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The impacts of articial intelligence on managerial skills
Laurent Giraud
a
, Ali Zaher
b
, Selena Hernandez
c
and Al Ariss Akram
c
a
Full professor, IREGE, IAE - Savoie Mont Blanc University, Annecy, France;
b
Lecturer, Magellan Research
Center, IAE Lyon School of Management, Jean Moulin Lyon 3 University, Lyon, France;
c
Full professor, TBS
Education, Toulouse, France
ABSTRACT
Articial Intelligence (AI) in organisations may change ways of
working and disrupt occupations, including managerial ones. Yet,
the literature lacks information about how managerial skills will be
aected by the implementation of AI within organisations. To
investigate this topic, a thematic content analysis was performed
on data collected from qualitative and semi-structured interviews
with 40 AI experts. These rst results were then conrmed through
descriptive statistics performed on data collected from 103 other AI
experts who also ranked the managerial skills to be developed in
order of priority. Our nal results show that most managerial skills
are likely to be augmented by AI, while only a few of them may be
replaced (information gathering and simple decision-making) or
remain unaected (leadership and imagination). Our study updates
existing technical and non-technical taxonomies of managerial
skills needed to keep pace with AI. It also contributes to the devel-
opment of the AI-Human Resource Management interface.
ARTICLE HISTORY
Received 27 September 2021
Accepted 20 April 2022
KEYWORDS
Artificial intelligence; impact;
managers; skills; artificial
intelligence-human resource
management interface
1. Introduction
Articial Intelligence (AI) is proving to be essential for the success of many organisations
(Scarcello, 2019) and one of the most impactful technologies of the 21
st
century (Fosso
Wamba et al., 2021). It has facilitated a fourth industrial revolution (Ray & Thomas, 2019)
and continues to develop rapidly (Lu et al., 2018).
The introduction of AI in organisations still remain associated with numerous chal-
lenges (Dwivedi et al., 2021) aecting the potential sustainable competitive advantage
that it is supposed to provide (Griva et al., 2021). Most of these challenges are related to
the disruptive potential of AI (Harney & Collings, 2021). Indeed, the integration of human
and machine intelligence requires organisations to review their practices in decision-
making (Herrmann & Pfeier, 2022), organisational learning (Balasubramanian et al., in
press), and issues that fall into the managerial domain.
By modifying potential sources of competitive advantage, the adoption of AI, rst
requires managers to develop new skills to stay relevant in an AI-based competitive
environment (Krakowski et al., in press). AI’s eects on organisations (Griva et al., 2021)
may cause changes to managerial tasks and related skills, as well as newly needed
CONTACT Laurent Giraud laurent.giraud@univ-smb.fr Full professor, IREGE, IAE - Savoie Mont Blanc University,
4, chemin de Bellevue 74944 Annecy-le-Vieux FRANCE, Annecy, France
JOURNAL OF DECISION SYSTEMS
https://doi.org/10.1080/12460125.2022.2069537
© 2022 Informa UK Limited, trading as Taylor & Francis Group
qualications (Vrontis et al., 2021). While Frey and Osborne (2017) estimate that there is
a 25% probability that AI will eventually replace general managers, Huang and Rust (2018)
see AI replacing jobs at the task level.
By augmenting managerial decision-making (Shrestha et al., 2019), AI-induced task
automation may also cause managerial skills to be augmented themselves (Merrill,
2019). Whether for managers or their employees, the AI-Human Resource
Management (AI-HRM) interface, therefore, seems to be essential for the eective
implementation of AI in organisations (Basu et al., 2022; Makarius et al., 2020;
Moldenhauer & Londt, 2019).
Further investigating AI’s consequences on jobs and skills requires a better under-
standing of the capabilities of this technology (Agrawal et al., 2019), theoretical
(Charlwood & Guenole, in press; Parker et al., 2019) and empirical (European Parliament,
2021) information is still lacking to decipher how managerial skills are likely to be aected
by AI. To date, the literature oers scant information about the interplay between AI and
skills with regard to the managerial category (Charlwood & Guenole, in press; Pereira et al.,
2021), notably when it comes to augmentation (Teodorescu et al., 2021). For example,
while Huang and Rust (2018) have written about how the rise of AI in organisations may
replace some managerial skills, their research did not focus on managers. Similarly, while
Xie et al. (2021) investigated the impact of AI on occupations with dierent levels of
skillsets, managers were excluded from their analysis. Finally, while Huang et al. (2019)
write that “managers must adapt the nature of jobs to compensate for the fact that many
of the analytical and thinking tasks are increasingly being performed by AI,ˮ their analysis
was performed at the macro-level and based on secondary data.
Alongside the existing managerial skills that will be aected by AI, the successful
integration of this technology into companies appears to require new managerial com-
petencies (i.e. Sousa & Rocha, 2019). Since organisations need to reinforce AI capabilities
(Chowdhury et al., 2022), managers seem to play a key role in AI implementation
(Anonymised authors, under submission). This technology indeed requires a variety of
considerations in order to have a positive impact on organisational decision-making
(Leyer & Schneider, 2021; Trunk et al., 2020). When not well managed, AI initiatives have
yielded disappointing operational impacts (The Economist, 2020) or even failed
(Fountaine et al., 2019). Wamba-Taguimdje et al. (2020, p. 1893) suggest that ‘organiza-
tions achieve performance through AI capabilities only when they use their features/
technologies to recongure their processes’. In fact, AI roles seem to be a rather unique
technology, which may require specic skills to be correctly implemented (Makarius et al.,
2020). Since ‘there is very little research on human-machine synergies’ (Malhotra, 2021,
p. 1095), more knowledge on the managerial skills possibly required to optimise the use
of AI in organisations.
Research on AI and people management would indeed warrant further scrutiny (Zhang
et al., 2021) as the already signicant gaps between research and practice may widen
(Cheng & Hackett, 2021). Our investigation of the AI-HRM interface through managerial
skills seems to be particularly timely. At present, practice and research seem to be in
a transitional period prior to dramatic developments in AI (Duan et al., 2019): a pre-
theoretic stage (Von Krogh, 2018) during which scholars can have a major inuence on
2L. GIRAUD ET AL.
how this technology develops in organisations (Charlwood & Guenole, in press). In
attempting to tackle the aforementioned gaps, our article addresses the following
research questions:
Q1. How managerial skills are likely to be aected by AI?
Q2. What managerial skills are needed to optimise the use of AI in organisations?
We highlight the specic interplays between managerial skills and AI. Our timing in
making such a contribution appears to be correct:: while AI has been in existence for over
six decades and has experienced AI winters and springs the rise of super computing
power and Big Data technologies appear to have empowered AI in recent years’ (Duan
et al., 2019, p. 63). Our ndings, thus, update existing taxonomies of managerial skills (i.e.
Fayol, 1916; Gentry et al., 2008). Ultimately, our objective is to guide future studies on the
AI-HRM interface, a new HR eld of action, which will be partly supervised by managers
(Basu et al., 2022).
In the second section of this article, we review the academic literature on manage-
rial skills in relation to AI. The third section outlines the methodology of our empirical
investigation. We present our results in the fourth section, before discussing them in
the fth section. We then conclude our study and suggest paths for future research.
2. Articial intelligence and managerial skills
AI involves computers performing tasks in a way that replicates human intelligence
and behaviour (Fiske & Kazdin, 2000). The boundaries of AI’s performance and scope
have been expanding (Berente et al., 2021) and may now have reached managerial
occupations. In this section, we review how hints from the extant literature already
suggest the dierent possible interface scenarios between AI and managerial skills
(2.1), and which managerial skills seem to be necessary to successfully implement
AI (2.2).
2.1. Interface scenarios between AI and managerial skills
The current literature generally anticipates three major kinds of impacts that AI will have
on jobs, tasks, and skills: the latter can be replaced, augmented, or remain unaected
(Daugherty & Wilson, 2018; Farrow, 2019; Murray et al., 2021; OECD, 2019; Paschen et al.,
2020; Teodorescu et al., 2021). For instance, Hinsen et al. (2022) suggest that individuals
can see their repetitive tasks replaced by AI (through pixie or informant roles) or that AI
can be a colleague to augment skills by delivering an informative, assisting, or consulting
result.
