Int. Journal of Business Science and Applied Management, Volume 17, Issue 1, 2022

Key elements in transferring knowledge of the AI

implementation process for HRM in COVID-19 times: AI

consultants’ perspective

Mohand Tuffaha

Business Administration and Management School, Universitat Politècnica de València

Aremoda 45056 Valencia, Spain

Email: mohtuf@hotmail.com

M Rosario Perello-Marin

Business Administration and Management School, Universitat Politècnica de València

Camino de Vera s/n E-46022 Valencia, Spain

Email: rperell@upvnet.upv.es

Esperanza Suarez-Ruz

School of Industrial Engineering, Universitat Politècnica de València

Camino de Vera s/n E-46022 Valencia, Spain

Email: esuarezruz@omp.upv.es

Abstract

Although artificial intelligence (AI) is transforming the workplace structure, very little is known about

the strategy that facilitates AI implementation in organizations. The purpose of this paper is to explore

key elements in transferring knowledge of the AI implementation process in human resource

management (HRM) from the perspective of AI consultants. This study utilizes qualitative data

analysis techniques. We first review the literature and then conduct in-depth semistructured interviews

with eight AI consultants. We analyze transcripts using the ATLAS.ti software. First, this research

reveals that AI implementation is affected by a shortage of employee data, no clear vision, a limited

understanding of the AI decisions framework and managers’ desire to bypass AI decisions. Second, the

combination of an intensive training program and assigning AI specialists is the best way to transfer the

knowledge of AI implementation processes to HR managers. Third, HR managers should create

communication channels and enhance employees’ awareness of the positive impact that AI solutions

have on smooth collaboration with AI-employees. The paper also reveals that accelerating the process

of implementing AI applications has no negative impact in COVID-19 times. However, an AI bias may

be considered a potential threat for AI implementation. This paper attempts to provide a practical

understanding of the elements that facilitate AI implementation in the HRM process. It provides vital

insights for HR managers and AI developers to benchmark their activities when designing and adopting

AI solutions. It also contributes to the literature by responding to the question of how AI

implementation should be provided to HR managers and employees.

Keywords: Artificial Intelligence, Implementation, HR Manager, Employee, COVID-19

Int. Journal of Business Science and Applied Management / Business-and-Management.org

1. INTRODUCTION

Data is becoming the most valuable asset for any organization and might be its only truly

inimitable asset (de Medeiros et al., 2020). Organizations have ample access to massive volumes of

data from several sources, which, when reliably and quickly processed, significantly increases the

likelihood of obtaining valuable insights to guide decision makers and employee performance in the

HR department (Wan & Liu, 2021). HR data plays a crucial role in guiding the performance measure in

a competitive business environment. This includes employee data, such as employee performance,

employee behavioural patterns, attendance, compensation and other personal data (Pillai & Sivathanu,

2021). Researchers have noticed that data strongly inﬂuences strategy formulation because the increase

in data and analytical capabilities is redeﬁning innovation, competition and productivity (Nisar, et al.,

2021). According to an interview published by the McKinsey Global Institute, conducted by Barr Seitz

and Rob Roy: “Having a data-first mentality is a crucial first step, but then you need to put in place the

processes and capabilities to be able to use this data” (Seitz & Roy, 2018). Therefore, HR data needs to

be appropriately managed to maintain data quality depending on two assets: emerging technology and

human asset (Yablonsky, 2021).

Artificial intelligence (AI) is considered the most advanced technology that transforms the nature

of the workplace and the relationships among employees. Based on a study conducted by McKinsey

and Company in 2020 about the state of AI, revenue increased from adopting AI in human resources by

55% and adopting AI across several teams led to 33% increased team efficiency (McKinsey, 2020).

These advantages of AI depend on a large set of technologies that allow the computer to perform many

HR tasks that generally require human intervention. These tasks extend to covering data mining in

recruitment and selection (Allal-Chérif et al., 2021), employee turnover (Zhang et al., 2021) and data

extraction, with an emphasis placed on employee performance and productivity (Arslan et al., 2021).

For instance, IBM and Microsoft are using AI and data mining to identify suitable candidates for

particular jobs by standardizing candidate sourcing and C.V screening methods for all their subsidiaries

(Garg et al., 2021). Similarly, Human Capital Management (HCM) from Oracle is using data-driven

insights, which help with talent acquisition and advanced HR metrics as a part of AI–HR process

integration (Fernandez J. , 2019).

HR data-driven solutions have a constructive effect on survival during times of crisis and

pandemics (Vahdat, 2021). COVID-19 is a contagious infectious pandemic that negatively influences

major economic sectors globally. This influence is driven by a series of widespread lockdowns of

various sizes, marking it as the first crisis and life-changing phenomenon of its type for nearly 7 billion

people around the world (Nguyen et al., 2021; Nizamidou & Vouzas, 2018). This crisis has, in turn,

posed financial challenges for organizations as a result of a massive decrease in product demand and

sharp declines of in-house employee numbers due to isolation and social distancing, which inevitably

leads to fewer investments (Adikaram et al., 2021). On the one hand, companies are concerned to

ensure their employees’ health and safety. On the other hand, curtailing the spread of this virus means

having to make drastic changes in the workplace structure and employee performance (He et al., 2021).

