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Impact of Artificial Intelligence on Management

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Impact of Artificial Intelligence on Management

Abstract

This study focuses on the impact of advancing Artificial Intelligence systems on management during the next decade. Much of the attention around Artificial Intelligence and work revolves around the replacement versus augmentation debate. According to previous literature, rather than simply replacing tasks, machine learning tools can complement human decision making. Based on semi-structured expert interviews, this study provides tentative evidence that this may be true for managers on the highest level of organisations, but perhaps less so for operational and middle managers who may find a larger number of their tasks replaced. As routine tasks of supervision and administration can be automated, the shift towards interpersonal tasks of leadership could continue for many managers. Two possible future scenarios are formed to illustrate how Artificial Intelligence may possibly impact management. In addition, algorithmic management is recognised as an important factor in the next decade as platform economy keeps growing. Having potential to replace tasks of the operative managers, it is important to continue research on fairer algorithmic management. Also for further studies it is recommended to evaluate AI's impact on each level of managers separately, because of the disparate work tasks of operative, middle and senior managers.
EJBO Electronic Journal of Business Ethics and Organization Studies Vol. 24, No. 2 (2019)
43 http://ejbo.jyu.fi/
Impact of Artificial Intelligence on
Management
Niilo Noponen
Abstract
This study focuses on the impact
of advancing Artificial Intelligence
systems on management during
the next decade. Much of the
attention around Artificial
Intelligence and work revolves
around the replacement versus
augmentation debate. According
to previous literature, rather than
simply replacing tasks, machine
learning tools can complement
human decision making. Based on
semi-structured expert interviews,
this study provides tentative
evidence that this may be true for
managers on the highest level of
organisations, but perhaps less
so for operational and middle
managers who may find a larger
number of their tasks replaced. As
routine tasks of supervision and
administration can be automated,
the shift towards interpersonal tasks
of leadership could continue for
many managers. Two possible future
scenarios are formed to illustrate
how Artificial Intelligence may
possibly impact management. In
addition, algorithmic management
is recognised as an important factor
in the next decade as platform
economy keeps growing. Having
potential to replace tasks of the
operative managers, it is important
to continue research on fairer
algorithmic management. Also for
further studies it is recommended to
evaluate AI’s impact on each level of
managers separately, because of the
disparate work tasks of operative,
middle and senior managers.
Key Words: Management,
Leadership, Artificial Intelligence,
Algorithmic Management,
Digitalisation, Future of Work Introduction
The research problem of this study is
to empirically evaluate Artificial Intelli-
gence’s (AI) impact on managers. The
impact is evaluated on the different as-
pects managers’ work tasks may include,
such as administration, supervision and
leadership. In this study all people whose
occupation is to lead people or manage
operations are referred to as managers,
despite the varying ratios they perform
these tasks. Rather than evaluating man-
agers as a homogeneous group, they are
divided to operative, middle and senior
levels for a more precise evaluation. The
aim is not to provide a certain forecast,
but to compare possible future scenarios
to better understand the phenomenon.
Brynjolfsson & Mitchell (2017) state
that most sectors of work and economy
are at the beginning of a large transfor-
mation caused by recent advances in ma-
chine learning. Unlike previous forms
of technology, the most recent break-
throughs in AI can also affect multiple
highly skilled and highly paid occupa-
tions (Frank et al., 2019). Naturally a lot
of debate and polar opinions exist about
what this means for different professions
and for the future of work. According
to Makridakis (2017, 57) some technol-
ogy experts claim that this AI revolution
could change society even more than
industrial revolution did. At the same
time, others argue that the impact of AI
is widely overestimated.
While the dialogue on AI and work
has mainly focused on the potentially re-
placing effect it may have on different oc-
cupations, its impact on management has
received less attention. Frey and Osborne
(2013, 44-45) label managers’ occupation
in the category of low risk for automa-
tion. Authors such as Autor (2015) and
Jarrahi (2018) have also claimed that
AI offers augmented decision making
rather than job replacement for manag-
ers. However, there seems to be evidence
that management and leadership may
be at the start of a transformation. Al-
gorithmic management in platform and
gig economy has introduced a new way
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of supervising workforce (Rosenblat and Stark, 2015). Mean-
while, Auvinen (2017, 42) states that this wave of digitalisation
is at a point where its first impacts on leadership can also be
identified, for example with the concept of a virtual leader.