AI would rst be capable of augmenting human tasks that remain too complex to be
replaced. An optimised managerial decision or specic skill could then be based on an
automated AI pre-analysis. This augmented output would be reached thanks to optimised
collaboration with the machine, which could take the form of a hybridisation of managers
(Moldenhauer & Londt, 2019), potentially through symbiotic metamorphosis (Makarius
et al., 2020).
JOURNAL OF DECISION SYSTEMS 3
Because AI can handle simple and repetitive cognitive tasks (Decker et al., 2017), it may
also take over most administrative (Kolbjørnsrud et al., 2016), analytical, and thinking skills
(Huang et al., 2019). The next step may be algorithmic management, referred to as ‘using
AI or algorithms to perform such functions as assigning tasks to workers, determining the
best way to complete these tasks, determining how much a worker should be compen-
sated for the task, and even evaluating the worker’s performance of the task’ (Harms &
Han, 2019, p. 74). AI support for repetitive tasks may eventually provide employees and
their managers ‘more opportunity to focus on work which requires their core competen-
cies’ (Hagemann et al., 2019, p. 160).
Finally, some skills might neither be replaced nor augmented because of their extreme
complexity like emotional intelligence (Mattingly & Kraiger, 2019) and more ‘“abstract”
managerial skills – critical and system thinking skills’ (Su et al., 2021, p. 341). Those skills
would therefore remain unaected by AI because it is ‘typically limited to a single frame or
type of problem’ (Lu et al., 2018). Therefore, for the time being, AI does not seem capable
of skills, such as creativity, empathy, judgement, storytelling, and giving motivational
speeches (Plastino & Purdy, 2018; Wilson et al., 2017). It also lacks imagination, such as
anticipating questions to ask or conceptualising something that does not yet exist
(Rometty, 2016).
2.2. Managerial skills to facilitate the implementation of AI
The literature suggests that introducing AI in organisations triggers the need for specic,
or even new managerial skills (Kietzmann & Pitt, 2020) to face unprecedented technical
and managerial demands like the ethical challenge, the shortage of machine-learning
engineers, the data-quality challenge, and the cost-benet challenge (Lee & Shin, 2020).
Additionally, a certain level of organisational maturity is probably needed before general-
ising AI (Lichtenthaler, 2020), in both its technical and non-technical dimensions
(Gunsberg et al., 2018). Indeed, Harrison and O’Neill (2017) explain that AI cannot be
implemented if the organisation lacks data automation, structured analysis, or other key
factors. To sum up, a successful introduction of AI that would provide business value
seems to depend on numerous organisational inhibitors and enablers that managers
need to deal with: organisational culture, top management support, organisational
readiness, employee-AI trust, AI strategy and compatibility (Enholm et al., 2021).
Therefore, managerial skills may no longer be only about planning, organising, leading,
and controlling the human workforce (Fayol, 1916) but also about AI and the interface
between the two (Basu et al., 2022). Managers might then have to weigh the human to AI
workforce ratio (Farrow, 2022) and make decisions about three related interdependent
facets of AI – autonomy, learning, and inscrutability (Berente et al., 2021). As AI-enabled
innovations can destruct or enhance skills (Paschen et al., 2020), they may even change
the forms of conjoined agency between humans and technology (Murray et al., 2021), and
more generally the nature of work and collaboration (Griva et al., 2021). Therefore, specic
skills seem to be needed in order to optimise the integration of AI into organisations
(Gobeil Proulx, 2021), notably for managers (Makarius et al., 2020). They are indeed central
to the strategic competitive advantage potentially provided by AI’s self-learning capabil-
ities (Jarrahi et al., in press).
4L. GIRAUD ET AL.
So far, the literature suggests that managers play a key role in the implementation of AI
(Sousa & Rocha, 2019), which requires new skills to identify the rationale for using AI (Fiore
et al., 2018), including analyses of the costs, benets, and business cases. It is also
important to understand cognitive technologies, guide the implementation process
through to adoption (Henke et al., 2018), and evaluate any misleading production result-
ing from the application of AI (Schrage, 2018).
Specically, researchers are getting interested in how to train managers in basic AI
management and working cooperatively with this technology (Dejoux & Léon, 2018;
Norman, 2017). As such, numerous business schools, such as the MIT Sloan School of
Management provides online programmes on AI’s implications for business strategy.
1
Individuals, such as managers, are encouraged to strengthen their technical skills
(e.g. information evaluation and analysis, AI knowledge, data visualisation, business
acumen, and complex problem solving) as well as their non-technical skills (e.g.
emotional intelligence, communication, negotiation, creativity, cognitive exibility,
entrepreneurial mindset, and data ethics; Britton & Atkinson, 2017; Danysz et al.,
2019; Geraghty, 2019; Gobeil Proulx, 2021; Maguire, 2014; Marion et al., 2020; Mikalef
et al., 2022; Moldenhauer & Londt, 2019; Parker & Grote, 2019; Sousa & Wilks, 2018).
These non-technical skills appear to be necessary to deal with the tensions that AI
makes on the workforce through the possibility of continuously changing ways of
working (Goto, 2022). This technology might even jeopardise the human capital by
triggering adverse reactions like fear (Papacharissi, 2018) or job burnout (Kong et al.,
2021).
Åkerblad et al. (2021) recall that AI-powered practices provoke a paradigmatic shift in
the relationships between humans and machines by profoundly changing organisational
structures, communication, aordances, and ecosystems. AI then redenes the way
people work and collaborate (Budhwar & Malik, 2019), and thus what managerial skills
are required (Budhwar & Malik, 2020), including new roles as trainers, explainers, and
sustainers to ensure eective collaboration with AI (Wilson et al., 2017). Moreover, there
may be multiple managerial leadership styles to implement AI (McCarthy et al., 2021) that
must be adapted to the proles of the change recipients (Frick et al., 2021). From the
perspective of long-lasting integration of AI systems and employees, scholars have
stressed the role of managers to guarantee AI socialisation (Makarius et al., 2020) and
trust (Boni, 2021; Glikson & Woolley, 2020) with this new technology.
If AI can make predictions in an abundant and inexpensive way, managers need to
decide how to best implement such predictions, which will involve using their judgement.
This judgement comes from knowledge of the organisational history (Kolbjørnsrud et al.,
2016), a dimension that AI usually fails to consider and which should be monitored by
humans. Managerial training might then shift focus from prediction-related skills to
judgement-related skills (Pistrui, 2018). Organisations may continue to need managers
who can make responsible decisions using ethical judgement and emotional intelligence
to engage customers as well as employees and to identify new business opportunities
(Agrawal et al., 2017). Consequently, future managerial skills may focus on maximising the
predictive capabilities of AI, such as identifying what should be predicted, how to max-
imise information in the predictions, and how to improve them over time (Agrawal et al.,
2017).
JOURNAL OF DECISION SYSTEMS 5
While some scholars have attempted to guide managers in the implementation of AI
(Heavin & Power, 2018), information on the AI-HRM interface remains scant in the
literature (Basu et al., 2022; Minbaeva, 2021) provoking a ‘growing concern that research
on AI could experience a lack of cumulative building of knowledge’ (Collins et al., 2021,
p. 1). The knowledge gap seems to be particularly profound regarding the specic
relationships between AI and managerial skills, in contrast to the signicance of introdu-
cing AI (Ågnes, 2022), which needs to be trusted (Ramchurn et al., 2021; Razmerita et al.,
2021). Expanded with self-insight roles, managerial skills can be conceptualised and
operationalised through the framework of Gentry et al. (2008). In this study, we consult
AI experts through this framework in order to explore the particular interplays between AI
and managerial skills.