Disruption in the workplace, such as that caused by COVID-19, prompts the need for a novel

solution to facilitate remote working options. The AI-powered solution is bridging the physical world

and the digital world by strengthening human-machine interactions and fostering automation through

integrations between smart machines and HR tasks (Pereira et al., 2021). Accordingly, AI is considered

the recent key answer in the unexpected situations faced by individuals and corporations related to

business survival in disasters (Kashyap & Raghuvanshi, 2020). In fact 52% of US companies are

accelerating their AI investments in the wake of the COVID-19 crisis in different company areas (PwC,

2020). One international bank has created a source of truth from datasets and launched an AI-powered

chatbot to respond to customer queries. These efforts not only helped customers, but also demonstrated

to employees the role of AI in facilitating job demands (McKinsey, 2020). Therefore, AI

implementation acts as the optimum solution for organizations and technology developers to bypass

COVID-19 challenges.

On the other side of the story, human assets, HR managers and employees are playing a crucial

role in maintaining the quality of data and uploading these data in AI-powered solutions to leverage

these solutions (Wiblen & Marler, 2019). Both HR managers and employees have witnessed the

advanced role of AI applications in several human resource management (HRM) functions. From a

manager’s perspective, AI facilitates the functions of collecting, managing, analyzing and visualizing

large amounts of data to generate recommendations and insights (de Medeiros et al., 2020). From an

employee’s perspective, AI enables employees to work in both physical and virtual spaces. These

facilities will help employees to save useless commuting time, provide them with more flexibility,

enable them to manage work and collaborate with no time and place constraints (Malik et al., 2021).

Mohand Tuffaha, M Rosario Perello-Marin and Esperanza Suarez-Ruz

Organizations have taken advantage of AI in different ways, which has led to a massive change in

the landscape of HR processes. However, there are still challenges and difficulties to be addressed with

AI advantages (Tambe et al., 2019), for instance: the data generation stage, employees' learning

capabilities, the accountability questions associated with fairness and ethical constraints, possible

adverse employee reactions to management’s decisions via data-based AI (Harney & Collings, 2021).

These challenges have brought about a need to enhance HR managers’ knowledge about AI and how

this technology should be adopted among their employees. Kolbjørnsrud et al. (2017) report that

managers are not confident enough about AI. Their readiness and enthusiasm for AI may vary

extensively across organizational levels, which raises serious questions about the optimum strategy for

sharing knowledge of the AI implementation process.

The importance of extending HR managers' and employees’ knowledge about AI implementation

has been further highlighted by Chang (2020). He calls for further studies to investigate the best HR

manager-employee balance for AI implementation and how knowledge about AI implementation

processes can be provided to HR managers and employees. At the same time, the current massive

challenges posed by COVID-19 provide an opportunity for management scholars to extend research

efforts and turn them into actionable insights. These initiatives will support organizations in handling

one of the greatest challenges in modern history by identifying the potential impact of COVID-19 on

AI implementation (Hamouche, 2021).

Despite several studies having been conducted on AI implementation in HR tasks, such as

recruiting, selecting and performance management (Wall & Schellmann, 2021; Tuffaha & Perello-

Marin, 2021; Xiong & Xia, 2020), there is still a gap in the literature in terms of understanding the best

HR manager-employee balance for AI implementation. To overcome these gaps, the following research

questions (RQs) are raised:

RQ1: How can the knowledge of AI implementation process be transferred to HR managers?

RQ2: What kind of strategies can HR managers adopt for smooth AI implementation among

employees?

Our analysis contributes to the literature about HRM in AI in several ways. First, we develop a

practical understanding of the elements facilitating AI implementation in the HR department during a

pandemic. Second, our study responds to Chang’s (2020) request to research the best-demand balance

for AI implementation. Finally, this novel research will be beneficial for HR managers and AI

developers when designing and adopting AI solutions.

The article is organized as follows. We ﬁrst provide a literature review. Then we discuss the

methods. Next we analyze the categories reported by the participants. This is followed by a section

with discussion and implications. The ﬁnal section offers some conclusions.

2. LITERATURE REVIEW

This section attempts to look closely at what is currently known about AI solutions in HR, the role

of AI in COVID-19 times and the effects of AI implementation on HR managers and employees. We

review the literature and technical reports on AI and HRM. HRM and COVID-19 are also considered.

2.1. Impact of technologies on HRM

For several decades, scholars have been studying the impact of information technology (IT) on

HRM. One of the main study areas is E-HRM: the planning and implementing of IT among employees

for collective actors of HR activities (Poba-Nzaou et al., 2020). Subsequently, scholars have focused on

big data and the development of HR analytics in organizations (Dahlbom et al., 2020). Later scholars

have centred on the implementation of AI, data mining, HRM cloud computing and algorithms in

functioning HR tasks (Alrashedi & Abbod, 2021; Black & van Esch, 2021; Marin et al., 2021), and

access to and the creation of structured and unstructured HR-specific datasets with growing

dependence on advanced digitalized HRM and AI applications to generate insights, solve issues and

participate in HR decision making (Caruso, 2018; Prikshat et al., 2021; Vrontis et al., 2021). The

diffusion and applications of AI-innovated database management are demonstrated by emergent AI-HR

solutions, such as SAP SuccessFactors, ERP and CloudHR (Oracle, 2022; SAP, 2022). Google,

Microsoft, IBM and LinkedIn are among the IT behemoths that have also created such applications.

