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Contents lists available at ScienceDirect
Futures
journal homepage: www.elsevier.com/locate/futures
The future of human creative knowledge work within the digital
economy
W. David Holford
University of Quebec at Montreal (UQAM), School of Management Sciences, Department of Management and Technology, Post O ffice Box 8888,
Downtown Station, Montreal, Quebec, H3C 3P8, Canada
ARTICLE INFO
Keywords:
Artificial intelligence
Creativity
Efficiency
ImaginariesDigital taylorismABSTRACT
Current and near future organizational strategies are placing great emphasis on machines, robots
and AI. Automation to reduce menial or repetitive jobs, digitization of work to render remainingworkers more efficient and AI to provide more reliable and productive top-end professional work
are all inter-related initiatives enacted by current dominant imaginaries of efficiency and max-
imization. We argue that there is an Ellulian phenomenon of efficient techniques spreadingwithin technical logics that go beyond neo-liberal frontiers –namely, algorithmic approaches
which attempt to capture and reduce allmanners of human knowledge and meaning across the
efficient explication, formalization and manipulation of signs. Such purely ‘efficient ’and ana-
lytical approaches fail to recognize the unique and inimitable characteristics of human creativityand its associated tacit knowledge.
Inspirations from more holistic interpretations of Jungian symbolism allow us to provide a
starting point towards comprehending the complex, ambiguous, constantly emerging and es-sentially hard-to-de fine aspects of human creativity and tacit knowledge. This, along with the
argument that there exists a relationship between the democratization of knowledge and de-mocratic decisional processes, provides the basis to present an alternative imaginary of efficiencyas proposed by Feenberg (1999) . Such an imaginary, allows for the democratic participation of
humans in the decisional process and development of technology; and also recognizes and enactshumans as full legitimate partners with technology in their mutual shaping capacities –thus,
leading to human-centric organizations.
1. Introduction
What will the nature of human work consist of in the digital economy of the (near) future? A 2017 McKinsey and Co. report claims
that about half the activities carried out by workers today have the potential to be automated, but that "for most occupations, partial
automation is more likely than full automation in the medium term, and the technologies will provide new opportunities for job
creation." More speci fically, technology will create new jobs to compensate for those more highly impacted such as independent work
and other work that can be replaced via automation.
Others, such as Kim, Kim, and Lee (2017) , present a more sober analysis of technology ’s economic impact –arguing that even
though in the longer run human workers will be in demand for more creative work, the current workforce will undoubtedly facehigher unemployment, and that it is imperative governments provide various forms of intervention to ease the transition. Yet, not all
seem convinced of more creative jobs for workers; studies have identi fied new forms of managerial control, such as Amazon ’s
https://doi.org/10.1016/j.futures.2018.10.002
Received 4 July 2017; Received in revised form 3 October 2018; Accepted 5 October 2018E-mail address: [anonimizat] .Futures 105 (2019) 143–154
Available online 09 October 2018
0016-3287/ © 2018 Elsevier Ltd. All rights reserved.
T

Mechanical Turk ( Irani, 2015 ), a platform for outsourcing –and effectively hiding –the labour of coders. In a similar vein, The
Economist (Sept.10, 2015) speaks of ‘digital Taylorism ’or scienti fic management ’s comeback across a much wider range of jobs
beyond the traditional industrial context, including service workers, knowledge workers and managers themselves. Technology now
plays a frontline role in dividing work across a wider range of jobs, conducting time-motion studies at newer levels, as well as linking
pay to performance as a never-ending trial. Furthermore, some argue that the resistant and mobile creative class is now being
captured back into the digital system across quanti fication, by promising much but delivering very little in terms of work valorization
and recognition, all the while imposing the same spenceristic ‘rank or yank ’dogma ( Moore & Robinson, 2015). The end-result would
appear to be a continued knowledge transfer from both workers and knowledge workers alike towards management leadership, who
in turn, have taken measures to re-organize work in a very techno-centric manner –that is, towards automation, platform tech-
nologies and/or learning algorithms. Along with this transfer of knowledge is the transfer of associated decisional powers towardsthese same technologies.
In parallel, arti ficial intelligence (AI) appears to be making signi ficant advances into ‘deep learning ’and abductive-type algo-
rithms, with the aim of providing more creative work, as well as capabilities to imitate the more tacit practices, until most recently
considered to be the last bastions of capabilities reserved solely to humans ( Bogost, 2012; Patokorpi, 2009 ). Hence, even here, one
could argue that creativity is now being taken over by machines, thereby further reducing any human decisional powers withintechnology driven organizations.