Literature review
AI – About the history and definitions
In this study the original definition by McCarthy, Minsky,
Rochester and Shannon (1955, 11) is used to broadly describe
Artificial Intelligence as action performed by a machine that
would be considered intelligent if done by hu-man. AI is con-
sidered a hypernym to developments within it, such as machine
learning and deep learning. Many more narrow definitions ex-
ist, but for the purposes of this study, if a machine is able to
perform a task previously done by a human manager, it is con-
sidered as artificial intelligence.
In its history of over 60 years, AI has seen multiple cycles of
initial excitement followed by eventual disappointment (Pan,
2016, 410). In the beginning of these cycles, recent advance-
ments led to claims such as that effectively every single human
task could be performed by a machine in just a few years. As
these hopes proved overoptimistic, a period of “AI winter”
would follow, with less outside funding and enthusiasm for re-
search in the field. (Kaplan, 2016, 15-16.)
Still throughout the years, the AI field has given numerous
demonstrations of advancement. From Arthur’s (1959) check-
ers program to AlphaZero’s (Silver et al., 2017) chess and Go
mastering reinforcement learning algorithm, many of the main-
stream milestones have been beating human players in games.
At the same time AI has increasingly been used to tackle nu-
merous real world problems, such as cyber-attack detection and
credit card transaction reviews (Kaplan, 2016, 39).
According to Remes (2018, 32-39) the rapid adaptation of
AI programs in various industries during the last ten years has
happened because programmers now have enough data and
computing power to develop deep learning systems, based on
the neural network research of previous decades. As it stands
though, even the most sophisticated deep learning software can
be incredibly efficient in the task it is trained to do, but com-
pletely clueless when assigned a different task. Still, even as the
coveted artificial general intelligence may be years away, these
learning systems do have demonstrated benefits in the growing
number of tasks they are assigned to (Frank et al., 2019).
AI in organisations
The role of technology in leadership and management has been
recognised for some time. E-leadership is defined by Avolio,
Kahai and Dodge (2001, 617) as IT-mediated means to pro-
duce change in organisations. Reviewing the theory, Avolio,
Sosik, Kahai and Baker (2014, 106) state that both the science
and prac-tice of leadership has dragged behind the adoption of
advancing technology in organisations. They argue that rather
than focusing on predicting the most desirable practices, the
leadership field has reactively studied the impact tech-nology
already has had.
According to Auvinen (2017, 37) leadership is shifting from
the scientific management of the last century towards structures
of lower hierarchy in order to enhance creativity, participation
and digital innovations. Also Auvinen et al. (2019) claim that
there has been an epoch change in leadership as the embodied
presence of the leader has seemed to transform into digital plat-
forms. The need for actual leadership has not disappeared, but
the methods of communication and presence of the leader have
been somewhat digitalised.
Another example of digitalisation is in the area of platform
economy, where algorithmic management is used to connect
customers and workers. Lee et al. (2015) define algorithmic
management as managerial functions performed by software
algorithms and their supportive devices. Lee at al. point that
in addition to the newer companies in the platform economy,
algorithmic management has been increasingly introduced to
optimise, allocate and evaluate work in traditional occupations
from warehouses to coffee shops.
This arrangement between the worker and the digital manag-
er raises a completely new dynamic. Algorithmic management
has been praised for the potential freedom it provides workers,
but it has faced criticism for the exploitative information asym-
metries that favour the company (Rosenblat and Stark, 2015,
3758). In her thesis, Tammisalo (2019, 63-64) concludes that
while employees in a financial institution prefer the more emo-
tionally intelligent feedback of human managers, they also see
the value of the instant input that AI can enable as a part of the
feedback.