The job of HR professionals involves redefining and restructuring as a result of greater digitization and

data utilization by AI-powered HR solutions (Qamar et al., 2021).

AI is defined as a smart machine-based system’s ability to correctly interpret data, find patterns

and adopt the resulting information to fulfill specific goals and to perform tasks (Arslan et al.,2021). AI

uses several techniques, such as the Internet of Things (IoT), deep learning, pattern recognition,

Int. Journal of Business Science and Applied Management / Business-and-Management.org

machine learning and artificial neural networks (ANN) (Ali & Frimpong, 2020; Lu, 2021). The main

advantages of AI, such as high-speed processing, big data mining and sophisticated prediction and

analysis, differentiate AI from existing heritage IT applications (Cheng & Hackett, 2021). These

advantages help HR managers to save time and to make accurate decisions (Borges et al., 2021) in

critical tasks like recruitment and selection, performance management and workforce planning (Black

& van Esch, 2021; Santana & Valle-Cabrera, 2021).

2.2. AI in HR amid COVID-19

The COVID-19 pandemic has created uncertainty in not only firm productivity and operational

terms, but also in HRM efficiency terms. Therefore, significantly increased attention has been paid to

AI during COVID-19. This demand is related to several novel applications with the abilities to

facilitate virtual workplaces. Organizations have leveraged AI in HRM during COVID-19 in several

dimensions. For instance, first AI-powered chatbots have facilitated the role of the HR department in

conducting virtual recruiting and to arrange interviews without candidates having to physically travel to

the workplace (Sowa et al., 2021). Second, AI plays an advanced role in training and development by

analyzing the data collected from employees and test results, and then building individual development

programs based on these results (Boiral et al., 2021). Third, AI also plays a role in monitoring and

analyzing employees’ cultural enrollment and engagement by identifying behavioural patterns (Rao &

Krishan, 2021). To illustrate all this, AI-enabled automation tools can handle and analyze massive

volumes of data, recommend courses of actions and carry out these recommendations (Vahdat, 2021).

As a reflection, HR managers can virtually operate HR departments.

AI applications in HR tasks during COVID-19 have raised employees’ concerns about their

existence (Mohamadou et al., 2020). Many scholars argue that fully adopting AI applications will

reduce aggregate labour hours (Frey & Osborne, 2017), which will imply fewer employees, while

others claim that many AI applications are useless without employees intervening (Dwivedi, et al.,

2021). Employees are required to analyze and confirm AI recommendations, convert recommendations

into courses of action and offer backup if an AI solution fails. As a result, a new trend of an AI

implementation scenario is formulated that depends on boosting employee productivity by AI-enabled

technology rather than replacing them (Dwivedi, et al., 2021).

The opportunities for AI are numerous. To accomplish the potential leverage of these applications,

organizations should maximize "AI-human integration". This integration should be based on detailed

information about the AI implementation process (Wilson & Daugherty, 2018). Therefore, this paper

attempts to bridge the following gap: in what circumstances and to what extent can organizations

transfer knowledge about AI implementation processes to HR managers and employees? It also studies

the impact of COVID-19 on AI implementation.

3. RESEARCH METHODOLOGY

3.1 Data collection

The study took a qualitative approach to examining the data collected from eight AI consultants

experienced in implementing AI in HR projects in multi-international firms. These professionals were

identified using LinkedIn (see Table 1) to obtain a more holistic overview of the research questions.

LinkedIn has had major implications for recruiters, hiring managers and job seekers. It is used by over

40% of potential job seekers and 85% of hiring managers for the purposes of screening applicants and

other recruitment processes (Collmus et al., 2016; Cubrich et al., 2021).

As the table shows, interviewees’ average work experience is 13.87 years. During the interviews,

we asked questions about how the AI implementation process can be provided to HR managers and

employees, and then about the impact of COVID-19 on AI implementation.

Mohand Tuffaha, M Rosario Perello-Marin and Esperanza Suarez-Ruz

Table 1. Interviewees’ profiles

Title / Position

Years of

experience

Area of experience

Company

Size

Interview

style

Senior Artificial

Intelligence and

SAP Consultant

over 16

years

Technology leader with expertise in

NLP, Deep Learning, Analytics and

SAP.

More than

1000

employees

Online

interview

(via Zoom)

Co-founder of

impress.ai

over 14

years

AI-Powered Chatbot Platform for

Recruiters

employees

Online

interview

(via Zoom)

Senior Artificial

Intelligence

(AI) Developer

at Cloud

Solutions

13 years of

experience

Specialist in:

AI in healthcare

Business Analyst

Full-stack Development (Web &

Mobile)

More than

500

employees

Online

interview

(via Zoom)

HR & Talent

Tech Start-up

Advisor, AI

Educator and

Organizational

Consultant

15 years’

experience

Businesses Adviser for AI and

emergent technology in HRM

functions

Online

interview

(via Zoom)

SAP Success

Factors

Consultant

11 years

SF/SAP solution consultant, business

process analyst, team leader,

integration consultant, trainer, onsite

service coordinator and career

counselor.