This paper draws inspiration from the domain of responsible research and innovation (RRI), whereby we first conduct a her-
meneutical critique of dominant imaginaries based on the notion of e fficiency and maximization by establishing clear associations
between recent/on-going developments within the workplace and speci fic neo-liberal and instrumental epistemological strands as
previously discerned by authors such as Marcuse (1964) ,Heidegger (1977) andEllul (1980) . We also examine what we term as an
‘efficient re flex’of knowledge truncation often occurring within numerous contemporary domains, including certain streams of
computer supported cooperative work (CSCW), as well as what is known as arti ficial intelligence (AI). Here, we argue that the
phenomena of sign manipulation representing codi fied knowledge leads to a highly flawed epistemology which assumes that all
knowledge may be captured in an explicit manner, a view which we shall argue paradoxically enacts serious impoverishments in
knowledge and creativity. This is because such an epistemology fails to recognize the holistic nature of human symbolism and its
associated symbolic transformations. As such, an important aspect of human knowledge is neglected, mis-handled or virtually ig-
nored, namely, in the form of tacit knowledge. Very closely related to tacit knowledge is human creativity itself, which in turn, we
shall argue, relies on human heuristics which (again) involve symbolic transformations which allow us to make connections between
seemingly unrelated or disparate events or elements, and thereby allow the expert human eye to identify relevance where novice eyes
and machines (AI) alike cannot. As such, we make the key argument that human knowledge and creativity is indeed rich and far from
being outdated by the onset of machine automation and AI. Finally, an alternative e fficiency is proposed based on Feenberg (1999)
vision as a starting point to reverse this latest attempt to shift knowledge and power towards the ruling elite and its associatedtechnologies. It is an e fficiency based on a human democratic engagement in technology development, as well as organizational
arrangements, which aims towards overall human well-being. It is also an e fficiency
which echoes Dewey (1927) vision of enhanced
democracy across a democratization in human knowledge and creativity.
1.1. A few words on methodology
As mentioned above, our methodological approach draws upon RRI, whereby we first conduct a critique of existing dominant
imaginaries prior to proposing an alternative one ( Grove et al., 2016 ;Simakova & Coenen, 2013 ). The concept of imaginaries has
drawn attention from various disciplines such as sociology, philosophy and media studies ( Alasuutari & Andreotti, 2015 ;Giddens,
1984; Habermas, 1996; McLuhan, 1964). Social imaginaries, or imagined versions of what ‘reality ’is, influence the way people make
decisions –or as the Thomas theorem states: "If men defi ne situations as real, they are real in their consequences". Similarly, the
concept of imaginaries in the socio-technical sense involves ways in which dominant visions of societal futures centred around certain
types of technological developments have e ffects on what happens at present –or as Jasano ffand Kim (2009 : 120) explain, such
dominant imaginaries involves "producing authoritative representations of how the world works –as well as how it should work".
It is argued that critical studies of socio-technical future imaginaries for RRI are necessary in that it allows for the assessment of
assumptions underlying social priorities which have helped to shape possible (and actual) technological pathways ( Grove et al.,
2016). In addition, such studies allows for the exploration of more desirable social and material worlds which can emerge around
given socio-technical arrangements ( Macnaghten & Szerszynski, 2013 ). Furthermore, across the ‘hermaneutic turn ’, the emphasis of
technology assessment is placed on future societal developments (and respective social priorities) with technology, as opposed to
future developments in technology alone ( Stilgoe, Richard, & Phil, 2013 ).
Along these lines, our initial hermeneutical assessment will uncover how the basic Neo-liberal epistemological assumptions of
efficiency and maximization, as materialised across both historical and more contemporary forms of Taylorism, are at the heart of
many current and near-future workplace developments (ie. workplace automation, surveillance technologies, novel ways of self-imposed pay to performance, etc.). It will also be shown that the presence of dominant imaginaries of e fficiency and maximization
within current managerial and technological practices and developments can be extended into what is termed as AI.
Indeed, an interesting aspect of AI is in regards to its foray into ‘reproducing ’human creativity and tacit knowledge which is
regarded by some as one of the final frontiers towards the objective of attaining intelligence that will be equal or superior to human
intelligence ( Bogost, 2012). This, in itself, is critically reviewed while attempting to de fine what many consider as (partially or fully)
undefinable, namely, human creativity and its associated tacit knowledge.W.D. Holford Futures 105 (2019) 143– 154
144

Finally, an alternative imaginary will be discussed which builds upon the arguments that both creativity and its associated tacit
knowledge are indeed uniquely human attributes –that is, to view and enact humans as legitimate partners with technology in both
their mutual shaping capacities, and respective strengths and contributions in future organizations –namely, human creativity and its
associated tacit knowledge in partnership with machine e fficiency.
2. Dominant imaginaries based on e fficiency and maximisation
In this section, we present dominant social imaginaries based on e fficiency and maximization. Towards this end, we mobilize the
positions and arguments of Marcuse and Ellul in regards to the inter-related socio-economic and socio-technical dimensions of
efficiency and maximization. Adopting this hybridized view of the social imaginaries at play allows us to subsequently present a
wider perspective on its possible consequences. Of particular importance, is in the complementary rather than in the traditionallyoppositional manner in which both authors are be mobilised –Marcuse as a constant reminder that e fficiency and maximization are
deeply integrated within neo-liberal thought, using technology and techniques as a means to achieve its objectives; and Ellul as areminder that there is also an autonomous vector of e fficiency within technology and techniques which go beyond the frontiers of
neo-liberal thought. This latter point will be further developed in Sections 2.4and2.5to show how there is in fact an ‘efficient re flex’
embedded within various domains, including more collaborative spheres, such as certain streams of computer supported cooperative
work (CSCW) for example (Sections 2.4and2.4.1 ), leading to the likelihood/risk of knowledge truncation or impoverishment. We
begin this section with a brief description on the historical infl uences leading towards the neo-liberal socio-economic logics of pro fit
maximization and organizational e fficiency.