There has been some conversation about what the advent of
novel technology means for managers. A study by Frey and Os-
borne (2013, 40-45) claims that while workers in many fields
are in a high risk of automation, managers are less likely to be
replaced as their work consists of tasks demanding social intel-
ligence. Similarly as Pulliainen (2019, 84) states in her thesis,
many senior level managers are not worried about replacement
as they see AI as a complementary tool they can use to be more
efficient. Other studies support this augmenting view as well.
Jarrahi (2018, 577) highlights the potential of an AI system
with vast computational capability paired with the more holis-
tic intuition of a human manager. Autor (2015, 5) claims that
historically scholars and journalists alike have overstated the
labour replacing power of advancing technology, while missing
that automation also augments human skills, creates new work
tasks and increases productivity and demand.
Still according to Makridakis (2017) some people in the field
of AI claim that this time it is different, as task after task can
be replaced. People supporting this revolutionary view of AI
maintain that as far as demand for their labour, most workers of
today are comparable to horses at the end of the 19th century.
While optimists among this group believe that in the end this
increased productivity will create a utopia for all, pessimists fear
that it will lead to a dystopia for most. (Makridakis, 2017.)
As Arntz, Gregory and Zierahn (2016, 4) point, it is quite
unlikely that in the near future every single task performed in
an occupation could be automated. For example even if one day
self-driving trucks replace drivers, human drivers may still be
needed for other tasks such as loading and offloading goods.
Therefore a task based approach is used in this study.
Based on the literature, AI can impact managers directly by
replacing or augmenting certain work tasks. AI can also affect
managers indirectly by causing changes in their working envi-
ronment. Therefore the effects of AI can be divided to four lev-
els: global level, level of society, level of organisational structure
and level of managers’ work tasks.
In this hierarchy, changes can trickle up or down the levels.
For example, if a country has a goal of being a global leader in
AI technology, it may allocate funds of the society to empower
AI development and education, which in turn could change the
way organisations and their managers operate.
Furthermore, for more precise inspection, managers are split
into three groups: operative managers, middle managers and
senior managers. Operative managers are considered the lead-
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ers of the workforce, middle managers are the leaders of opera-
tional management and the highest level senior managers are
the leaders of middle management.
Methodology
Research strategy in this study is qualitative and interpretative
(Eskola and Suoranta, 1998). The empirical data consists of six
semi-structured thematic interviews (Kovalainen and Eriksson,
2008). The interviewees have been chosen using purposeful
sampling (Patton, 2002). Using knowledge and expertise of AI
as the requirement for participation, a high ranking group of
authorities in research, data science and consultancy were se-
lected.
As part of the ethical guidelines, the participants were asked
for the permission to record and transcribe the interviews. The
interviewees were also granted anonymity to allow the expres-
sion of personal opinions independent of affiliation. Therefore
the informants’ identities are codified and in the analysis section
they are referred to as Experts 1-6. The more detailed overview
on the empirical data is represented in the figure 1.
A similar set of questions were given to each expert, still pro-
viding freedom for the interviewee to focus on the aspects they
Informants Job title Duration of
interview
Transcribed
pages
Expert 1 Director of Research 33:39 7
Expert 2 Consultant 44:53 9
Expert 3 Principal Software
Architect
24:51 5
Expert 4 Research Professor 42:56 8
Expert 5 Data Scientist 42:26 10
Expert 6 Consultant 43:04 9
Figure 1 Detailed overview on the empirical data
saw most important on each topic. The questions are based on
1. How AI can replace managers’ work tasks during the next
decade and 2. How much AI can replace managers’ work tasks
during the next decade. Because of the difficulty of the topic,
the main ques-tions were sent to each participant for familiari-
sation before the interview. For practical purposes, the inter-
views were conducted on Skype. Before the actual interviews,
two practice interviews were conducted to adjust the ques-tions
to better focus on the relevant themes.