3. Methodology
Study 1 used an exploratory research method, collecting data from semi-structured
interviews with 40 AI experts. These initial ndings were then tested using descriptive
statistics collected from an additional 103 AI experts (Study 2), similar to what other
researchers investigated on complementary AI topics (Grace et al., 2018; Müller &
Bostrom, 2016).
By combining the quantitative data from descriptive statistics with the qualitative
information obtained during the interviews (Eisenhardt & Bourgeois, 1988), we ensure
a more holistic understanding of the studied phenomenon (Baxter & Jack, 2008), which in
this instance is the AI-HRM interface, a quite unchartered eld of research to date (Basu
et al., 2022).
3.1. Qualitative data collection (study 1)
We adhere to Yin’s (2014) argument that the ‘how’ questions should be addressed
through qualitative research methods. Data were collected in the summer of 2019,
after and during a time of rapid AI development (Duan et al., 2019). Two researchers
from our team led face-to-face interviews in English. Each interview lasted approxi-
mately one hour and the audio was recorded (with the participants’ permission) and
then transcribed.
To achieve diversity in terms of professional experiences, gender, country of residence,
and other criteria, we used purposeful sampling (Silverman, 2004). The sampling of
a variety of management levels, sectors, and countries should increase the transferability
and dependability of our results (Guba & Lincoln, 1994). Appendix 1 shows the non-
probability sampling of participants, which is particularly suited to our exploratory pur-
poses (Saunders, 2012).
We used NVivo 12 software, a common tool in business research (Bell et al., 2018),
which can be used to perform a thematic content analysis of a complete interview
transcript, as recommended by Roulston (2014). This method of analysis is the most
common in organisations and best suited to these kind of data (Symon & Cassell,
2012).
6L. GIRAUD ET AL.
To ensure qualitative rigour in this inductive research, coding was used to analyse the
data extracted from the interviews (Gioia et al., 2013). As Saldaña (2013) writes, ‘a code is
a researcher-generated construct that symbolizes and thus attributes interpreted mean-
ing to each individual datum for later purposes of pattern detection, categorization,
theory building, and other analytic processes’. The coding technique we used was
sequenced in dierent steps.
First, we conducted open coding or segmenting of the data into units of mean-
ing (Goulding, 1999). In this case, the data were represented by verbatims directly
extracted from the interview transcriptions, as shown in column one of Table 1.
Those units or pieces of data were then labelled with more descriptive tags to
facilitate subsequent coding. Next, we built categories through axial coding (Corbin
& Strauss, 1990). The objective of this phase was to assemble units of meaning into
more abstract codes or categories. Table 1 illustrates these categories (such as
‘Simple decision-making’), which entails dierent units than those in the rst step
of the analysis, yet with similar meaning. The third and nal step was to create
theoretical dimensions to integrate the categories from the second step. The
theoretical dimensions were divided into two main sections, according to our two
research questions: (1) AI’s direct impacts on managerial skills, and (2) managerial
skills to optimise the use of AI. In the Results section, Figure 1 shows the summary
of the results as well as all the categories and theoretical dimensions which have
been identied in our data.
Finally, interviews were conducted up to saturation point, where collecting additional
data showed no further explanatory insights (Charmaz, 2006). In other words, data
saturation refers to the tipping point where new data emerge to provide new ndings
(Bowen, 2008; Gioia et al., 2013).
Table 1. Data coding examples.
Verbatim
Descriptive
tags
Categories
(Axial
coding) Theoretical dimension
(#21) ‘With a system of algorithm and
comparison, it will give you the compatibility
of the profile of the candidate with the team
occupation in which he can fit’.
Candidate
profiling
Recruitment Managerial skills likely to be
augmented by AI (AI direct impacts
on managerial skills).
(#5) ‘AI can get more relevant information
faster so that it helps you to keep up to date
and to potentially learn the job faster’.
Relevant
information
regarding
the job
Knowledge
of job
and
business
Managerial skills likely to be
augmented by AI (AI direct impacts
on managerial skills).
(#30) ‘Paperwork can be replaced by AI’. Simple task Simple
decision-
making
Managerial skills likely to be replaced
by AI (AI direct impacts on
managerial skills).
(#24) ‘[AI] will reproduce what we tell it to say,
what we tell it to do, it will not invent’.
Creation of
new ideas
Imagination Managerial skills likely to remain
unaffected by AI (AI direct impacts
on managerial skills).
(#3) ‘It will always be a human behind to
control what the AI does, to adapt it’.
Need of
human
judgement
Judgement
and
ethics
Non-technical managerial skills to
optimise the use of AI (Managerial
skills to optimise the use of AI).
(#4) ‘There is a resistance in not knowing how it
works because it might not work [. . .] even
man can have a failure’.
Fear of failure Risk taking Non-technical managerial skills to
optimise the use of AI (Managerial
skills to optimise the use of AI).
JOURNAL OF DECISION SYSTEMS 7
3.2. Descriptive statistics (study 2)
Through descriptive statistics, Study 2 aimed to test whether he Study 1ʹs results
could be generalised and to assess the priority of each managerial skill to develop
and to optimise the use of AI. In this regard, a self-administered questionnaire was
emailed to specic AI experts (Saunders, 2012) in 2020to provide more accurate
answers (Bryman & Bell, 2015).
We used a non-random sampling approach and we limited our scope to respon-
dents with specic AI expertise. Seeking to address the research questions eec-
tively, the respondents of the questionnaire have a diverse range of AI-related
academic and industrial backgrounds. The questionnaire was completed online by
inviting specic respondents belonging to four categories (professors, associate
professors, experts, and CEOs). Respondents were initially identied through their
connection to the AI-HRM interface or through their participation in related aca-
demic and professional conferences. We received 103 answers out of 1,500 LinkedIn
survey invitations, which is a 9% response rate. Table 2 presents the details of our
quantitative sample.
Respondents were asked to classify the same managerial skills as for Study 1. Each
category’s sense of belonging was estimated through a likelihood expressed on a 5-point
Likert scale. The same procedure was used to assess the order of priority for managerial
skills to be developed to optimise the use of AI.
At the end of the Results section, we will integrate the results and the mean supporting
scores for the retained categorisation – as well as the priority of each managerial skill that
should be developed to optimise the use of AI.
Figure 1. A1 impacts on managerial skills.
%N = Proportion of Study 2 respondents who confirmed the managerial skills classification obtained
after study 1. = Mean scores of probability or priority levels as reported by study 2 respondents.
8L. GIRAUD ET AL.
4. Results
In line with our research questions, we rst present in Figure 1 the results collected from our
interviewed respondents (Study 1) about (1 – left column) how the rise of AI is likely to aect
managerial skills and (2 – right column) which skills should be developed to accompany the
growing presence of AI in organisations. Our results in the left column are structured
according to the dierent ways our respondents see the managerial skills being aected
by AI; whether those are likely to be replaced, augmented, or remain unaected.
4.1. Results of study 1
Within this section, the chapter 4.1.1 gathers illustrative verbatims which support our
qualitative ndings about AI direct impacts on managerial skills (which appear on the left
column of Figure 1). Then, the chapter 4.1.2 displays the collection of verbatims about
Managerial skills that optimise the use of AI (which appear on the right column of Figure 1).
4.1.1. AI direct impacts on managerial skills
Managerial skills likely to be augmented by AI
Complex decision-making
Our respondents point out that AI can quickly identify certain problems, analyse gathered
information, make predictions based on history, and propose solutions and actions. As the
following responses indicate, AI might signicantly improve business performance and
enhance complex decision-making:
So, what is happening is that digitization is helping the managers to make more, you know,
database decision-making. (#12)
Table 2. Quantitative sample (Study 2).