2.1. A brief retracement on the neo-liberal socio-economic quest for pro fit maximization and organizational effi ciency
While it is not within the scope of this paper, nor do we claim to be able to fully explain, how successive historical periods
culminated towards current neo-liberal thinking, a partial examination is perhaps of use. According to Saives, Ebrahimi, Holford, and
Bédard (2017) andAktouf (1999) , the Renaissance was characterized by a series of revolutions, namely of a political, a philosophical,
a technological, an artistic, a scienti fic, a religious and of a commercial nature. These reforms greatly infl uenced classical liberal
ideology. Of particular interest was the Protestant-Calvinistic reform, in which The Book of Common Prayer became the Calvinisticfoundation of future Anglo-Saxon Protestant ethics ( Weber, 1958 ). Here, concepts of calling, predestination and individual
achievement were manifested across the individual receiving “concrete signs of his predestination and election through the success of
his‘earthly vocations ’’’(Aktouf, 1995: 135). As such, classical liberal thinking argued that the accumulation of wealth was no longer
a vice, but in fact, a virtue. In parallel, was a first technological revolution involving the introductions of the first mechanical spinning
machine (1733) and first steam engine. Liberal economists such as Smith, Ricardo, Say and Mill elaborated the first economic theories
on productivity and production yields ( Saives et al., 2017). This period was characterized by the first concentration of humans in
workplaces
containing machines –i.e.factories .
It is in reference to this point in history that Marcuse (1941: 49) argued that the philosophical reform consisting of the en-
lightenment principles of individual rationality, autonomous and critical thought and personal liberty were gradually sectioned offand drawn into conformance in a manner that “…holds good for the functioning of the apparatus and for that alone ”. For example,
one notable reformer, Jeremy Bentham, envisioned a system of workhouses as a rational enterprise, across a design which he called aPanopticon. It was essentially a surveillance system with records and work-rules that became a key component of the liberal political
program in schools, hospitals and factories ( Clegg, Courpasson, & Phillips, 2006). Much later, the work of F.W. Taylor (1911 –
Principals of Scienti fic Management ), became responsible for creating the employee as a consciously designed utilitarian project by
stressing the need for national e fficiency, whereby "e fficiency means achieving desired e ffects or results with minimum waste of time
and effort; through minimizing the ratio between e ffective or useful output to the total input in any system" ( Clegg et al., 2006 : 48).
The ‘why ’of this quest, according to Taylor (1911) , was to attain higher pro fits for factory owners and higher wages for workers. But,
as argued by Clegg et al. (2006) , underlying this quest for e fficiency was a shift in more power across control and surveillance into the
hands of management. By then, Marcuse (1941: 49 –50) had gauged the growth of modern technological rationality from the early
stages of its modern development into its establishment in twentieth century industrial society consisting of “unconditional com-
pliance and coordination (and) the subordination of thought to pre-given external standards". Fast forwarding into today ’s neo-liberal
ideology, it is argued that e fficiency remains a dominant "cultural logic" which "values the fastest and least expensive production and
distribution of any product, as well as fast and inexpensive modes, technologies, and behaviors. This logic is concerned with bal-
ancing the quality of the product with the speed and cost of its production" ( Clair, 2016: 176; Gere, 2008). By cultural logic, Clair
(2016 : 173– 174) refers to a “construct that has been used by many but defi ned by few …as a shared and collectively imagined
prescription for individual or collective social action …They are prescriptions and practices picked up and used by individuals in
varying circumstances and contexts ”. In a similar manner, more recent manifestations of one of e fficiency ’s ever-present prescriptions
or mantras, namely, ‘doing more with less ’, has been witnessed across both manufacturing automation ( Autor, 2015) and increased
work intensi fication
amongst professional knowledge workers within more flexible work practices ( Kelliher & Anderson, 2010).
Accompanying this on-going quest, remains the panopticonic practice of surveillance and monitoring across wearables and other
technologies ( Moore & Robinson, 2015). In similar fashion, we can fast-forward the Liberal elite ’s Calvinistic quest for wealth
accumulation to today ’s neo-liberal cultural logic of pro fit maximization in "inter-action" with e fficiency ( Clair, 2016: 174) –that is,
"any product (material or symbolic) should be placed in the marketplace and should be priced in such a way as to maximize the
economic pro fit the seller receives from it" ( Clair, 2016 : 175– 176). In turn, Marcuse (1941: 41) argued the inter-relationship betweenW.D. Holford Futures 105 (2019) 143– 154
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the growth of technology and the neo-liberal objective of making pro fit by "business, or power over other men", whereby "the
development [of] machines have extended these aims and provided a vehicle for their ful fillment".
In the next sub-section we take a closer look at e fficiency and maximization from the ternary socio-technico-economic per-
spective.