Content analysis was used to group the data and to search
for repetitive themes and patterns in it (Eskola and Suoranta,
1998). In expert interviews data collection and analysis often
merge together, because the interview questions are customised
for the expert group (Alastalo and Åkerman, 2010, 377-381).
In this case it means that the divisions used in this study (as laid
out at the end of chapter “AI in organisations”) largely shaped
the structure of the questions in the interviews and the data
analysis that followed. The interview tapes were transcribed
and listened to carefully, to ensure correct understanding of
the experts’ ideas. Because of the nature of expert interview,
no hidden meanings were searched for within the interviewees’
speech, instead their answers were taken at face value. Experts’
opinions were grouped and colour coded based on each topic to
make analysis easier and more direct. Within these themes the
answers were examined for similarities and differences.
Using the empirical data as a guide, two possible futures sce-
narios were formed to illustrate AI’s possible impact on man-
agement. According to Godet (1994, 44) a scenario is a basic
concept of futures studies that tells what logical chain of events
leads to a plausible situation in the future. Scenarios can be
divided to possible, probable and desirable scenarios. Possible
scenarios are all the futures that can be envisioned as possible.
Unlike probable and desirable scenarios, possible scenarios
don’t have to be as rigorously tested, because the function is to
expand understanding of the potential events. Possible scenar-
ios are evaluated by the logicality and plausibility of the events
depicted. (Amara, 1991, 646-647.) As with any study regard-
ing future, the three principles of futures studies apply: future
cannot be perfectly foreseen, future is not predetermined and
future can be influenced with acts and choices (Rubin, 2004).
As a limitation of this study, a relatively small sample size was
used to gather the data. This study deals only with possible fu-
ture scenarios, and does not make any statement of their prob-
ability. For probable or desirable scenarios, a Delphi method
could be used. It is also important to remember that this study
tries to chart out the impact of AI on managers’ work tasks,
from which is not possible to draw straightforward conclusions
on what it might mean for their employment. For more exten-
sive scrutiny on the topic, more research is needed.
Analysis
Revolutionary view
Based on the empirical evidence, the expert opinion on the im-
pact of AI on management can be roughly split into two groups:
revolutionary and evolutionary. The revolutionary group be-
lieves that due to the unforeseen capabilities of AI technology,
managers’ work tasks will be greatly affected on all levels.
These experts believe machine learning systems can be used
in various white collar work tasks previously thought too dif-
ficult to replicate by machines. After decades of comparatively
slow AI development for practical applications, the possibilities
set by computing power have finally caught up with the neural
network algorithms of old, leading to the breakthroughs of the
last decade (Remes, 2018, 32-39). Some of the experts expect
that the rate of change starts to grow exponentially in the com-
ing years.
Expert 3: It is all about training. The pace of training the
learning models starts to grow exponentially. In ten yea-
rs I believe AI can teach AI and the exponential curve gets
steeper. Based on human managers’ history it is possible
to make good conclusions, forecasts and finally decisions.
That’s why I believe manage-ment as it is now understood
can be quite light when it comes to humans. Ma-chines will
be able to do almost all decisions and can make more logical
insights based on better algorithms than humans can alone.
The growing capabilities of processing units indeed set the
limits for AI de-velopment. Big datasets need a lot of comput-
ing power to fine tune the models, making it energy intensive
and expensive. Therefore the most ambitious projects are most-
ly limited to the biggest players. Novel methods specifically
built for AI, such as Cerebra’s AI chip, may however change
the landscape and make it possible for smaller organisations to
develop models swiftly (Freund, 2019).
As the capabilities of AI systems grow larger, some of the ex-
perts suggest that managers should focus more on understand-
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ing the technology. Many of them also suggest that positions
such as Chief Technological Officer will become more impor-
tant in the future.
Expert 1: Managers must increasingly think on how they use
their time. Managers should probably be some in AI courses
learning those tools more, instead of getting involved with
routine or detail management. They should focus more on the
big picture and focus on mastering A.I and robotics technology.