N = 103 Percentage
Sector University 73 70%
Industrial 9 9%
Banking 3 3%
Services & Others 18 18%
Educational level PhD 78 76%
Postgraduate 21 20%
Undergraduate 4 4%
Organizational size 10–500 32 31%
501–1000 11 11%
1001–100,000 48 47%
>100,000 12 11%
Occupational tenure 1–4 38 43%
5–10 25 28%
11–42 26 29%
Experience in the AI field (Year) 1–4 44 43%
5–10 22 21%
11–45 37 36%
Gender Male 80 80%
Female 23 20%
Age 20–30 18 19%
31–50 53 55%
51–70 25 26%
JOURNAL OF DECISION SYSTEMS 9
So, the manager has some time to focus on the real decision based on the evidence that
a good AI brings to him. (#37)
Innovation
Although creativity is not yet replicable via AI, our results suggest that systematic
correlation analysis can provide managers with new perspectives to consider, which can
improve human innovation.
There are some specic areas where AI has the potential to augment ideation and open up
areas of a solution space that humans might not have thought of. (#19)
‘Knowledge of jobs and business
According to our data, AI can help increase job-specic and business acumen. It can also
gather information quickly to make predictions based on history.
So, certainly I can have AI-based sensing mechanism and I can actually understand my
business, my competitors and the industry trends, which is actually, you know, the AI-
based analysis. [. . .]. So, we can get a lot of these best practices from other companies, we
can get, you know, a lot of the, I would say the best practices of the industry and help
managers to get more, I would say, targeted knowledge related to their job inuence.
(#12)
AI can get more relevant information faster so that it helps you to keep up to date and to
potentially learn the job faster. (#5)
Recruitment
According to our respondents, AI can play an important role in assisting hiring managers
with candidate screening, interview scheduling, and communication throughout the
recruitment process.
With a system of algorithm and comparison, it will give you the compatibility of the prole of
the candidate with the team occupation in which he can t. (#21)
AI can assist in hiring processes by identifying candidates from large databases, though
respondents note ethical considerations, specically regarding diversity.
Regarding if I hire someone or I don’t? Yes, this is done already. [. . .] But the same problem.
This tool that looks amazing and saves time, it is based on what? On the databases that you
already have, the employees that you have recruited in the past. (#8)
I think that it should be a human-reviewing algorithmic decision, or everything relating to
kind of selecting, developing and, you know, promoting people. (#17)
Time management
Respondents suggest that AI can assist managers in scheduling. Project management may
also continue to be impacted by AI:
AI can actually prioritize my work based on [. . .] my corporate or business objectives. And I am
actually doing more time management for my high priority items. (#12)
10 L. GIRAUD ET AL.
Yes, [time management can be improved] with a software that will allow tracking the time
spent on a project, an action, and tell you that this should be your priority. (#10)
Coping with pressure
AI can provide information to managers that could be helpful for dealing with pressure.
If AI can suggest the degree of risk or where there is risk it might be really helpful in keeping,
of course, the interior, lowering the pressure regarding the understanding. (#30)
AI is good, because sometimes as a human, when you are under pressure, you don’t see many
options, or you cannot think of that, maybe. But if they use the AI [. . .] It presents options,
I think. Yes, this denitely helps. (#37)
Communication
Though respondents indicate that AI can enhance communication via relationships and
translation, they doubt its ability to communicate eectively without human support.
Relationships
Our respondents conrm that AI can improve communications by providing valuable
background information about people.
When we meet for the rst time and I have some background [. . .], I think that will help the
conversation get started. (#15)
AI could also help managers develop their communication skills by providing feedback on
social interactions.
I think the manager will be able to give a topic or something and the AI will be able to
generate a structure, a piece of text, a speech. (#37)
So, I can imagine that if you look at the distribution of communication skills, especially written
communication skills, there’s a lot of people that write very poorly. If you can bring them up
10 or 20%, by augmenting them that could be valuable. (#19)
Translation
Our respondents agree that AI is increasingly advanced in language translation. AI is thus
likely to assist managers in communicating with foreign team members. Although
respondents remain sceptical of AI’s value in oral translation, they believe that written
translation can be signicantly improved.
[AI can] correctly translate from one language to another so that you don’t have translation
gap [. . .] for improving the communication in between teams. (#4)
[. . .] nowadays, if you take a look at the machine learning, machine translation papers,
machine translation conference, almost everything is about deep learning. So, this deep
learning thing has changed the elds a lot, in a lot of cases. It’s in Google Translate, it’s being
used everywhere. (#37)
JOURNAL OF DECISION SYSTEMS 11
Managerial skills likely to be replaced by AI
Information gathering
Respondents suggest that managers might increasingly struggle to search, gather, and
compute an ever-growing amount of information – all skills with which AI computing
capacities and training models may help. Illustrative remarks are as follows:
A man will not be able to go through terabytes of data, but AI can. So, it’s just a tool to gather
information altogether to gure out the main points and maybe which article is the most
valuable to read. (#4)
Obviously, when you’re looking for the patterns in large quantities of information, and
probing and seeking information, that’s something for which AI can do an enormous amount
of the legwork. (#19)
Simple decision-making
Our respondents also indicate that simple decision-making may be outsourced to AI,
which can do these tasks more eectively. Respondents mentioned simple tasks related
to logical sequences of actions that could be programmed and automatically performed.
I think that in the future, the tasks that are repetitive, time consuming, automatable, will be
done by AI. (#10)
We still have human decisions to make and, in some cases, it would be replaced because the
task is simple enough and the AI is able to do it. (#4)
According to our data, basic administrative skills are likely to be replaced by AI; examples
include organising calendars and schedules, planning for meetings, and coordinating
these tasks with room availability or other factors.
Paperwork can be replaced by AI. It should be better for the worker, I think. (#30)
I think that everything that is administration, AI can help. [. . .] There is the more and more
software that can help managers or administration people. (#10)
As indicated by the following responses, AI integration may allow more time for managers
to work on their core value-added tasks.
Because all simple tasks, let’s say, will be done by the AI. So, people will be pushed to learn
higher skills in order to have an added value in the work. (#26)
The expert can focus on their real core of this task, and they can delegate some tasks that take
time [. . .] to an AI. (#37)
Managerial skills unlikely to be replaced by AI
Our respondents agree that some managerial skills will remain untouched by AI, such as
those related to imagination and leadership.
12 L. GIRAUD ET AL.
Imagination
Our experts believe that AI does not innovate or invent directly but instead it just
replicates or executes algorithms based on the provided data.
[AI] is not going to do something innovative because you want to do something to make
some innovation. You’ll have to break the rules, not follow the rules, I think. (#38)
[AI] will reproduce what we tell it to say, what we tell it to do. It will not invent. (#24)
Leadership
According to our data, AI should not be able to perform a leading or inuencing role in
relation to people.
Leadership and all is purely human, so using AI for that is very far o, because the human still
has to keep the hand and the leadership. (#1)
[. . .] I don’t think the AI will have direct inuence on your leadership. (#26)
4.1.2. Managerial skills that optimise the use of AI
In this section, we present several technical and non-technical managerial skills that
optimise the implementation and utilisation of AI.
Technical managerial skills that optimise the use of AI
As respondent #18 notes, ‘data scientists are spending their time educating business partners
and not doing their job’. Therefore, managers ought to acquire specic skills, notably from
a technical point of view, so that AI experts can focus on their core added value.
Basic AI knowledge
Our respondents agree on the importance of managers having a basic understanding of
AI, including its mechanisms, potential, and limits. The interviewed AI experts insist that,
at a minimum, a basic knowledge of logic should be taught.
[Managers] need the extra education on how to actually implement and grow a part of this
digital transformation. (#29)
I think, you know, it might be a good idea in the industries and all the managers are exposed,
more, you know, to some kind of a basic AI training. (#12)
Some respondents feel that managers do not need to master advanced computer
programming or coding, as this part will be handled by data scientists.
[Managers should have] knowledge of AI, not in-depth but to understand it and be able to
discuss with an AI specialist and design the needed system. (#8)
JOURNAL OF DECISION SYSTEMS 13
Data and algorithmic inputs are created by humans, so there is a risk of errors and bias.