2.2. A socio-technico-economic view on e fficiency and maximization
Efficiency is a major theme emerging from recent and current theorists on technology. According to Heidegger (1977) , the essence
of technology is anything but technological in that it is a kind of thinking that reveals to us only one way of existing whose essence is
to seek more and more e fficiency for its own sake –that is, producing the most with the least energy possible ( Hanks, 2010; Van Vleet,
2014), whereby "expediting is always itself directed from the beginning …towards driving on to the maximum yield at minimum
expense" ( Heidegger, 1977 : 15). In a similar, yet complementary vein, Ellul (1964) technological society is a society of techniques
requiring that we always choose the most rationally e fficient techniques for every endeavour. Technique is a mindset or ideology
which values e fficiency over all other things ( Ellul, 1964 :1–20). This "certain frame of mind …of looking at situations", which seeks
maximum yield with least amount of e ffort, involves a rationality consisting of mathematical calculations, systematizations and the
creation of standards ( Ellul, 1980 : 23). And finally, Marcuse (1964 : 158) rejoins Heidegger ’s explanation of technology ’s essence as
consisting of a Gestell or enframing in which human agents and the natural environment are treated as resources, in that "technics
becomes the universal form of material production, [in that] it circumscribes an entire culture; it projects a historical totality –a
'world'". Keeping in mind that both Ellul and Marcuse explicitly showed an on-going link and interrelationship between technicalefficiency and the on-going economic quest for pro fit maximization and economic growth, it is not surprising that we find a great deal
of technical e fficiencies integrated within various workplace environments, such as in current and future workplace automation
developments and objectives, which according to Autor (2015) is‘to do more with less ’.
In a general sense, inherent to the phenomenon of e fficiency is maximisation –whether we are speaking of Heidegger and
Marcuse ’s essence of technology or Ellul ’s more socio-cultural stance, maximum e fficiency is sought. This seemingly trivial point
leads us to ask what or who is behind this objective of maximum e fficiency. According to both Heidegger (1977) andEllul
(1964) ,
technology is autonomous and all pervasive, thus proposing a substantivist stance on technology –ie. it is not neutral, but rather
embodies values which shape our lives. Here, technology is both political and self-determining. For Ellul (1964 : 133), techniques
have become autonomous because of our refusal to interfere in any away with e fficient reasoning. Although an essentialist, Ellul does
not deny the psycho-social element of technological determinism ( Harris & Taylor, 2005: 35; Holford & Saive, 2013) in that "he
provides a detailed …account for the alienating properties of technology without denying, at the root, that human intention originally
lies behind such subsequent alienation." Ellul ’s interpretation of e fficiency and maximization includes the Weberian tendencies
within capitalist-managerialist societies towards instrumentalisation of reasoning processes coupled with metrics of e fficiency to
achieve maximum pro fits. But for Ellul (1981 : 155), the phenomenon of Technique is all pervasive and goes beyond capitalism –
irrespective of whether we are speaking of neo-liberal capitalism or of a moderate and/or radical socialism. Contemporary adherentsof this view include Son (2013) andAlexander (2008) who have argued that e fficiency is prominent in contemporary society due to
the force of technology. Son (2013) in particular, argues that the prevalence of e fficiency can be attributed to the underlying
assumption that all elements can be controlled, including human and social elements, and that as such, everything can be planned
and measured.
Marcuse (1964) does not deny the control technology has on humans in that individuals are forced to submit to a one-dimen-
sional, technological rationality which restricts and changes human thought and actions ( Van Vleet, 2014). However, Marcuse ’s
interpretation of a dominant imaginary of e fficiency and maximization is di fferent to Ellul ’s in one key aspect –namely, that
technology is not autonomous and beyond the control of humans, but rather "technology is controlled by the elite and powerful in
society. They have no concern for the well-being of humanity, only for personal economic gain and for absolute control of the masses"
(Van Vleet, 2014). Yet, the pursuit of e fficiency and maximization fit within both an Ellulian or Marcusian narrative of technology.
Both views acknowledge today ’s digital culture ’s quest for e fficiency and maximization across productivity, timeliness, dependability
and salary reductions in not only monotonous tasks but in more complex professional tasks requiring elaborate analysis, calculations
as well as more tacit work competencies ( Gere, 2008 ). For example, high level reasoning can now be adequately codi fied via
algorithms, resulting in computers outdoing humans both in terms of speed and performance in a variety of tasks ( Autor, 2015).
Various middle management echelons are now being replaced by management decision systems involving data interpretation via
advanced data analytics, also known as algorithmic management. Such systems can replace highly cognitive and non-routine jobs,
and are now being used in various crowd-sourcing platforms to manage people, and improve ‘crowd ’productivities ( Yu et al., 2017 ).
Furthermore,
where more tacit type tasks are involved, machines can even master the task across emulation through a process of
exposure, training, and reinforcement –known as machine learning (or ‘deep learning ’)–Autor, 2015 .
In the next sub-section, we illustrate how this digital environment appears to follow what many authors consider to be ‘scienti fic’
management principles resembling the Taylorism of past.
2.3. E fficiency and maximization across digital taylorism: enter the gig economy
Both Ellul (1980) and Marcuse (1964) referred to Taylorism or scienti fic management as one of the principal modes of how
techniques of maximization and e fficiency materialised themselves across weberian-like instrumental thinking. Today, authors such
asAu (2013) ;Irani (2015) andMoore and Robinson (2015) , argue that digital technologies appears to have either brought backW.D. Holford Futures 105 (2019) 143– 154
146

Taylorism or simply made it more evident in many contemporary management practices within the workplace. Brown, Lauder, and
Ashton, 2011:7 –9), speci fically refer to "Digital Taylorism", a system based on the global organization of both routine as well as
"knowledge work", whereby the latter involves creative and intellectual tasks being subject to the same process as chain work. Their
argument is that, once codi fied and digitalized, such tasks can be conducted by automatic programs with computerized decision
protocols, thereby replacing human decisions and judgements. Such processes can be easily relocated across computerized globalconnections, thereby rendering many jobs easy to export, change or replace. Digital Taylorism essentially follows one or more of
Taylor ’s original principals by:
1 Breaking down complex tasks into simple standardized ones
2 Measuring/surveilling everything that workers do
3 Linking pay to performance
The negative impacts of standardization have, for example, been felt in the US public school system as well as the Danish home
care sector, whereby standardization has led to alienation amongst teachers and home care workers alike, thereby sti fling job sa-
tisfaction, creativity, as well as innovative approaches tapping into worker competencies and experience ( Au, 2013; Gerdes, 2008 ).