Evolutionary view
The evolutionary group believes that even though AI may im-
pact managers in many ways, even replace some tasks, it will not
cause any unforeseen changes in managers’ work. They believe
that while machine learning systems can automate some repeti-
tive managerial tasks, the focus will merely shift to softer lead-
ership skills. These tasks of motivation and encouragement are
arguably harder to automate.
Expert 6: You don’t have to manage routines and processes.
Instead it will be managing human capacity, interaction and em-
pathy. In the narratives there’s been a lot about soft leadership
skills. This I believe will be more common, lead-ing individuals.
This view of the second group is consistent with Laitinen’s
(2018, 45) claim that we live in a society of work, in which po-
litical, cultural, social and economic factors define the meaning
of work for the individual – while technology only defines what
work is done within these parameters.
Similarly Autor (2015, 5-7) points that technological change
also complements labour, raising the demand for non-auto-
mated tasks. Autor claims that workers in tasks complement-
ed by automation benefit more than workers in tasks that are
replaced. Based on the expert interviews, it thus seems likely
that the impact of AI may be kinder for managers competent in
interpersonal tasks such as communication, employee motivat-
ing and creative decision making, as the skills can be used to
complement automated tasks. On the other hand, technologi-
cal change may not be as welcome for managers whose skills are
based on routine administrative tasks such as reporting, work
supervision and synchronisation.
In his book Graeber (2018) defines a bullshit job as employ-
ment that is so unnecessary that even the employee cannot
justify its existence – yet they have to pretend this is not the
case to keep receiving their salary. Why this is a matter for this
paper is because among the anecdotal evidence gathered for
Graeber’s book is a number of testimonies by middle managers,
HR managers and administrators, who confess that their work
lack any meaning. Some middle managers claim that as their
subordinates are mostly completely fine without their supervi-
sion, they perhaps have to invent unnecessary tasks to justify
their existence, while their own bosses don’t know what they
do. Naturally this is not a claim that all middle managers are
unnecessary. Too many conclusions can’t be drawn from these
personal stories, but it does make analysing changes in work
more complicated. We tend to assume that other people are do-
ing something useful, but who really knows what other people
do at their jobs? Can a manager be replaced if their work was
not needed in the first place?
AI’s impact on different levels of management
Perhaps unsurprisingly, most of the interviewed experts believe
that AI’s impact is higher on operative and middle managers
than on senior managers. On average the experts estimate that
during the next ten years a third of the work tasks of operative
and middle managers can be automated. For the senior manag-
ers the assumption is that slightly less than a quarter of the work
tasks can be replaced by different AI methods. The interview-
ees explain that the two lowest levels of management contain
more repetitive tasks of supervision that are easier to automate.
Expert 2: Automating operative tasks, such as ad-
ministration, synchronising timetables, filling out
work sheets and checking whether somebody did
their work, is a very straightforward process.
Expert 5: The tasks that can be replaced are administrative, for
example if you have a factory manager that uses a lot of time
to adjusting duty schedules. And usually you should as it’s not
very difficult. And if it saves half of a managers working hours,
it is quite a valuable thing. I think that type of administration,
excel optimisation and managing different matters will decline
radically. How much it can replace a manager depends obvious-
ly on how much their work con-sists of that type of tasks.
The experts view that the amount of work for middle manag-
ers is connected to the number of operative managers they su-
pervise. Along with the tasks of managers themselves, the over-
all demand of managers’ work may also fluc-tuate if the number
of workers they supervise increases or decreases. For example, if
a large number of workers are replaced because of automation
or another reason, the amount of managerial tasks needed could
also drop.
This potential shift to less managers may be desired by some.
While Auvinen, Riivari and Sajasalo (2018) highlight the need
for the emotionally intelligent embodied leader, they also note
that traditional leadership theories have been contested in fa-
vour of new-age approaches emphasising self-leadership, digi-
talisation and flexibility in a time of constant change. To en-
hance productivity, some organisations have chosen to ditch
middle management, giving more freedom and responsibility to
the employees. According to one of the experts, some profes-
sionals prefer lower levels of hierarchy and more shared govern-
ance.