Senior system engineer at IBM

Around 200

employees

Online

interview

(via Zoom)

Director

Solutions

Consulting

(Gulf region)

HCM at Oracle

over 16

years

AI in HRM implementation

consultant

Solution orientated for HRM project

110

employees

Online

interview

(via Zoom)

Hiring Solutions

Selling

Professionals

(Cloud,

CyberSec, &

AI)

10 years of

experience

Cybersecurity & compliance

Software Engineering (Custom

Applications Development -

Enterprise)

Cloud & Infrastructure

Data Science and Advanced analytics

400

employees

Online

interview

(via Zoom)

Director, AI &

FinTech Leader

in PwC

over 16

years of

experience

Helping PwC and its clients benefit

from global AI insights and expertise

Leading the development of AI-

enabled products and solutions

Online

interview

(via Zoom)

3.2 Data analysis

To evaluate the data collected, we used the following procedure (see Figure 1). In the first step,

interviews were coded into text and turned into a transcript by compiling replies on a single response

sheet for each research question individually. In the next step, the transcript was cleaned of special

characters, numerical values and spaces. Finally, these transcripts were imported to the ATLAS.ti

qualitative data analysis software (Paulus & Lester, 2016). The team working on establishing reliability

and validity consisted of two researchers. Each one has prior research experience and works as an

Associate Professor in a management college. A word cloud was created, which was then utilized to

extract the major content from the analysis. A mixture of text mining and qualitative content analysis

(Using ATLAS.ti) was applied so that thematic convergence was evident from the data collected from

the interview transcripts.

Int. Journal of Business Science and Applied Management / Business-and-Management.org

Figure 1: Data collection and analysis process

4. FINDINGS

Data analysis has been done with the help of statistical tools like Statistical Package for the Social

Sciences (SPSS) and Structural Equation Modelling using AMOS version 21. To ensure the validity of

the Information quality measures (adapted scale), Exploratory Factor Analysis (EFA) was conducted.

The EFA result showed a one-dimensional scale of information quality. The total variance explained

was 67.20%. Further to establish convergent validity, mainly three criteria are used: “Factor loadings

>0.7>, Average Variance Extracted (AVE) >0.50 and Composite Reliability (CR)>0.7” (Sarstedt,

Ringle and Hair, 2017; Hair et al., 2018). The results are under the prescribed threshold limits. Table 2

displays the factor loadings along with Cronbach’s Alpha values. The correlation matrix, composite

reliability (CR), and AVE of the study variables are shown in Table 3.

The analysis of interviewees’ responses revealed three major categories: 1) challenges associated

with AI implementation in the HR process in COVID-19 times; 2) transfer of knowledge about AI

implementation processes to HR managers; 3) HR managers' strategy for smooth AI-employees

interaction (Figure 2). The first two categories addressed the first question, while the third category

dealt with the second question.

Selection of

respondents

145 potential AI

consultants and

developers in HRM

were identified from

Initial contact

Official invitations were

sent to the initial sample

through LinkedIn

messenger

Invitation

rejected

94.5% did

not

participat

e in the

study

Schedule interview

5.5% interviewees

showed an interest and

participated in the study.

Interviews were

conducted via Zoom

Coding

Online

interviews

were

recorded and

then

converted

into

transcripts

by the cloud

365 single

Microsoft

sheet

Cleaning

data

Data

cleaning was

done to

eliminate

unnecessary

values,

characters,

numbers and

spaces from

transcripts.

Analysis

The thematic analysis of the

interview transcripts was browsed,

coded and interpreted by ATLAS.ti

Findings and discussion

Alignment of responses was done

with identified themes and

literature

Mohand Tuffaha, M Rosario Perello-Marin and Esperanza Suarez-Ruz

Figure 2: Key elements to transfer knowledge about AI implementation processes for HRM in

COVID-19 times

4.1. Challenges associated with AI implementation in HR processes in COVID-19 times

The word cloud for the first research question indicates that “AI Data” is the word that was

highlighted the most by the interviewees, followed by vision, clear, accept, solution, bypass,

infrastructure, etc. The thematic analysis of the first research question indicated some barriers and

challenges for implementing AI in the HR process (see Table 2). Interviewees highlighted weak

employee datasets, which minimized the efficiency of AI functions. Data and AI are merging into a

synergistic relationship, where AI is useless without HR data sources like personal employee data,

employee performance and financial data. In contrast, mastering these data is difficult without AI

solutions. This idea comes over in the following remarks:

Interviewee 5: “Of the key challenges for AI we find data availability in organizations. If it's

available, it might not be central enough”.

Interviewee 2: “Data collecting and processing speed have enabled the development of AI. This

development leads to an analysis of the data, which help to assess and predict employee performance”.

Transferrin

knowledge

of the AI

implementa

tionprocess

to HR

managers

and

employees.

Challenges

related to

implementi

ng AI in

processes

Transferring

knowledge

of the AI

implementat

ion process

to HR

managers

manages’

strategy for

smooth AI -

employees

interaction

Shortage of employee data

Companies have no clear vision and no clear

understanding of the AI decision framework

Weak infrastructure

Understand the client's requirements

AI involvement in decision making and

manager’s desire to bypass AI solutions

Limit integration between departments

Offering trainings program and assigning AI

specialists

Create communication channels

Enhance the awareness of the positive impact

of AI on employees’ performance

AI-employees collaboration maintains the

balance among job demand, professional

development and social life

Int. Journal of Business Science and Applied Management / Business-and-Management.org

Interviewee 7: “Massive volumes of data would be worthless unless AI models unlock the

potential of these data and turn them into information”.