Our respondents agree that managers need to understand that AI is not unbiased just
because it is a machine. On the contrary, AI is likely to acquire bias from the data and
training it receives from humans. It is therefore important to understand, verify, and
monitor AI to avoid potential misuse.
You probably need to understand what data has been trained on, what bias is introduced,
and what are the risks. (#5)
So sometimes [managers] misunderstand the kind of data they need to collect. [. . .], unless
they understand the technology side, they don’t understand the limitation. (#7)
Ability to define needs and business cases
Managers seem to be the best positioned to identify where and how AI can provide value
to their activity. Our respondents recall the necessity for human intervention in the
process of dening the business case.
Managers need to know how we can use IA for our needs, to analyze needs, and in which
occupation we can use it. (#22)
We have to show the limitations of the AI but also the evolution or growth of the AI, so we
have to manage expectations, communicate that AI cannot do everything [. . .]. (#32)
Non-technical managerial skills that optimise the use of AI
Judgement and ethical decision-making
Interviewees stress the importance of judgement and ethics regarding AI usage. Human
judgement ensures relevant implementation of AI and an appropriate response to mana-
ging ethical issues and sensitivities related to the use of AI.
We cannot trust the tool 100%. But there’s always specic human conditions that generate
output that are dierent from what the logic wants. (#6)
There will always be a human behind to control what the AI does, to adapt it. (#3)
So there may be some push back, or there needs to be some oversight, to make sure that
there are no hidden vices in the training data, so then you’re not getting biased results. (#29)
Basically, data was really biased, so it’s like you build tools, and if you rely blindly on the tools
without really understanding, it can do really stupid things, right? (#5)
Risk taking
According to our data, managers need to accept the risks associated with AI. This
technology does not grant immediate and accurate results: it requires time to learn and
adjust.
There is a resistance in not knowing how it works because it might not work [. . .] even man
can have a failure. (#4).
14 L. GIRAUD ET AL.
[Managers need to] take more risk and know more deeply about technology. (#36)
Open-mindedness
Our respondents believe that being exible and open-minded can facilitate acceptance
of AI.
Now, if you want to make a good use, you must try this ability, to challenge your humility,
develop your exibility, your openness of mind. (#21).
I think that there is still a lot of education needed on the company side, to embrace AI [. . .].
(#37)
Organisational change management
Respondents point out the need to manage the basics of organisational change manage-
ment to successfully implement AI. Managers are also expected to develop leadership
skills to drive the associated transformations.
We need to put in place the elements of change management. (#2).
I explain to managers that [. . .] the job market has evolved. So, our way of doing things, our
working methods, our diagnostic methods must evolve over time. (#21).
[AI] can replace another job. (#27).
I think it’s a bit of fear, bit of pride [. . .] not going to let the machine take my job. (#26).
[Managers] don’t really believe in numbers. I think numbers could be better than their feeling,
their intuition, and their knowledge of the market and of the business. So, it’s really a whole
cultural reprogramming to actually imagine that AI will really help their business better than
they would on their own. (#24).
Several AI experts suggest that the best tactics for implementing AI may require extensive
argumentation through communication, potentially supported by training.
[A tactic to implement AI would be] to accompany the people in training and to convince
them to put things in place. (#2).
Because there are people who, especially here in Japan, they are against change, not only
against AI but for other things and if you teach that, if you attack there; I think they will be
more open to accept. (#37).
Multidisciplinary collaboration
Our respondents add that the business case should probably be dened by managers and
data scientists with a collaboration process involving parties from dierent backgrounds.
We would need to have a strong connection between the employee who knows the business
and the one who can apply this technique. (#4).
It’s complicated: an engineer with mathematicians is complicated. Mathematicians with
experts on the eld are still very complicated, too. So here, we have three phases, three
dierent types of population to communicate with. It is the problem of background. (#25).
JOURNAL OF DECISION SYSTEMS 15
4.2. Results of Study 2
The information provided by Study 2 is also included in Figure 1 for a more comprehen-
sive view of our overall results (Åkerblad et al., 2021). %N corresponds to the proportion of
respondents from Study 2 who conrmed the managerial skill classication obtained after
analysing and synthetising the results of Study 1. Scores below 50% were not retained for
analysis and are not displayed.
�
x translates the mean probability scores or priority levels as reported by respondents of
Study 2 about the managerial skills identied in Study 1. �
x Probability and mean priority
scores were measured by Study 2 respondents on a Likert (1932) scale ranging from 1 to 5.
As an example, the rst line at the top left reads as follows: ”76% of Study 2 respondents
conrmed that communication would be a managerial skill likely to be increased by AI,
with a probability of 4.3/5.” In both columns of Figure 1, the centrality of managerial skills
is ranked according to this �
x mean score.
Our results show that most managerial skills are likely to be augmented by AI, in order
of likelihood: communication, recruitment, complex decision-making, innovation, time
management, knowledge of job and business, coping with pressure. The managerial skills
related to information gathering or simple decision-making (i.e. administrative) tasks
could be completely replaced by AI. Meanwhile, only a few managerial skills would remain
intact in the wake of AI (leadership and imagination). Finally, our data shows that
technical as well as non-technical skills seem to be necessary for the successful imple-
mentation of AI in organisations. The technical skills that are top priority for managers are
understanding what an algorithm is and understanding how AI can add value to their
business. The non-technical skills that managers need as a priority are ensuring good
judgement as well as ethical data collection and usage, facilitating multidisciplinary
collaboration, managing the potential reorganisation around AI, and showing open-
mindedness and risk-taking capacities.
5. Discussion, implications, & limitations
Our study aimed to answer calls from scholars regarding further investigation into the
links between AI and skills with empirical elements and regarding the future of work (i.e.
Dennehy et al., 2021). The data collected has allowed us to identify the most plausible and
major upcoming interplays between AI and managerial skills. First, AI is likely to augment
most managerial skills. Second, technical and non-technical managerial skills will probably
have to be developed in order to optimise the use of AI in organisations.
Our results specically show which managerial skills are likely to be replaced, augmented,
or likely to remain unaected, conrming the relevance of those categories when it comes
to qualifying AI’s eects on jobs, tasks, and skills (Daugherty & Wilson, 2018; Farrow, 2019;
Murray et al., 2021; OECD, 2019; Paschen et al., 2020; Teodorescu et al., 2021).
Our respondents agree that the managerial skills to be augmented by AI actually cover
a large spectrum from planning, organising, and controlling (Fayol, 1916; Robbins &
Coulter, 2012), to self-insight roles like self-management and self-development (Gentry
et al., 2008). Yet, Raisch and Krakowski (2021) still argue for a change of perspective on
these categories as, in the management domain, augmentation cannot be neatly sepa-
rated from automation.
16 L. GIRAUD ET AL.
Because managerial jobs are complex, involving analysis, solving advanced operational
problems, and human relations, the experts sampled conrm that few managerial skills
are eligible for complete replacement by AI (Huang et al., 2019). Our respondents conrm
that AI has already replaced managers for providing information and simple decision-
making (Harms & Han, 2019). This nding is consistent with the work of Lichtenthaler
(2018) who has categorised automation jobs involving limited complexity as a ‘substitute’
matrix that replaces human work with increased eciency. Therefore, AI may increase
managers’ opportunities to focus on work requiring their core competencies in order to
better participate in value creation (Hagemann et al., 2019), even though our respondents
add that some specic managerial skills will then be needed for successful AI
implementation.
Our respondents add that AI might never replace advanced managerial skills such as
imagination and leadership. Imagination, including tasks such as thinking of questions to
ask and imagining something that does not yet exist, may not be replicable by AI
(Rometty, 2016). Agrawal et al. (2017) have also noted that AI is not creative enough to
nd new opportunities by itself. In that sense, we can conrm that storytelling and
motivational speeches are unlikely to be duplicated by AI (Lichtenthaler, 2018; Wilson
et al., 2017). The experts we interviewed also indicate that AI may be unable to lead
humans because of the inherent diculty it has generating ideas ex nihilo and dealing
with employees’ trust or emotions (Ferràs-Hernández, 2018; Solberg et al., 2022). Skills
pertaining to leadership should then remain a core managerial and human competency
unaected by AI, which is consistent with previous ndings (Agrawal et al., 2017; Manyika
& Sneader, 2018).