Expert 6: In a study young managers in expert organisa-
tions thought that the flatter the organisation the better.
In a sense the number of managers or middle mana-
gers goes down and there will be smaller team structu-
res, in which leadership is shared between people.
Meanwhile it seems that the complementary aspects of AI
benefit senior management the most. Many of the foreseeable
advancements in AI seem to make senior managers jobs easier.
For example high level decision making can be facilitated with
dashboard systems giving real time information and sugges-
tions of action.
Expert 4: There may be these dashboard systems that con-
dense information and extend it in a sense. And they make
some recommendations that in this situation you should
proceed like this: before we have proceeded like this, and
this way of operating has created these types of results.
Unlike the others, one of the experts believes that in the fu-
ture, the impact of AI may be greater on the highest level of
management. This is based on the idea of automated decision
making: with enough data on human managers’ decisions and
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their consequences, machine learning programs can be trained
to select the most desirable decision for each situation. Mean-
while this expert believes that when dealing with the challenges
of leading the workforce, an algorithm may not be enough.
Expert 3: In operative management you need things that
a computer cannot re-place. Hands-on teaching, especi-
ally in human resource management. AI can’t analyse a
person in ten years as well as another human being.
Most of the other experts also believe that while the repetitive
tasks can be replaced, managers can use more of their time to
focus on tasks demanding softer leadership skills. Even though
many tasks can be replaced, leadership is still necessary.
Shifts in the working environment also affect managers’
work. Between both countries and companies, global competi-
tion for AI supremacy may further accelerate the adaptation
of new technologies. On national level experts believe that data
protection legislation may decelerate the development and ad-
aptation of AI systems, especially in public organisations. On
the other hand, increased government funding can hasten AI
development and provide more opportunities for organisations.
As the experts point out, companies may be encouraged to uti-
lize bold approaches to digitalisation as they seek the gains of
the first player on the market, as Uber, Netflix and Spotify have
done in their respective industries. Perhaps most crucially re-
garding this study, algorithmic management can make opera-
tive management redundant in companies using the methods of
platform economy.
Algorithmic management in platform economy
Most experts identify platform economy as a factor that can
cause disruption to the way organisations manage their work-
ers. According to the opinions of the interviewees, it seems like-
ly that the platform economy model will be-come more com-
mon in various industries. One can claim that with processes
of algorithmic management, organisations are able to replace a
large chunk of the tasks of operative management.
Expert 2: I guess that platform economy type thing – or-
ganising operations, which operative management is – will
become more common. Certain tasks can be automated
completely. For example in Uber, taxi automation is au-
tomated now. You don’t need managers for that.
Expert 3: I would say there will be more of this in dif-
ferent industries. Energy sector, insurance sector, the-
se traditional industries will have more of these which
will change the way of operating quite radically.
As the interviewed experts note, the ways of platform econ-
omy may not only change organisation structures, but the rela-
tion of employment and leader-ship as well.
Expert 6: When talking about AI and work, the influen-
ce is not just on work or tasks, because as AI enables larger
and better systems of platform economy, it also transforms
employment relationships. This allows the development
of shorter, fixed-term employment resembling freelancing,
which changes leader-ship away from leading teams. For
example in Uber they don’t really have (the drivers as) emplo-
yees. Then there is the question of does it change motivation
and commitment, potentially having multiple employers.
Since its initial boom a decade ago, this sharing economy was
met with wide-spread enthusiasm as it has been portrayed of
creating the flexible jobs of the modern age, where workers can
become their own bosses (Rosenblat, 2018). However, studies
such as Lee et al. (2015) and Schneider (2018) have demon-
strated some of the problems arising in platform companies
such as Uber and TaskRabbit. According to Rosenblat and
Stark (2015) Uber’s algorithmic man-agement creates power
asymmetries, which has led to cases of worker and customer
exploitation.