Table 2: Themes identified for Research Question 1 (RQ1) (Part 1)

Note: Only themes with a frequency of more than two were included

The next theme was the unclear vision on the areas where AI is used to support organizational

performance (16.6%), which resulted from the ambiguity as to how AI is building its decisions or

predictions. HR managers frequently lack an understanding of the assumptions and decisions made by

AI. They also need standardized assessments of the overall risk and financial metrics of AI like return

on investment (ROI). Additionally, tools that explain how AI systems make decisions could enable

managers to better understand the potential risk associated with AI implementation. Quoted below are

some instances from interviewees’ answers:

Interviewee 3: “One organization manager told me: we are facing problems to build a clear

vision for the future of AI in our company resulting from the ambiguous process of the AI decisions”.

Interviewee 4: “HR managers need a clear conceptual model to analyze the ROI when adopting

AI in their departments; such as potential risks and financial impacts. We know the features of AI, but

managers are unaware of the disadvantages of AI in their departments”.

Another theme is that managers wish to have the authority to bypass AI solutions (11.2%). AI has

wide applications for integrating information, analyzing data and using the insights gained to improve

employee performance. However, interviewees admitted that their organization’s manager wishes to

have the upper hand in the final AI decision related to employees. Some instances from interviewees’

answers are quoted below:

Interviewee 6: “Another challenge that we are facing is: to what extent should AI be involved in

decision making? Some HR managers would like to use AI internally but, at the same time, they want

the authority to bypass AI solutions”.

Interviewee 3: “Some companies are not responding to AI recommendations, which poses

obstacles to machines toward generating correct decisions in the future”.

Finally, another aspect worth highlighting is that all interviewees shared the same impression: AI

is the best solution that organizations should adopt to manage their employees and to minimize the

influence of uncertainty and confusion. Therefore, AI-powered solutions and implementation are in

great demand in COVID-19 times. These responses are reasonable if we analyze the advantages of AI

in the virtual workplace. These advantages include but are not limited to: 1) the role of AI-powered

solutions in effectively achieving employees’ tasks; 2) helping the HR department to follow up the

analysis of the sentiment of employees and to flag up employees’ concerns; 3) detecting employee

engagement trends across specific employee segments (PwC, 2020)

Along similar lines, all the interviewees also reported that accelerating AI implementation during

pandemics does not have a negative impact on organization performance. However, interviewees voice

a concern about the negative impact of AI bias on organizational performance.

An AI algorithm decision is based on training data, which may either include biased human

decisions or reflect historic or social concerns, even if sensitive variables like gender, race or sexual

orientation are removed. Algorithm biases reduce employees’ ability to participate in developing their

organization by encouraging mistrust, which may produce inadequate results.

Business and organizational leaders need to ensure that the adopted AI system is free from

algorithm bias to maintain the quality of the decision-making process. They have the responsibility to

encourage the progress of research and standards that reduce any bias in AI to eliminate potential

negative impacts from implementing AI. The following remarks made by AI consultants reflect this

approach:

S. No

Themes

Frequency

Percentage

1.1

Employee data and data quality

20.5

1.2

Companies have no clear vision or no clear understanding of the AI

decision framework

16.66

1.3

Managers’ desire to bypass AI decisions

11.2

1.4

Weak infrastructure

7.6

1.5

Limited integration between departments

5.1

1.6

Understanding the client's requirements

4.5

Mohand Tuffaha, M Rosario Perello-Marin and Esperanza Suarez-Ruz

Interviewee 3: “if we have an algorithm that is biased toward women because of training data,

this will be reflected in the determinant of women’s future success, and women who have trained will

be subject to an AI algorithm bias because most leading positions have to be for men”.

Interviewee 6: “The role of imbalanced data is vital for introducing bias. Feeding AI with the

correct dataset is vital to maintain the perfect image in the face of our employees. Otherwise AI

implementation will collapse”.

Based on our data, it would appear that while AI is a tempting next step toward creating value for

organizations and employees, shortage of data and the relationship of mistrust between AI and

managers have been considered a barrier for AI implementation. These challenges are boosting the

need for initiatives that eliminate the risk of users being threatened by AI implementation. This

message is similar to a project launched by the European Union called “TRUST-AI” (European, 2020)

to develop trust in AI adequacy to tackle predictable.

4.2. Key factors in transferring knowledge about the AI implementation process to HR

managers

Another word cloud created for the first research question showed that “AI training” and “AI

specialist” were the words highlighted by most interviewees, followed by knowledge, technical,

consultant, person, function, etc. The thematic analysis revealed two main defined themes (see Table

3). According to interview data, interviewees took two different points of view about the best way to

transfer knowledge about AI implementation processes to HR managers.

The most prominent ones were that transferring knowledge about AI implementation depends on

two key factors: offering a training program for HR managers and assigning AI specialists (67%) to

follow AI implementation progress.

Table 3: Responses for Research Question 1 (RQ1) (Part 2)

S. No.

Themes

Frequency

Percentage

2.1

AI provided for HR managers by Internal training and AI

consultant/specialist

67.3%

2.2

AI provided for HR managers by internal training only

22.2%

Note: Only the themes with a frequency of more than 2 were included

AI’s contribution to the organization’s performance depends, on the one hand, on enhancing HR

managers’ knowledge by an intensive training program and, on the other hand, appointing an AI

specialist in the organization. Quoted below are some instances from interviewees’ answers:

Interviewee 1: “If I need an AI solution for interviewing candidates, this kind of solution needs the

recruiter to be familiar with the AI system. At the same time, AI technicians should be available in the

organization to maintain data and to cover any problems if the system collapses.”