Here, Taylorism is clearly identi fied across the planning/management vs execution delineation, whereby teachers, for example, are
forced to execute standardized curriculum "that require no creative input or decision-making" on their part, while using "verbal
scripts that defi ne and limit what they can say as they teach" with the sole objective of addressing high stakes testing imposed upon
them by the US Federal Government ( Au, 2013:3 1 –32). In the Danish home care sector, over-emphasis on standardized protocols for
patient care muzzles or truncates the tacit knowledge which experienced homecare workers have acquired, as well as reduces their
levels of engagement within the profession, thereby a ffecting quality of services rendered ( Gerdes, 2008 ).
Many of the technology companies that have set the tone for today ’s businesses appear to be applying Taylorism as well, such as
Amazon ’s Mechanical Turk, an internet platform which allows companies to break jobs into smaller tasks and o ffer them to people
across the globe, known as ‘Turkers ’or contract workers who perform small tasks for menial pay ( Irani, 2015). This whole process
known as crowd-sourcing can also involve training of AI systems to learn new tasks that were historically too complex for a computer.
Here, Turkers are involved in menial and mentally wearing tasks involving the labelling of millions of data sets often in the form
videos, whereby each individual can be repeating the same simple action hundreds of times. In a more general sense, crowd work can
be defi ned as the performance of tasks online by distributed crowd workers who are financially compensated by organizations.
According to Kittur et al. (2013) , crowd work can replace some forms of skilled work with unskilled labour as tasks are decomposed
into smaller and smaller units. For example, speech transcription and copyediting are increasingly being accomplished with crowd
labor, whereby even more complex tasks such as writing, product design, or translation may be amenable to novice crowd workers
with appropriate technological supports ( Kittur et al., 2013; Yu & Nickerson, 2011). Such cognitive e fficiency at the expense of
education and skill development re flects yet another aspect of Taylorism in its original form, which according to Clegg et al. (2006) ,
involves the depletion and transfer of workers’ knowledge and experience towards total management control.
The growing use of surveillance systems is yet another characteristic of Digital Taylorism so as to monitor workers for two
principal reasons: 1) to ensure they are following standardized tasks and procedures, and 2) to ensure they meet performanceobjectives often in the form of speed or timeliness ( Parenti, 2001 ). Many companies now impose wearable monitoring technologies as
part of the new form of time and motion studies ( Moore & Robinson, 2015 ). Technological platforms which manage contract workers
such as amazon ’s Turkers, Uber ’s
taxi drivers, and other industries, is what is known as the gig economy –that is, labour markets
characterized by the prevalence of short term contracts or freelance work as opposed to permanent jobs. Workers in this economy arerequired to measure their own productivities with wearable technologies not only within the context of standardized tasks, but also
within more entrepreneurial and knowledge intense environments ( Moore & Robinson, 2015 ).
In a more general sense, Moore and Robinson (2015) refer to the Taylorist in fluence within the more entrepreneurial and
knowledge intensive environments as seen across the augmented use of steps 2 and 3 of Taylor ’s scienti fic management. As workers
experience intensi fied precarity, intense competition and anxiety for jobs, they internalise the imperative to perform using their
"mind to subordinate their body to the ego-ideal and hence to the economic system …a process increasingly supplemented by ma-
chines that expand processes of workplace discipline" across the use of wearable monitoring devices ( Moore & Robinson, 2015: 2).
Creative knowledge workers are now expected to incarnate a dialectic of self-observation and self-exploitation –leading to wide-
spread deception as knowledge workers compare their actual achievements with the myth of what they are supposed to achieve interms of valorisation and real monetary gains ( Moore & Robinson, 2015; Schmiz, 2013 ). Furthermore, knowledge performance is
based solely on what can be captured and codi fied as an end result, and thereby fails to recognize actions and tacit knowledge flows,
thus undervaluing the total output of workers ( Till, 2014 ).
The above examples of digital Taylorism con firms the spreading of e fficiency and control in a manner echoing Ellul (1980) all-
pervasive techno-instrumental logic and Deleuze (1992) neo-liberal society of control. Such systems have proven to be anti-creative
(Deleuze & Guattari, 1987 ) in all spheres of work, including the creative working classes now increasingly under the control of
digitized scienti fic Taylorism across (self-) monitoring and pay-to-performance –whereby discourses on the importance of creativity
in the workplace has been in dissonance with the resultant deception, demotivation and over-worked conditions associated withknowledge workers in actual practice ( Fisher, 2014 ).
From a Marcusian viewpoint of neo-liberal market dominance by the elite few, digital Taylorism leads to an inherent contra-
diction: today ’s neo-liberal economy in recognizing the importance of innovation and creativity towards creating new wealth, seeks
to appropriate creativity within a digitized system of command and control to augment e fficiency and maximisation, which in turn,W.D. Holford Futures 105 (2019) 143– 154
147

discourages human creativity in the first place.