In addition, our respondents recall that human decision-making based on AI will
probably require managers to acquire new skills, as already observed in other occupations
(Lindebaum et al., 2020). While the literature has previously investigated the topic from
a macro point of view (Huang et al., 2019), our results provide specic information about
managers.
Our data suggest that managers should rst acquire technical skills like basic AI
knowledge (Dejoux & Léon, 2018) as well as the ability to dene when and how AI
could be helpful for their activity. Our results conrm that managers play a key role in
identifying the rationale for using AI (Fiore et al., 2018), analysing business cases (Henke
et al., 2018), weighing costs and benets as well as spotting any misleading conclusions
that could be produced by AI applications (Schrage, 2018).
Moreover, managers are invited to onboard non-technical managerial skills in order to
optimise AI implementation. According to our respondents, a good AI prediction still
requires good managerial judgement. This nding is consistent with the literature stating
that AI usually lacks good judgement (Agrawal et al., 2017) and that managerial training
may shift from focusing on prediction-related skills to judgement-related skills (Pistrui,
2018). Thus, our respondents agree that nal decisions related to critical issues are
unlikely to be replaced and should be handled by humans (Neubert & Montañez, 2020).
We conrm that future managerial skills are likely to be about determining how to best
apply AI to making predictions and what should be predicted (Agrawal et al., 2017). Our
respondents also reiterate the necessity for managers to maintain a clear sense of ethics
to prevent AI bias and misuse (Mikalef et al., 2022).
JOURNAL OF DECISION SYSTEMS 17
Our results corroborate that managers must develop the necessary skills to dene new
job descriptions and organisational structures required by AI (Knickrehm, 2018). This
technology may lead to stronger multidisciplinary collaboration. Our experts agree that
clear role descriptions and task separation may be needed in order to obtain an eective
AI-HRM interface as humans team up with machines (Basu et al., 2022). Our research
therefore conrms that companies adopting AI should further consider the associated
relational and structural complexities.
Our respondents also conrm that AI may radically change power relationships
(Moldenhauer & Londt, 2019; Sousa & Wilks, 2018). Thus, our results suggest that managers
may be in charge of dealing with AI-induced changes, including power recongurations
(Sousa & Wilks, 2018). In fact, knowing that technical changes are occurring at a fast pace
(Sousa & Wilks, 2018), our respondents agree that the development of organisational
learning appears to be a prerequisite for the successful implementation of AI (While, 2019).
In conjunction with the increased need for organisational change management skills,
which could facilitate commitment to AI (Adil, 2016) and reduce resistance to this new
technology (Abraham et al., 2019), our respondents add that managers need to be open-
minded and keener on taking risks. Building and maintaining trust with a risky tool
therefore seems to be a priority (Shestakofsky, 2017). While AI remains costly, this
technology does not always yield immediate results as it often requires learning and
adjustment. Overall, our respondents conrm that AI’s success requires preliminary
socialisation with this technology (Makarius et al., 2020).
Last but not least, our study also reveals the new extended scope of managerial skills
and suggests that the socio-technical capital of managers will become more strategic
(Makarius et al., 2020) to optimise organisational processes through an eective synergy
between employees, managers and the machine (Maguire, 2014). The AI-HRM interface
will thus become a new territory of action for HRM (Basu et al., 2022) to be partly
delegated to operational managers who then reinforce their central position in organisa-
tions (Anonymised authors, under submission).
5.1. Implications for research
First of all, our research contributes to the emerging literature on the AI-HRM interface
(Basu et al., 2022) by providing empirical information about the interplay between AI and
managerial skills. We list the exact technical as well as non-technical skills which add to
the already identied mediators (Fountaine et al., 2019) and moderators (Parent-
Rocheleau & Parker, 2021) of the success of AI initiatives. Our data also conrm that the
task and skill perspectives (Huang & Rust, 2018; Vrontis et al., 2021) are particularly
appropriate when studying AI’s impact on managerial jobs.
Second, we explored the possible interactions between AI and managerial skills as well
as the subsequent impact on the reengineering of collaboration schemes among employ-
ees, managers, and data scientists. The implementation of a human-machine interface
raises new issues for managers to handle, such as ‘how do new technology workers
eectively work together with other organizational members?’ (Kim et al., 2021, p. 14).
This study provides preliminary responses to this line of inquiry. While we had initial
information about the importance of managers when it comes to successfully
18 L. GIRAUD ET AL.
implementing AI in organisations (Anonymised authors, under submission), our data
uncover the underlying mechanisms at work in order to grant the added value of AI to
business (Griva et al., 2021).
Third, we also specify which managerial skills are likely to be aected by the imple-
mentation of AI and how. To date, the relationships between AI and managerial skills had
not been properly investigated as such and remained scattered in the literature
(Charlwood & Guenole, in press; Pereira et al., 2021). Furthermore, as little remains
known about the way AI could be integrated into people management (Minbaeva,
2021), investigating AI’s capabilities was therefore encouraged to anticipate the nature
of its impacts on occupations (Agrawal et al., 2019), which is what we specically explored
for managerial jobs. As a consequence, our work enriches the extant taxonomies of
managerial skills (i.e. Bhanugopan et al., 2017; Fayol, 1916; Gentry et al., 2008;
Mintzberg, 1973) with an updated list of technical and non-technical skills to ensure the
optimised use of AI.
Fourth, our research shows that only two managerial skills (i.e. imagination and leader-
ship) might remain untouched by AI. These two skills are likely to stay under the purview of
human managers, and human training on these skills should remain relevant for the
foreseeable future. These two exceptions notwithstanding, our results indicate that most
managerial skills will eventually be augmented by AI, as the technology continues to grow
and enhance most aspects of managerial work (Frey & Osborne, 2017; Huang & Rust, 2018).
Fifth, our results add to the AI-HRM literature by clarifying how AI may improve the
socio-technical capital of managers (Makarius et al., 2020) as well as their cost eective-
ness (Malik, Budhwar et al., 2020) through potential human-machine synergies (Malhotra,
2021). This evolution might result in a more radical transformation of managerial jobs
through hybridisation (Moldenhauer & Londt, 2019) or symbiosis (Makarius et al., 2020).
Accordingly, the introduction of AI into organisations may raise questions about (1) the
conceptualisation of AI managerial skills and human managerial skills as well as of (2) their
dierent possible congurations in terms of combination. Our insight adds to the current
pre-theoretic debate (Charlwood & Guenole, in press; Von Krogh, 2018) about commonly-
accepted denitions of AI (Collins et al., 2021) and the AI-HRM interface (Basu et al., 2022;
Vrontis et al., 2021).
Sixth, our ndings substantiate the relevance of the dierent NHCT postulates
when applied to the introduction of AI in organisations. First of all, our results
conrm that technological change can lead to improvements in managers’ produc-
tivity: AI can, for instance, replace simple managerial tasks and perform them faster.
Second, our paper conrms that the demand for skills and human capital increases
during a signicant technological change like AI integration (Acemoglu & Restrepo,
2019), especially for highly qualied jobs like managers (Bartel & Lichtenberg, 1987;
Wozniak, 1984, 1987). While shortages of data scientists and qualied managers who
can implement AI are likely to threaten the success of AI initiatives (Baldegger et al.,
2020), the NHCT recalls that the demand for highly educated employees should
decline over time as the experience with a specic technology increases (Bartel &
Lichtenberg, 1987). Third, our data conrm that managers will have to sustain ups-
killing eorts, as AI is an evolving technology. The literature suggests that we can be
optimistic about this regular learning since NHCT posits that highly qualied
JOURNAL OF DECISION SYSTEMS 19
populations like managers are more likely to adopt technological change and new
skills (Bartel & Lichtenberg, 1987; Wozniak, 1984, 1987). Yet, it would be interesting
to check if managers (and their team) can easily keep up with AI updates.