Interviewee 7: “I believe that internal training is not enough alone. The organization should

assign an AI specialist who is aware of this recently emerged technology to maintain the follow-up of

data in the AI solution”.

To maintain the quality of the first point of view (Theme 2.1), and to explore the potential

negative impact on the organization’s performance, the authors followed interviewees’ answers with

another question: “If AI specialists are hired, could the role of AI specialists overlap that of decision

makers?”. The first point of view believed that AI specialists would have no reflection on HR decisions

for two reasons: 1) AI specialists do not work for the HR department only, but facilitate AI

implementation for other departments within the organization; 2) AI specialists work as consultants

only because HR managers have the qualitative and quantitative skills which are difficult for AI

specialists to acquire. Some instances from interviewees’ answers are quoted below:

Interviewee 7: “AI specialists should do cross-company work, and not just deal with HR. They are

solutions-oriented and AI specialists perform cross-domain functions”.

Interviewee 4: “AI specialists provide and explain why this recommendation is proposed. They

play no role in final management decisions”.

Int. Journal of Business Science and Applied Management / Business-and-Management.org

The second point of view (Theme 2.2) emphasizes that the training program is a sufficient way to

transfer knowledge about the AI implementation process (22%). Here it was argued that personal data

and employee performance data should remain confidential and not be exposed to any third party.

Quoted below are some instances from interviewees’ answers:

Interviewee 2: “We should not accept the AI specialists’ point because [1] of data privacy, and

[2] they could guide us in the wrong direction, which could affect on the functionality of the HR

department”.

Interviewee 4: “Training is key for AI implementation. We can’t expose our employee data to a

third party. We can go for some kind of external support, but only at the beginning. But, in the end, you

have to manage the whole process”.

The first point of view is in line with the finding reported by Khabiri et al. (2012). They claimed

that the most important aspect of technology transformation is how to transfer technology and which

mechanism offers the transferee more benefits. Collaboration between the transferer (the AI specialist

in our case) and transferee (HR managers in our case) is essential for the smooth implementation of

new technology. Therefore, in parallel to what is noted above, the authors decided to follow the first

point of view as a key factor for transferring knowledge about the AI implementation process to HR

managers.

4.3 HR managers’ strategy for smooth collaboration with AI-employees

A word cloud was generated for the second research question, which indicated that

“communication” is the most frequent word used in the responses, followed by awareness, acceptance,

collaborative, orientation, partners, integrate, help, future, efficiency, etc.

The thematic analysis for the second question revealed some definitive themes related to smooth

AI implementation among employees (see Table 4). Themes were categorized as creating

communication channels (19.4%). This implies that HR managers can gain insights into what things are

threatening employees from implementing AI by expressing their ideas and perspectives without

criticism. These HR manager-employee discussions and communications are the best way to build a

strong relationship, remove ambiguities and increase the efficiency of AI-driven solutions. Aligning the

AI-business goals with the capital strategy in a wide range of attitudes, behaviours and intentions could

strongly impact smooth AI implementation among employees. Quoted below are some instances from

interviewees’ answers:

Interviewee 2: “Communication is essential in this sense because it is a key to solve the problem

of mistrust, preconceived ideas on AI implementation. This kind of communication will promote the

idea that employees are more valuable than AI”.

Interviewee 3: “Open dialogue between managers and employees is the best recipe for integrating

these systems in ways that actually enhance the quality of work”.

Table 4. Themes identified for Research Question 2 (RQ2)

S. No.

Themes

Frequency

Percentage

3.1 Create communication channels 28 19.4

3.2

Enhance the awareness of the positive impact of AI on

employee performance

17 14

3.3

Collaboration with AI-employees maintains a balance among

job demand, professional development and social life

9 8.5

Note: Only the themes with a frequency of more than 2 were included

Another dominant theme was enhancing awareness about the positive impact of AI on employee

performance (14%). AI plays a role in accelerating the achievement of employees' daily tedious work

by providing a powerful database and analytical support for their decisions. To rule out alternative

explanations, the dominating idea spread among employees that AI-powered solutions are machine-

oriented and have difficulties in understanding human perceptions. To eliminate negative perceptions,

HR managers should offer practical evidence to convince employees of the abilities of AI-powered

solutions in customized recommendations depending on the tasks demanded. Quoted below are some

instances from the interviewees’ answers:

Mohand Tuffaha, M Rosario Perello-Marin and Esperanza Suarez-Ruz

Interviewee 1: “HR managers should make employees aware of the usefulness of AI; it’s here to

support them, not to disturb them, as well as sharing the message that employees could promote

themselves by focusing on developing their skills. On the one hand you improve your performance and,

on the other hand, it could minimize your routine”.

Interviewee 2: “Promote awareness with real examples. For instance, if you would like to speak

with HR staff, obviously you have to talk during working hours. But if you're talking to AI-powered

chatbots you can talk at night once you're back home after work. So this atmosphere should be

dominant among employees. AI minimizes employees’ routine tasks and maintains the organization’s

reputation”.