Based on the workers’ cries of exploitation under their algo-
rithmic managers it seems that it has been harder to optimise
for worker satisfaction than for the creation of monetary value
for shareholders (Rosenblat and Stark, 2015). These examples
point to a call for more research on how to make these plat-
forms more just. After some initial disappointments in the plat-
form economy there still exists hope for more shared govern-
ance and ownership – for example with platform cooperatives,
as suggested by Schneider (2018).
Discussion
In light of the data, it seems that the impact AI may have on
operative and middle management during the next decade may
be somewhat understated. For senior managers however, the
impact may be one of augmentation.
With technology such as automated decision making and
dashboards that provide real time information, a smaller num-
ber of managers may be needed for supervisory and adminis-
trative tasks. Still, most of the interviewed experts stress the
increasing importance of interpersonal leadership. Managers of
any level excelling in soft leadership skills may be in higher de-
mand in the near future, which corresponds to the longer pro-
gression of leadership shifting away from the scientific manage-
ment of last century, demonstrated by Auvinen (2017).
Algorithmic management is interesting in the sense that
it seems to be heading in opposite direction of this long time
trend of leadership. Many companies operating in platform
economy are implementing methods that could be described as
scientific management, with clearly defined specific tasks, roles
and objectives. In a sense the platforms are often designed to
make workers operate as reliably as machines. One way to ex-
plain this phenomenon is that some of these platform economy
jobs are precursors for further automation. For example in the
future, more rides may be conducted by self-driving cars instead
of Uber drivers and restaurant takeout orders may be delivered
by drones instead of Deliveroo riders.
The nature of this virtual manager poses some interesting
questions. According to Sintonen and Auvinen (2009) the
ownership of leadership power is ambiguous and blurred. As
they claim (Sintonen and Auvinen, 2009, 107) it is the story
rather than the leader as a person who leads. In other words,
the members of an organisation follow the meaning integrated
in the story rather than the leader as a person. When leadership
integrates into digital platforms, the question of who is actu-
ally leading can become even more blurred. One could argue
that in principle a leader can have more direct control and exact
orders than before by using algorithmic management. But at
the same time it is not always clear how a machine learning al-
gorithm makes decisions, even to the programmer in charge of
optimising it – let alone for the person who commissioned it.
Indeed, the responsibility of programmers seems to be grow-
ing as these platforms control the work of millions of people
around the world. In these situations the goals and values that
are directly or indirectly affecting the algorithm are essential in
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shaping how it operates.
Even with the concerns raised in this study, it is good to re-
member that the recent and future breakthroughs of AI are
part of the technological progress that has arguably raised the
quality of life and increased productivity during the last centu-
ries. The main issue remains the same: what actions to take to
make sure the spoils are evenly shared between people.
Conclusion
Previous literature (Frey and Osborne, 2013; Autor, 2015; Jar-
rahi, 2018) recognise the potential of advancing AI technolo-
gies, but estimate that for managers the impact will be one of
automation rather than replacement. Based on the expert inter-
views conducted for this study, it seems that AI may augment
highest level senior managers more than operative and middle
managers, whose work tasks could be more prone for automa-
tion. The methods of platform economy may also affect opera-
tive management the most.
The findings implicate the importance of just algorithmic
management systems as the model of platform economy seems
to spread. More research is still needed on the various aspects
necessary for a solid algorithmic management system. Leader-
ship and management scholars could surely have valuable in-
sight on this matter.
In addition, two groups could be distinguished of the ex-
perts – revolutionary and evolutionary. The former believe AI
has transformational potential for most occupations, including
managers. On the other hand, the evolutionary view stresses
that new technology mainly complements managers’ skills.
While other tasks may be automated, the skills that are harder
to replace become more important. As routine administrative
tasks may be automated, interpersonal leadership skills could
become even more crucial in the future.
Based on the revolutionary and evolutionary views as well as
the indications by Amara (1991, 646-647), two possible scenar-
ios can be mapped out for management in the next decade or
so. The extensive forecast of advancing technologies by Kuusi
and Linturi (2018) is used as a loose guideline for this author’s
imagination. The aim is to provide two opposing scenarios to
broaden the understanding of how AI can possibly impact man-
agement - not to speculate which one is more likely to happen.