In the next subsection however, we further develop Ellul ’s position that there is indeed an autonomous vector of e fficiency within
technology and techniques which goes beyond the frontiers of neo-liberal thought across what we will call an ‘efficient re flex’
embedded within various domains. Of particular interest is the domain of computer supported cooperative work (CSCW), in which
certain streams manifest this ‘efficient re flex’across a clear over-emphasis to capture and reduce allhuman knowledge into a codi fied
or explicit form. Such a rationality, which ignores the ambiguous and emergent nature of human symbolic transformations, as weshall later argue in sub-section 2.4.1, leads to the likelihood of knowledge truncation or impoverishment.
2.4. Speci fic shortfalls of CSCW as examples of over-emphasis on explicit knowledge
Thefield of computer supported cooperative work (CSCW), according to Roth, Tenenberg, and Socha (2016) , has been char-
acterized by a variety of ‘voices ’or discourses. Sometimes viewed as a catch all term or concept concerned with people, computers,
and cooperation in some form, people from di fferent disciplines with overlapping interests and concerns have come together to study
the current state of technology development and the understanding of use contexts within the workplace. Some, such as Suchman
(2009) , have taken a particular interest in CSCW as the design of computer-based technologies with a speci fic concern on the e ffect of
socially organised practices on their intended users. Others, such as Bannon and Schmidt (1992) : 12) however, view CSCW as "an
endeavour to understand the nature and characteristics of cooperative work with the objective of designing adequate computer-based
technologies". Ackerman (2000 : 199), on the other hand, warns against such a technology-centric approach emphasizing artefact
creation and generation of ‘cool toys ’, while ignoring how people really work and live in groups, organizations, communities and
other forms of collective life, thus leading to a gap between social requirements and technical feasibilityto produce unusable systems,
while "distorting collaboration and other social activity". Some see such distortions manifesting themselves in crowd-sourcing ( Kittur
et al., 2013) as discussed earlier in Section 2.3.
Of particular interest is Schmidt (2011) contribution as to the concept of ‘work ’which attempts to provide a comprehensive
description of what work actually constitutes, namely a description that goes far and beyond the prescriptive, so as to more fullyunderstand what people actually do in terms of "real" work in regards to activities which managers typically ignore. This approach
concurs with authors such as Saives et al. (2017) who speak of the wide gap often ignored by managers in regards to prescribed vs
actual work. Schmidt (2016) also raises an important constructivist argument in regards to the various incompatible ways in which
work awareness is viewed and theorized including the problematic ways in which the term ‘shared ’is misused, or when di fferent
kinds of textual work is conducted which ignores shifting metaphors and interpretations. An equally important point is that raised byRoth et al. (2016) : 389 –390), in regards to the absence of
discourse in CSCW in regards to unconscious or tacit awareness, such as "the
case for both interlocutors, who may navigate around some lamp pole on the way without being able to recall what they did evenwhen they had to do so on opposite sides of the pole. In the same way, the conversation itself occurs within a world of text that does
not need to bring to ‘conscious awareness ’anything other than the particulars of the speci fic topic of the talk". This point re flects
Collins (2010) argument that tacit knowledge (such as in the form of collective knowledge) cannot be wholly reduced (or ‘converted ’)
to explicit representational knowledge objects. This holistic view illustrates the entanglement which occurs between tacit and explicit
knowledge ( Holford, 2018 ). Here, Polanyi (1962) : 87) speci fically explains that a person ’s tacit skills are always cooperating with his
explicit knowledge, in that explicit knowledge involves articulated language made up of symbolic representations –yet the very
meaning of these symbols relies partly on the tacit which cannot be articulated. Hence, to understand the most formalized sentences, a
person is needed ( Polanyi, 1962 : 139– 141), in that language involves tacit bodily or phenomenological aspects which go beyond
what can merely be articulated ( Kupers, 2008 ).
2.4.1. Mixing up symbols with signs: enter the e fficient truncation of knowledge
The linguistic shortfall identi fied above is one that we find in various disciplines (IT, management, knowledge management, etc.),
that is, where we attempt to formalize or encode language and practice with the assumption that all can be captured in the form of
explicit knowledge (for example, Best Practices viewed as all-encompassing rules rather than as starting guidelines). On this issue,
Needleman and Baker (2004: 55) provide an interesting comparison between so-called ‘objective ’unequivocal concepts vs the
ambiguous constantly emergent characteristic of artistic symbols:
"A scienti fic notion has an unequivocal meaning and, in the exact sciences, a mathematical form, in the sense of course, of
quantitative mathematics. On the other hand, a symbol can never be taken in a final and defi nite meaning …a symbol itself
possesses an endless number of aspects from which it can be examined and it demands from a man approaching it the ability to seeit simultaneously from di fferent points of views …A symbol can never be fully interpreted. It can only be experienced."
Symbols are a means of complex communication that can have multiple levels of meaning (multivocal and polysemic), some of
which are ineff able –which is di fferent from signs, as signs have only one meaning ( Womack, 2005 ). In a similar manner, both Jung
(1964 :2 0–22) and Ricoeur (1976) emphasize the inexpressible nature of symbols in that it expresses something vague, hidden or
unconscious to us. People use symbols not only to make sense of the world around them, but also to identify and cooperate in society
across discourse ( Palczewski, Ice, & Fritch, 2012). According to Tillich (1987 : 46), there has been a great deal of confusion between
signs
and symbols. More speci fically,
"The mathematician has usurped the term ‘symbol ’for mathematical ‘sign’, and this makes a disentanglement of the confusion
almost impossible. The only thing we can do is to distinguish di fferent groups, signs which are called symbols, and genuineW.D. Holford Futures 105 (2019) 143– 154
148

symbols. The mathematical signs are signs which are wrongly called symbols."