Therefore, employees collaborating with AI maintains the balance among job demand,

professional development and social life (8.5%). AI is led to constantly change in the jobs conducted

by employees. Combining the rapid speed of AI development with changes in employees’ lifestyles

gives a balanced job-social life a whole new meaning. AI implementation will offer employees new

opportunities to develop their skills and to spend time with their families as a result of achieving

deadlines more quickly. Therefore, sharing the message with employees that AI complements human

productivity and, unlike the normal perception, poses no threats, will facilitate AI implementation

strategies. Quoted below are some instances from interviewees’ answers:

Interviewee 8: “We have to build confidence. What is the benefit of the intention of using new

technology? We should send to employees the notion that AI reduces their workload and helps to strike

a balance between job demands and social life demands. This kind of balance will add value to their

job and family. On the one hand, relieving employees from tedious tasks and providing them with

easier access to information. On the other hand, employees will have time and effort to invest in their

children and family. It’s about these kinds of benefits and how they will impact their lives, and not

about taking their job away but, instead, AI will reduce their work so they can concentrate on

somewhere else”.

Interviewee 2: “HR managers have to share the message that AI is here to support employees and

for them to reach their targets, which will definitely be reflected in social life”.

These results fall in line with the findings reported by Dabbous et al. (2021), who claimed that

employees’ acceptance of new technologies depends on several factors. Individual factors are major

determinants of employee acceptance. They also added that the organization's social environment,

culture and HR strategy are technology acceptance parameters. Therefore, HR managers should

integrate organizational culture, social environment and technology acceptance factors when

implementing AI-powered solutions.

5. DISCUSSION

The present study makes some significant contributions with consequences for both research and

practice. This section highlights the areas in which we extend the boundaries of current knowledge and

the ramifications of our findings.

5.1. Research implications

Several studies in the academic literature have dealt with the relation between HR managers and

emerging technology. Previous scholars have identified the crucial role of AI techniques in HR

manager activities, such as supporting complex HR managerial decision making (Reddy et al., 2019)

and assisting HR managers in performing productive big data analyses to achieve the desired outcomes

(Qamar et al., 2021). In contrast, other scholars have advised HR managers to update their technical

skills and enhance their knowledge about data privacy and AI ethical issues (Stahl et al., 2021). These

skills play a role in spreading “AI-enabled-HR services” technology (Qamar et al., 2021).

Scholars have also been advised to identify the keys that facilitate leveraging the AI-powered

applications in HR tasks (Fernandez & Gallardo-Gallardo, 2021). In line with this, the present study

performed an in-depth analysis to understand the key elements for transferring knowledge about the AI

implementation process to HR managers. The findings identified the following challenges associated

with AI implementation: potential risks resulting from data shortage; ambiguous vision of the emerging

technology; mistrusting AI recommendations, which leads to fewer advantages of AI applications.

According to these results, companies that seek to adopt AI solutions in HRM should pay attention

to these elements to ensure the successful knowledge transfer about the AI implementation process. In

order to do so, they should: 1) offer intensive training programs for HR managers that cover the AI

Int. Journal of Business Science and Applied Management / Business-and-Management.org

implementation process, 2) assign AI specialists to follow up implementation progress and to eliminate

any potential collapse in AI-powered solutions.

After the present study performed a detailed analysis of the key elements to transfer knowledge

about AI implementation processes, it provides a comprehensive view of the COVID-19 impact on AI

implementation. There are many advantages from accelerating AI implementation during a pandemic.

However, this usefulness is affected by the potential threat associated with an AI bias to the

performance of both employees and organizations. Prior research on AI implementation during

COVID-19 has demonstrated that making investments in AI can represent a successful approach to

limit disruptive impacts (Acciarini et al., 2021). Accelerating AI implementation may enhance

employee welfare in many ways, such as improving productivity, performance and learning. However,

AI misuse due to an algorithm bias and lack of governance could inhibit employees’ rights and result in

a high turnover, customer dissatisfaction and employee retention (Yang et al., 2021).

Second, this paper presents the steps carried out by HR managers to ensure smooth AI–employee

collaboration. The study proposes a list of factors to ensure positive collaboration, such as: establishing

communication channels; highlighting the positive impact of AI on employee performance; examining

the role of AI in maintaining employees’ job demands, professional development and social life. Prior

research works have indicated that AI-powered technology interventions are seen as being relatively

complex, and this does not pose only a challenge for an organization to implement it, but is also

difficult for employees to accept it (Dabbous et al., 2021). It is HR managers’ responsibility to spread

to employees a sense of trust, morality, transparency and value in relation to AI (Papagiannidis &

Marikyan, 2020). This effort depends on collaboration across several departments because the premise

behind AI implementation is not to replace employees, but to enhance employee performance. A high

transparency level is particularly important when new technology affects the HR process

(Lichtenthaler, 2020).