In the revolutionary scenario, the continuing AI development
sweeps across industries, transforming societies with unprece-
dented velocity. AI enhanced technological breakthroughs keep
lowering the marginal costs of goods – most importantly food
and energy production become largely automated. A large per-
centage of permanent workforce from cashiers to radiologists
switch to freelance work in gig economy. The need for operative
and middle managers plummets as their administrative tasks
are automated, and old and new companies alike adopt the
ways of platform economy. In companies with expert workers,
employees prefer lower hierarchy and shared governance. Peo-
ple get used to the reliable, sincere and immediate feedback in
their fine-tuned algorithmic management platforms. From Jür-
gen Klopp to Gandhi and Gandalf, organisations can perhaps
choose as their leader a virtual version of a football manager, a
historical figure or a fictional leader that matches their mission
and story. In addition to human relations management, most
of the human managers’ tasks left can be performed mainly by
the senior management. A smaller group of leaders is able to
choose the direction their organisation takes, augmented with
automated decision making systems and dashboards that pro-
vide real time information. As an organisation is able to change
its whole operating model for each day of the week if the algo-
rithms so suggest, leadership and management theories of old
have to be rewritten.
In the evolutionary scenario, AI continues to develop and
disrupt industries, although not as widely as in the revolution-
ary scenario. Industries keep adopting the new possibilities of
automation, but for the managers the impact is not as strong as
for some of the workers. As societies are built around working
individuals, people whose tasks were automated are retrained
for new tasks created by AI, such as supervisors of automated
road and drone traffic. Platform economy doesn’t transform in-
dustries as much as originally expected, mainly impacting some
of the new companies and industries only. Algorithmic man-
agement becomes more common, but human administration
and oversight is still preferred, especially in more traditional
organisations. Even though some of the repetitive supervisory
and administrative tasks are automated, the complementary ef-
fects of AI help operative, middle and senior managers to better
focus on interpersonal leadership skills. Operative and middle
management are impacted slightly more than senior manage-
ment after the adoption of slightly flatter organisational struc-
tures. As the shift from management to leadership continues,
much of the responsibility of leaders contain tasks of employee
motivation, engagement and satisfaction. Even though techno-
logical breakthroughs in AI and other fields continue, the role
and tasks of leaders and managers evolve gradually, but do not
transform into something completely different.
Even though the rate of change in these scenarios is different,
what is common is that in both changes caused by AI are not
predetermined. Just like a hammer, AI can either be used as a
tool for creation or destruction. Technological progress cannot
and should not be stopped, but to make sure it is headed in
a preferred direction, good leadership is needed – maybe now
more than ever.
Some suggestions for further research can be recommended
based on this study, as leadership and management seem to be
entering some uncharted digital waters. First, in further studies
on AI’s impact on management, it is recommended to specify
the level of managers considered. AI impacts each level dif-
ferently because each group consists of widely different tasks.
Therefore, instead of referring to managers as a homogeneous
group, analysing each group separately could provide more ac-
curate results.
Secondly, the conversation of the embodied leader in an or-
ganisation, by Sintonen and Auvinen (2009) for example, could
be revisited in the age of the virtual leader. Because program-
mers have an increasing amount of power and responsibility, it
may be interesting to study who is actually in charge in the crea-
tion and operation of a digital management platform – the pro-
grammer, the supervisor, the story or perhaps the shareholder.
Finally, algorithmic management changes how organisations
are able to guide and control their workers. Many of the previ-
ous studies (Rosenblat and Stark, 2015; Schneider, 2018), have
rightly focused on the workers’ point of view, but more research
is needed to study how algorithmic management is currently
changing leadership and management and what direction it
should be taken in the future.
EJBO Electronic Journal of Business Ethics and Organization Studies Vol. 24, No. 2 (2019)
49 http://ejbo.jyu.fi/
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Author
Niilo Noponen
PhD student at Jyväskylä University School of Business and Economics, Finland
Email: niilo.v.noponen@jyu.fi
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