MNO3701/MNO3333
Semester 2, AY2021/2022
Cover Page
CA4 Term Paper (30%)
Name of student
Kathik Jeyabalen
(as in official records)
Student Number
A0217997H
Class group
(A1 or A2)
A1
Title of paper
Can AI ‘fix’ performance
management?
Introduction
Performance management is defined as the cultivation of a work environment which
maximises the abilities of its employees and achieves organisational aims. (Hayes,
2021). This process primarily aims to fulfil two broad-based objectives: developmental
and administrative. With an additional 30% of companies worldwide adopting Artificial
Intelligence (AI) (Baker, 2020), performance management is becoming increasingly
digitised. (Baker, 2020). However, this has not improved its perception - it is still viewed
as ineffective in bolstering performance and facilitating constructive feedback
(Paulsen,2021). Thus, this paper aims to evaluate the effectiveness and importance
of AI’s predictive and analytical capabilities in bolstering managers’ attainment of
administrative and developmental processes in performance management.
The integration of AI in performance management has bolstered the decisionmaking capabilities of managers, particularly in administrative processes.
The utilisation of AI in performance management processes has enhanced the
decision-making capabilities of managers. The integration of algorithms and statistical
models which accurately analyses large quantities of information and uniquely
evaluates nonlinear similarities in an unbiased manner (Colson, 2019) bolsters
companies’ identification of sources that hinder performance (Schrage et. al, 2019).
With this information, managers can now develop more targeted solutions to bolster
performance, a pivotal administrative goal (Snell & Morris, 2019). However, the
aforementioned benefits are not unique to performance management - AI has similarly
improved planning processes in other business functions. (Kelly,2022).
Hence, the integration of AI can only be effective in achieving administrative
aims if its implementation is accompanied by strategic changes in
performance management.
For instance, many companies have faced a ‘technology trap’ in performance
management, wherein the purchase of AI-driven softwares to measure pre-existing
metrics has not resulted in an increase in performance (Avature,2021). This could be
attributed to the failure of pre-existing metrics to accurately measure performance (Avature, 2021). An example of effectively changing performance management
strategies in conjunction with digitisation would be DBS. DBS’s machine learning
based system predicts talent retention at an effectiveness of 85% (Schrage et. al,
2019). This enables managers to make more informed decisions pertaining to
retention, termination, and human resource planning - pivotal administrative goals.
However, DBS also changed its measurement of performance entirely (Kiron &
Spindel, 2019). For instance, its new metrics included measures such as digital
adoption amongst consumers, and transitions to digital channels because it was
focused on digitising its banking processes. (Kiron & Spindel 2019). By implementing
these changes and measuring it using AI driven software, DBS was better
dispositioned to measure its employees’ ability to fulfil its unique definition of
performance. These strategic changes, coupled with the integration of the predictive
accuracy of machine learning analytics, bolstered its ability to make pertinent
decisions pertaining to retention. This illustrates that the analytical and predictive
capabilities of AI are only effective when performance management metrics are
aligned with a company’s unique definition of performance, as strategic changes which
require human thought processes cannot be formulated using AI’s boundaries of
objective rationality (Colson,2019).
The importance of integrating AI with strategic changes could explain why it
has not wielded an equally commensurate improvement in feedback, a key
developmental aim.
Feedback is arguably the most important developmental goal in performance
management - it is a pivotal channel which enables managers to fulfil other
developmental goals such as highlighting successes, examining employees’
progression and bolstering their performance (Snell & Morris, 2019). Theoretically, the
integration of AI in feedback systems should bolster its effectiveness, given its
predictive abilities and lack of biases (Jain, 2021). However, employees simply do not
trust AI -generated feedback due to a fear of replacement, and performance levels
have decreased with its implementation, highlighting the importance of human
interaction (Jain,2020). At the same time, managers have been derided for providing
feedback which is not constructive (Lee, 2020). Hence, this indicates that the manner
in which feedback is provided requires strategic change – an issue which cannot be
solved by the integration of AI alone.
Hence, managers should aim to concurrently implement strategic changes to
feedback with the integration of AI enhance its effectiveness.
The principles of providing constructive feedback are established and recognised
amongst managers, namely the importance of providing honest yet supportive
feedback which culminates in professional development (Memon et al, 2019).
However, managers still struggle to provide constructive feedback. This could be
attributed to an aversion to providing blunt feedback because of the impact of negative
feedback on employee engagement and talent retention (ClearCompany,2019).
Hence, in order to correct these issues, neither the integration of AI nor the recognition
of constructive feedback is sufficient. Rather, constructive feedback needs to be
embedded within organisational culture to ensure that it is provided to emplyoees.
A pertinent example of this would be Netflix, a company renowned for its ‘feedback
culture’. Netflix was able to normalise honest, supportive feedback through its ‘farming
the dissent model’ which encouraged employees to critique each other’s contributions
and subsequently redevelop their ideas. The implementation of this policy coincided
with Netflix’s success in its innovative offerings (Lee,2021), highlighting the positive
impact of a feedback culture in providing constructive feedback and bolstering
performance. In this instance, the integration of AI-driven software can inform
managers on the effectiveness of current shared practices in providing constructive
feedback, enabling them to make pertinent changes. Managers can leverage on AI’s
superiority in providing insights from analysing different variables to assess if the
current shared practices in its ‘feedback culture’ manifests positive outcomes in
innovation and engages employees to develop ideas (Zaki, 2021). Subsequently,
managers can develop improved shared practices, whose effectiveness can be tested
prior to implementation by harnessing its predictive capabilities (Zaki, 2021). This
reiterates the importance of implementing strategic changes with the integration of AI.
Conclusion
AI alone cannot ‘fix’ pre-existing issues in performance management. It merely
bolsters the effectiveness of strategic changes targeting developmental and
administrative aims. While the information harnessed from the superior predictive
abilities and analytical capabilities of digitisation can bolster the decision-making
capabilities of managers, it cannot make strategic decisions for them, where unique
human judgement is necessary (Colson,2020), as evinced by the ‘technology trap’ and
the failure of AI generated feedback. Hence, managers should instead utilise AI to
inform their implementation of strategic measures, which require human intuition
(Colson,2019). This is an important takeaway which applies to all aspects of human
capital management and business processes amidst a landscape obsessed with the
integration of artificial intelligence.
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