5.2. Practical implications

AI implementation processes consist of: data input (e.g., software-readable data about employees’

attitude and performance); data analysis (e.g., analysis of the structured and unstructured data); results

generated from data analyses (e.g., information, statistics or predictions) (Meijerink & Bondarouk,

2021). Thus HR managers need to understand the functionality of each stage and improve their

technical skills to obtain utilitarian benefits from AI-powered solutions. For instance, acquiring

essential knowledge on data mining, programming, big data analytics and robotics could be the

backbone of emerging AI technology (Pereira et al., 2021). These skills will help HR managers to

understand the AI implementation process and, at the same time, to protect AI specialists from any

potential employees’ resistance to AI applications affecting and eliminating their decisions. For

instance, one requirement of a new AI system involves making certain modifications to the job

specification and performance appraisal system, which may bring about mistrust in of an organization’s

decision making if it is performed by an AI specialist instead of the HR manager.

The advantages of HR managers acquiring essential knowledge about the AI implementation

process will facilitate the functionality of HR department during crises and pandemics. COVID-19 and

the consequences of lockdown have threatened companies in different ways, such as sacking many

employees, losing financial funds and income, and changing the workplace structure. AI has acted as a

magic wand to solve most of these abnormal circumstances. Companies with a higher AI maturity level

have greater flexibility to adapt to these challenges by protecting employee well-being, having

personalized internal communication, reducing costs and providing differentiated user experience

(Abed, 2021). To leverage AI-powered solutions, organizations have to be aware of managing different

types of AI risks, such as: 1) insufficient data security precautions; 2) insufficient data analytics

processes; 3) AI scripting errors; 4) lapses in data management; 5) misjudgments in the training data

model (Cheatham et al., 2019).

The AI training data bias is considered another type of risk that has an impact on the whole

performance of both employees and the organization. A training data bias may come from algorithms,

data input, or from the interaction between both (Sun et al., 2020). Ignoring potential risks associated

with AI implementation can easily compromise employees’ fairness, privacy, security and compliance.

Second, motivating employees toward using AI should be driven by practical incidents. For

instance, combining the speed of AI to collect and process data and to achieve employees’ tasks and

responsibilities are drivers to minimize employees’ resistance to AI implementation (Jarrahi, 2018).

Demotivated employees toward adopting AI applications could harmed AI functionality in two ways:

through non-cooperation and data obfuscation (Newlands, 2021). Non-cooperation entails ignoring AI

directions and recommendations. For instance, AI is able to boost employee productivity by

recommending various solutions to a single problem. Ignoring these recommendations may affect the

Mohand Tuffaha, M Rosario Perello-Marin and Esperanza Suarez-Ruz

overall performance evaluation. Data obfuscation is described as any process that hides sensitive data

while retaining certain aspects of usability. For example, when employees create an alternate version of

data that is not easily identifiable or reverse-engineered they, therefore, seek to manipulate the

generated results using AI-algorithmic control mechanisms (Kellogg et al., 2020). HR managers have

to deploy a sequential AI implementation process by first selecting pilot employees, followed by

analyzing the reflection of AI solutions on the selected sample and, finally, deploying training with a

wide range of employees. The symbiotic interaction between employees and AI solutions is critical for

successful AI implementation (Grover et al., 2022).

6. CONCLUSION

The evolving and mutual relation between the implementation of emerging technologies and the

HR process can be lucidly explained by an in-depth qualitative study. This exploratory study builds on

two recommendations made by Chang (2020), namely: 1) key factors in the successful knowledge

transfer of the AI implementation process to HR managers; 2) HR managers’ strategies for smooth AI

implementation among employees. By building further on these earlier contributions, we offer a deep

understanding of the challenges associated with AI implementation in the HR process and the impact of

COVID-19 on AI implementation. These contributions help in building the key elements to transfer

knowledge about AI implementation processes for HRM in COVID 19 times.

Such a study provides a comprehensive understanding in addition to the considerable existing

literature on AI implementation and the AI-HR manager-employee interaction. The study is one that

focuses, on the one hand, on the challenges that affect AI implementation and, on the other hand, on

particular strategies for managers and employees to undertake digital transformation.

The bottom line is that this study’s findings provide valuable details for AI developers and HR

managers when digitalizing the HR process. It calls for a wide range of organizational activities that

come in the form of developing HR managers’ technical and soft skills, such as data analysis,

digitalization trends, basic AI core concepts, communication skills, critical thinking, team building and

leadership skills. All these soft skills are lifesavers in today's technology-driven environment because

they allow employees to accept HR managers’ guidance to implement emerging technologies (Caputo

et al., 2019).

7. LIMITATIONS AND FUTURE RESEARCH

The present study is subject to limitations. Due to the novelty of this study, a qualitative study was

performed and the sample of respondents is relativity small. Using different meta-analyses and

extending the number of interviewees could generate new areas to transfer knowledge about the AI

implementation process.

Different future research work could be carried out in several directions to open up new avenues

for the complex interrelations among AI transformation, management and employees. For instance,

examining if HR managers’ academic background could become the key factor for successfully

transferring the AI implementation process. Second, analyzing if HR managers with IT experience

have enough skills to cover the needs of the AI specialists in their organization. Third, empirically

validating the impact of transparency against employee fears about AI implementation. In addition, it

will be interesting to test moderate factors, such as employees’ age, experience, seniority and academic

background, and their influence on HR managers accepting activities leading toward smooth AI-

employee collaboration.

Bearing in mind the fear of the unknown that has been raised from the AI bias, future research

could investigate the legislation and constraints on AI-trained data, as well as the level of dependence

on AI’s judgment for employee professional development-related issues.

Int. Journal of Business Science and Applied Management / Business-and-Management.org

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