Seventh, our results conrm that with the advent of AI, managers ”will need to be more
skilled than ever before” (Gratton, 2016, p. 8). While previous scientic observations suggest
that only jobs with low skill intensity are acutely vulnerable to AI implementation (Agrawal
et al., 2019; Scott & Le Lievre, 2019), our results indicate that managers’ employability may
become dependent on upskilling to AI (Man & Man, 2019; Merrill, 2019). In fact, contrary to
what Grugulis (2019) has observed regarding most occupations, individual skills might rise
more slowly than job skills when it comes to managerial positions having to adopt AI.
Last but not least, our research proposals aim to guide future scientic investigation of
the links between AI and managerial skills, notably when it comes to understanding the
moderators of a successful AI-HRM interface (Parent-Rocheleau & Parker, 2021).
5.2. Implications for managers
Our study rst identies which managerial skills will be aected by AI and how. Because
our results suggest that most managerial skills are likely be augmented by this technol-
ogy, we advise organisations to prepare their managers for stronger collaboration with AI,
notably through a possible hybridisation. Organisations should start by informing man-
agers about the dierent socialisation phases of AI. As building trust and skills around this
tool could be even more dicult for less qualied populations, organisations are invited
to anticipate appropriate resources and actions to tackle this issue. More than with
previous technological changes, our data suggest that successful adoption of AI requires
thorough support from both organisations and their managers. Our results also enable
companies to prioritise their training eorts to achieve a successful AI implementation.
Our research depicts how the list of managerial skills and their weight in the existing
taxonomies could be updated. Managerial development programs should act quickly to
include specic training to optimise the use of AI through technical skills (basic AI
knowledge and dening business cases) as well as non-technical skills (judgement, ethics,
risk taking, open-mindedness, organisational change management, and communication).
Our results may also encourage higher education and executive training programs to
favour teaching imagination and leadership skills (which are likely to remain under the
helm of humans) over administrative and analytical skills (which may be taken over by AI)
in the long run.
Our work also suggests that the manager is the only actor able to gather employees,
data scientists, and business acumen around AI. When combined with the central position
of managers for the successful implementation of AI, the broadening of managerial skills
will probably increase pressure on those occupations. Managers are encouraged to antici-
pate new job requirements and to upskill in response to emerging organisational needs.
With this in mind, attracting and securing managers particularly skilled in AI collabora-
tion and augmentation may then be essential for an eective AI-HRM interface and
therefore become a source of sustainable competitive advantage. Indeed, regular ups-
killing to AI requires stable hybrid human resources. The advent of the AI-HRM interface
thus supports the call by Stuart et al. (2021, p. 906) for ‘job retention [to] be considered
a core practice of HRM’ in organisations.
20 L. GIRAUD ET AL.
Organisations may also contemplate remodelling their structures in order to optimise
the AI-HRM interface, which may in turn improve operations if the appropriate conditions
are met, notably in terms of skills and cooperation. We even encourage companies to
further consider the socio-technical capital associated with AI when making strategic
decisions. At the same time, because implementing AI does not grant immediate results
(as machine and managers need to learn to work together), organisations should foster an
organisational culture keener on risk taking.
6. Limitations
A major limitation of our study is the heterogeneity of our sample in terms of selected
locations, occupations, and sectors. Because access to AI experts was challenging, we
thought it was more important to produce rich accounts of experts’ experiences,
a technique known as ‘thick description’ (Geertz, 1973), thereby providing other research-
ers with a useful database for assessing the possible transferability of ndings to other
contexts (Lincoln & Guba, 1985). The diversity of our respondents nally turns out to be
a relevant way to achieve a comprehensive exploration of a globally rising phenomenon
like the introduction of AI into corporations and its impacts on managerial skills (Vrontis
et al., 2021).
7. Future research perspectives
Based upon our work and the existing scattered information on the interface between AI
and managerial skills (Charlwood & Guenole, in press; Pereira et al., 2021), we propose
future research paths that we further develop in the next subsections: (1) gauge the
priority of each managerial skill which may dier in dierent industries and managerial
levels, (2) identify eective methods to train managers in AI, and (3) investigate potential
moderators aecting the successful application of AI by managers. These suggested paths
complement the conclusions of recent AI-HRM systematic reviews being either more
general (Basu et al., 2022) or targeting AI applications for HRM functions (Budhwar
et al., 2022).
7.1. Gauge the priority of each managerial skill which may dier in dierent
industries and managerial levels
The priority of each skill still remains unclear, especially when it comes to context-related
aspects. Clarication would be welcome in order to prioritise training eorts depending
on dierent industries, cultures and managerial levels. Scholars could further fuel research
eorts in that direction by, for example, identifying context-specic priorities (Parry et al.,
2021).
7.2. Identify eective methods to train managers in AI
As more managers will work with AI (Norman, 2017), it would be useful to investigate the
process through which they relinquish some of their tasks to this technology. To date, the
literature lacks advanced knowledge of eective training methods for collaborating with
JOURNAL OF DECISION SYSTEMS 21
AI (Beane, 2019). Researchers could therefore investigate how to augment specic man-
agerial skills, with the objective of better understanding how managers can learn new
tasks and let go of others. The capacity to regularly update ways of collaborating with AI,
through bilateral learning (Sturm et al., 2021), may also be central. For instance, scholars
may look at how airline pilots progressively introduced AI into their environments and try
to anticipate similar mechanisms for managers.
7.3. Investigate potential moderators aecting the successful application of AI by
managers
Our data indicate that the successful appropriation of AI by managers depends on several
potential factors that warrant further investigation; they are: (1) corporate and regional
culture regarding risk taking, (2) data availability and quality, (3) digital transformation
strategy, (4) level of discretion regarding AI ethics, (5) organisational structure and
context, and (6) collective intelligence and power sharing.
Our data suggest that risk-taking mindsets and environments aect the success of AI
initiatives. Specically, our data show that organisational cultures that do not embrace
risk-taking reduce the chance of AI eectively supporting managers. Future research
could compare how managerial skills are impacted by AI technologies in organisations
that foster an open risk-taking culture versus those that do not. This research should
consider regional and cultural variations in the relationships with AI and more generally
with risk-taking. For instance, managers in emerging economies may be more inclined
towards AI when making decisions than those in more developed areas (Kolbjørnsrud
et al., 2017). In that regard, various layers of culture may moderate the success of AI
implementation. The work of Xie et al. (2021) on the impacts of AI on occupations within
the dierent regions of China could for instance, be extended to managers and represents
another interesting path to explore.
In line with Agrawal et al. (2017), our respondents suggest that the more available data,
the more tasks can be assisted by AI. Data must then remain relevant and widespread to
be able to support managers in decision-making. As Kaplan and Haenlein (2019) have
documented, the quality of AI technology depends on the quality of the data used to train
it. A large amount of data enables AI to assist managers in solving complex issues. While
data quality plays an important role in mitigating biased decisions, it should also condi-
tion the use of AI and its implications in terms of potential managerial impacts.
Digital transformation strategies aim to implement digital activities that have implica-
tions in all sectors of the organisation (Matt et al., 2015). These strategies have a signicant
impact on how employees collaborate with AI technologies (Jarrahi, 2018). Indeed,
a certain level of organisational maturity in terms of AI agility seems to be a pre-
requisite for the introduction of AI (Lichtenthaler, 2020). Our respondents indicate that
without a strong digital backbone, maintaining good data quality and individual commit-
ment to AI is impossible. It is thus plausible that organisational digitalisation plays
a signicant role in AI’s eects on managerial skills because employees and managers
need organisational support to implement and utilise AI. Further research in that direction
would be welcome, all the more so as the process of individual acceptance of AI still needs
to be sorted out (Del Giudice et al., 2021).
