that artificial intelligence has a great scope of improvement and
growth in the realm of modern data analysis and handling. However, areas such as human
resource management require a sense of imperative judgment, analysis of qualitative skills
and attention to certain determining factors that matter more than others. These tasks require a
more human approach and despite the efficiency, speed and accuracy of digital systems,
should be best left to experienced human consultants. As a human resource leader, I would opt

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to reserve the decision of recruitment to the most senior experienced staff of my department,
as I believe that machine learning cannot provide the judgment that can be given by the years
of experience gathered from handling various personnel and identifying talent. But, I do not
entirely rule out the role of AI in the field of HR. Artificial systems can be used to improve
the issues of data sorting, storage and analysis, and also for suggestive recruitment patterns,
which can be generated from the data (Wislow, 2017).
It cannot be denied however, that artificial intelligence is a growing trend that is being
adopted steadily by several organisations around the world. One of the most prominent
examples is the virtual recruitment system Jobs Intelligence Maestro (JIM) developed by the
talent acquisition team of DBS Bank (Efma, 2019). JIM is renowned for reduction in
applicant screening time, better job application completion rate and response to candidate
queries. Success stories like JIM serves as boost for leaders and HR executives worldwide for
considering and incorporating AI systems and optimization.

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References:
Bhardwaj, R. (2019). How AI is Revolutionizing the Human Resource Functions. Retrieved
31 August 2019, from https://www.entrepreneur.com/article/325715
Cappelli, P., & Tambe, P. (2019). Using AI in Human Resources: The Promise -- and the
Pitfalls. Retrieved 31 August 2019, from
https://knowledge.wharton.upenn.edu/article/using-ai-in-human-resources/
Efma. (2019). Banking Innovation of the Month: Jobs Intelligence Maestro by DBS.
Retrieved 31 August 2019, from https://www.efma.com/article/detail/30800
Mathur, N. (2019). How HR leverages artificial intelligence, analytics to recruit, retrain,
reward. Retrieved 31 August 2019, from https://www.livemint.com/technology/tech-
news/how-hr-leverages-artificial-intelligence-analytics-to-recruit-retrain-reward-
1554058741437.html
Nicastro, D. (2018). 7 Ways Artificial Intelligence is Reinventing Human Resources.
Retrieved 31 August 2019, from https://www.cmswire.com/digital-workplace/7-ways-
artificial-intelligence-is-reinventing-human-resources/
Wislow, E. (2017). 5 ways to use artificial intelligence (AI) in human resources. Retrieved 31
August 2019, from https://bigdata-madesimple.com/5-ways-to-use-artificial-
intelligence-ai-in-human-resources/