Words or sentences in languages can be both signs and symbols, whereby they can either point to one explicit meaning or to
multiple levels of meaning ( Tillich, 1987). ‘Peter hit Paul ’or‘Peter was hit by Paul ’say approximately the same thing whereby the
identity of meaning is attributable to the grammatical transformation rule from active to passive verb form ( Rapoport, 1969 ). Yet, to
recognize that ‘every rose has thorns’ and ‘there are no unmixed blessings ’goes beyond formal grammar and semantic rules –it
involves symbolic transformations, "a jungle, whose depth psychologists have been valiantly attempting to chart" ( Rapoport, 1969:
373). Narratives, across symbols allow humans to "make meaning and to forge connections between seemingly disparate bits of
knowledge and experience" ( Blyler & Perkins, 1999: 245). Sapir (1934) describes symbols as being highly condensed forms of
behavior, and are saturated with both conscious and unconscious emotions. Symbols also serve as vehicles of conception for allhuman knowledge ( Langer, 1953 ). As such, the symbol represents the complex entanglement of knowledge as being at once tacit and
explicit ( Polanyi, 1962 ;Sanzogni et al., 2017; Holford, 2018). The sign, on the other hand, represents that which has been explicated
and reduced in the same way a word in the semiotic sense, stands to an arbitrary referent ( Jung, 1964).
Today, Ellul (1980) all-pervasive technique (as an ideology of e fficiency), consists of methods reminding us of Taylor ’s quest for
the‘one best way ’: namely, a method consisting of dissecting and reducing all experiences and phenomena into explicit signs or
codes, with the assumption that these signs and explications truly and completely re flect the ever-changing, emergent meanings of
the symbolic and the experienced, thus leading in reality to signi ficant levels of knowledge impoverishment (Tsoukas, 2003 ): in that,
acts of knowing not only start in our head, but also in our somatic (ie. sensorimotor aspects, gut feelings, etc.) and social (ie. relational
affinities, distributed quality, local interpretations, etc.) dimensions.
In the next section, we shall argue that this ‘efficient re flex’or technique is clearly present within the current trend of algorithmic
thought and AI, resulting in what we argue to be an accelerated trend of knowledge truncation with the accompanied stripping ofdemocratic powers within organizations.
2.5. AI ’sefficient enactment of truncated knowledge
Both traditional and more recent learning algorithms involve the processing of data that has been restructured and formatted
(Faraj, Pachidi, & Sayegh, 2018 ) within an objectivist (or representational) approach to knowledge that can be de fined,
measured and
formalised/codi fied as words, signs and numbers ( Szulanski, 2000 ). This IT view of the firm (Falconer, 2006; Selamat & Choudrie,
2004), as argued by Sanzogni, Guzman, and Busch (2017) , neglects an adequate understanding of what indeed constitutes the tacit
dimensions of knowledge.
Knowledge is embedded in practice ( Gherardi, 2009). Such is the case with Collins (2010) collective tacit knowledge whereby,
"dancing in a social setting, speaking a natural language, and riding a bicycle while negotiating tra ffic on a busy street are examples of
collective tacit knowledge. This is a unique human characteristic constituting the ‘ability to absorb ways of going on from the
surrounding society without being able to articulate rules in detail ’"(Sanzogni et al., 2017:4 2 –43 citing Collins, 2010: 125). We can
also tap into the constructionist arguments put forward by Glasersfeld (2002) –in that human mental operations lead up towards
mental/subjective constructions of reality. Such operations involve both the construction of action and symbolic schemes (the latteras interpretive semantics) leading towards sensorimotor and conceptual knowledge, respectively ( Glasersfeld, 2002). Each of these
schemes is constructed "based on unique personal experiences, which may be similar, but never identical to, another person's con-structions" (2002: 158). As Carter, Clegg, and Kornberge, 2008: 62) state, “short of a brain transplant, the capacity to know [(tacit
knowledge)] is not a transferable commodity ”.
The objectivist approach to IT and AI, on the other hand, dissects and stores all experiences across the practice of reducing
complex, in finitely interpretable and partially ineff able aspects of symbolic and experienced phenomena to mere unequivocal signs
(Faraj et al., 2018 ;Ananny, 2016 ), resulting in knowledge truncations and subsequent loss of human expertise. This is also the case
with machine learning algorithms involving image recognition techniques to emulate di fficult-to-articulate human experiences. For
example, in the health care industry, such algorithms are guiding radiologists toward certain diagnoses that compare favorably with
certified doctors, thereby initiating calls within the IT community to cease training radiologists ( Mukherjee, 2017 ). Yet, deep expert
understanding of these tasks are of utmost importance in that expert radiologists are still in better positions to make correct inter-
pretations when looking at complex situations involving weak, contradictory and/or disparate signals ( Faraj et al., 2018 ).