22 L. GIRAUD ET AL.
Our respondents conrm that ethical questions regarding the use of data
usually arise when implementing AI (Malik, Srikanth et al., 2020) in order to
minimise or avoid its negative outcomes (Mikalef et al., 2022). While AI technology
continues to rapidly develop, governments have generally been slow to establish
a legal framework. Thus, corporations or managers may have to assume responsi-
bility for ethical AI implementation (Bez & Chesbrough, 2020; Helbing, 2019). The
extent of this responsibility will depend on managerial discretion imposed by
corporate and legal frameworks still under development as we write these words
(Lilkov, 2021). The European Commission (2019, p. 3) identied potential legal gaps
regarding the misuse of AI and recommended the evolution of ‘EU and national
liability regimes’.
A multilevel investigation could be particularly relevant to explain attitudes and
behaviours towards AI at the individual, team, and organisational levels (Buchanan
& Huczynski, 2019). At the organisational level, research could explore how com-
panies restructure (Knickrehm, 2018) in response to changes in managerial skills
and in the AI-HRM interface. We show that AI is likely to signicantly aect
organisations, especially as more managers work directly with AI (Norman, 2017)
and use AI in decision-making (Lindebaum et al., 2020). At team levels, scholars
could experiment with the dierent ways in which AI and managers optimise their
joint added value and target areas for possible synergies (Malhotra, 2021), or
‘symbiotic metamorphosis’ (Makarius et al., 2020). Moreover, specic managerial
positions or sectors might require unique structural interfaces with data scientists
and AI itself. At an individual level and following Gentry et al.’s (2008) exploration
of managerial skills, it would be interesting to weigh the importance of each skill in
the AI era (as depicted in the present study) in terms of managerial position and
hierarchical level.
Last but not least, our data suggest that AI is likely to reorganise collaboration processes,
notably between managers and AI specialists. Thus, a focus on collective intelligence in the AI
era may be needed (Kaplan & Haenlein, 2019). If managerial skills are automated or
augmented, organisations must remain attentive to how they can ensure optimal human-
AI interactions at the individual, team, and organisational levels (Buchanan & Huczynski,
2019). As AI empowers intermediary actors (e.g. managers and data scientists), collective
intelligence appears to be a promising path to successfully modify management processes in
the digital era (Elia et al., 2020).Successful AI implementation may require a signicant
collective eort because it seems to mainly depend on the human-AI interactions (Wilson
& Daugherty, 2018) and on a shift in organisational culture towards more risk taking and
more transversal collaboration (Fountaine et al., 2019).
A related question lies in the reorganisation of political relationships (March &
Simon, 1958). AI represents a signicant organisational transformation, notably
aecting power structures, and can lead to intermediary managers as well as
data scientists gaining more inuence over collective action (Crozier & Friedberg,
1977). It would be useful to investigate how power relationships, group dynamics,
and collective intelligence interrelate when AI is being adopted (Prikshat et al.,
2021).
JOURNAL OF DECISION SYSTEMS 23
8. Conclusion
Our research aimed to unveil AI’s impacts on managerial skills. Our results show that most
managerial skills are likely to be augmented by AI, while only a few of them may be
replaced or remain unaected. In addition, our data highlight the technical and non-
technical managerial skills that should be prioritised to successfully implement AI.
Note
1. https://www.getsmarter.com/courses/us/mit-articial-intelligence-online-short-course?utm_
campaign=MIT-AI_INT-APLHA&utm_medium=automatic&utm_source=email&_ke=
eyJrbF9lbWFpbCI6ICJsYXVyZW50LmdpcmF1ZEB0c20tZWR1Y2F0aW9uLmZyIiwgImtsX2Nvb
XBhbnlfaWQiOiAiZHZLdVpxIn0%3D
Data availability statement
The authors conrm that the data supporting the ndings of this study are available in an online
dataset repository.
Disclosure statement
No potential conict of interest was reported by the author(s).
Funding
This work was supported by the Airbus Leadership University.
Notes on contributors
Laurent Giraud is a former executive recruiter in the automotive industry and currently Full
Professor at IREGE, IAE - Savoie Mont Blanc University, France. His research focuses on
International HRM, Change Management and AI-HRM Interface. He is also Track Co-Chair at the
British Academy of Management HRM Special Interest Group.
Ali Zaher is a PhD candidate, Lecturer at IAE Lyon School of Management, France and researcher in
“Chaire Valeur du Soin”. His research focuses on the implications of Articial Intelligence techniques
on the HRM practices within organizations and hospitals.
Selena Hernandez is a PhD candidate at Toulouse School of Management – TSM Research (UMR
CNRS 5303 – Toulouse 1 Capitole University). She is also Teaching and Research Assistant at TBS
Education, Toulouse, France. Her research focuses on the alternative forms of work organization.
Dr. HDR Akram Al Ariss is a Professor of Human Resource Management at TBS Education, Toulouse,
France. His research focuses on Strategic Global Talent Management and International Mobility. He
is also Associate Editor of the Human Resource Management Review.
ORCID
Laurent Giraud http://orcid.org/0000-0003-3703-9075
Ali Zaher http://orcid.org/0000-0002-9992-948X
24 L. GIRAUD ET AL.
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32 L. GIRAUD ET AL.
Appendix 1: Qualitative sample (Study 1).
# Dr.
Occupational
tenure Main activity Area of Expertise Sector Location
1 1 Data scientist Data analytics Automotive France
2 X 2 Executive AI strategist Machine learning Research France
3 5 Data science manager, head of data lab Machine learning Consulting France
4 X 2 Data scientist AI business applications Automotive France
5 2 Data scientist Machine learning Automotive France
6 X 2 Data scientist Multi-agent systems Automotive France
7 X 4 AI engineer, data scientist Machine learning Research USA
8 X 2 Researcher AI business applications,
open innovation
Research USA
9 X 5 Researcher Machine learning Research USA
10 X 2 CEO Innovation Consulting USA
11 4 CEO AI business applications Consulting USA
12 X 2 Researcher Marketing, strategic
management
Consulting USA
13 X 4 Researcher AI business applications
(finance)
Research USA
14 X 3 Researcher AI modelling Research Japan
15 X 6 Researcher Human-computer
interaction
Research Japan
16 X 12 Researcher Cognitive science,
creativity, & AI
Research Japan
17 X 4 Researcher AI & sociology Research USA
18 4 Education specialist Lifelong learning Education USA
19 X 9 CEO Data collection &
sensemaking
Consulting USA
20 3 CEO AI & coaching Consulting USA
21 6 Head of employment & competencies HR competencies Aerospace France
22 19 Data scientist Systems architecture,
data analysis
Energy
industry
France
23 4 Data scientist Digital services Energy
industry
France
24 1 CEO AI business applications Consulting France
25 1 Smart City Project manager, cofounder &
CTO
Technology Consulting France
26 1 Cluster dealer & manager of business
analyst team
AI business applications Consulting France
27 2 CEO AI business applications Consulting France
28 X 2 Chief digital technology scientist, text
mining CoE, digital transformation
group
Emerging technology,
business
development
Research Japan
29 4 Consultant AI business applications
(notably HRM)
Consulting Japan
30 X 7 Senior VP of AI solutions division AI modelling Consulting Japan
31 3 R&D manager AI business applications Consulting Japan
32 X 9 CEO Human-computer
interaction
Research Japan
33 X 1 General manager of data analytics Data analytics Utilities Japan
34 X 2 General manager of data analytics Data analytics Utilities Japan
35 3 Director, strategic product management New technology,
business scouting
Automotive USA
(Continued)
JOURNAL OF DECISION SYSTEMS 33
(Continued).
# Dr.
Occupational
tenure Main activity Area of Expertise Sector Location
36 X 3 Consultant at data transformation
department, group strategy business
unit
Machine learning Research Japan
37 X 1 Researcher Machine learning,
natural language
processing
Education Japan
38 3 Senior product manager AI business applications Consulting China
39 X 1 Researcher AI, management Research France
40 X 3 Researcher AI, management Research Canada
34 L. GIRAUD ET AL.