In a more general sense, such a quantitative turn towards the purely measurable and to that which can be formalized assumes that
digital data can stand for social life –and when applied in a widespread manner, enact and structure to become "constitutive of that
person" ( Faraj et al., 2018 : 64). Indeed, this Global Network of Machines, profuse in signs and automated representations, transforms
meaning negotiations into symbolic violence ( Berrio-Zapata, Moreira, & Santana, 2015 ). Network readers, as immersive readers of
the information network, willingly accept the new myths (in the form of meanings and connotations) produced by this network ofmachine algorithms designed to standardize minds. In the absence of tacit expert knowledge, and the accompanied amputated senses
and inability to transcend imposed paradigms, the digital global elite remain unhindered towards their goal of accumulating and
exercising their power –in that Barthes ’s (1988) possibility of meanings are now reduced into families of paradigms across the
unending machine generation of structured speech and rhetoric.
As such, with an increase in worker ignorance across knowledge stripping and thought control, comes the continued loss in power
to
engage in socially-structuring decisions. This last point constitutes one of our opening arguments leading towards an alternative
imaginary as proposed in Section 4.
In the next section, we first look at the unique and inimitable nature of human creativity. We then show how AI ’s foray intoW.D. Holford Futures 105 (2019) 143– 154
149

‘reproducing ’human creativity and tacit knowledge is flawed, yet also dangerous, in that once again, as we argued above, AI attempts
to present itself as a legitimate replacement to human creative capabilities, and thereby further strip human workers/citizens of their
democratic powers to in fluence and shape the evolution of technology.
3. The relationship of human creativity with the tacit/emergent dimensions of human symbolic transformation
Creativity has often been de fined as the ability and process of creating something new and useful ( Amabile, 1996 ).Boden (2004)
defines creativity as the ability to generate ideas or artefacts that are new, surprising and valuable. ( Seyidov (2013) has argued that
human creativity is inherently paradoxical –in trying to isolate and model creativity, we impose rules and generalities to it, keeping
in mind that creativity abhors rules in the first place. Researchers have acknowledged its complex and interdisciplinary character
(Pearson & Ingleton, 1994 ). In cognitive psychology for example, creativity has been examined from di fferent angles, such as the role
of traditional intelligence as well as emotions in relations to creativity ( Runco, 2007).
Of particular interest is on how symbols (as opposed to signs) can generate creativity. Here, Kuuva (2010) shows speci fically how
visual symbols evoke artistic creativity across hard to explicate emotions and imaginations. Symbols invoke not only explicit asso-
ciations but tacit emotional concepts related to bodily or embodied experiences in the form of sensations and motor activities
(Kupers, 2008; Womack, 2005 ). In turn, individuals create new knowledge when there is already a fundamental base of tacit
knowledge to draw upon ( Boden, 2004 : 12). Alony and Jones (2007) illustrate this across a qualitative ethnographic study, on how, in
the Australian film business, tacit knowledge residing within various film workers and specialists is transformed into new ideas which
in themselves carry both an explicit and tacit dimension. This synthesis of both the tacit and explicit realms is further highlightedacross researchers who have looked at what is known as ‘embodied cognition ’involving a synthesis of both mind and body and its
importance on how it helps us address mathematical concepts across both visual and sensory-motor memories ( Nemirovski,
Rasmussen, Sweeney, & Wawro, 2012 ). Here, we posture, gesture, point and use tools when expressing mathematical ideas as
evidence of our holding mathematical ideas in the motor and perceptual areas of the brain –whereby, when explain ideas, even when
we don’ t have the words we need, we tend to draw shapes in the air ( Nemirovski et al., 2012 ;Alibaba & Nathan, 2012 ). Hence, the
body, which contains a great deal of tacit knowledge ( Gherardi, 2009 ;Tsoukas, 2003 ), is an intrinsic part of cognition.
In the following sub-section, we argue that the sole emphasis on sign explication, formalization and manipulation within the field
of AI poses two important consequences: 1) the likelihood of limiting creative outputs in the form of finite paradigms or families of
creative ideas, and 2) structuring and limiting human creative thought towards an algorithmic logic of ‘what is ineff able or isn’ t
measurable does not exist ’.
3.1. AI ’s truncation of creativity: once again confusing the sign for the symbol
In
returning to Boden (2004) definition of creative ideas and artefacts needing to be new, surprising and valuable, we begin to
appreciate the complexity and challenges facing AI. Coming out with something new is not enough. The manipulation of explicit signs
(which is what AI algorithms do) may well give us a new combination never thought of before, yet is not considered creative ( Boden,
2004). One must achieve an idea/artefact which produces a surprise in regards to existing social practices and beliefs, as well asproducing value in regards to these same social beliefs and values. This is exactly what Boden (2010) refers to as being able to achieve
an idea or artefact that is relevant between seemingly disparate ideas, objects or concepts. This, as previously explained in Section2.4.1 , is what humans do across symbolic transformations (such as in narratives). Furthermore, being able to achieve something
significant and relevant requires knowledge of social beliefs, values and practices that are highly tacit (and often distributed) in
nature, which Collins (2010) refers to as collective (or strong) tacit knowledge. For these reasons, this is also why according to Boden
(2010 and2015 ), who has worked on machine creativity, AI cannot be as creative as humans since machines cannot determine what
is relevant to a human expert ’s eyes. This is partially because AI algorithms cannot manipulate or transform symbols; it can only
manipulate signs. Symbols, in themselves are meaningless, yet, when in the presence of humans, re flect endless and emergent tacit/
explicit meanings or signi ficances residing within humans.
Recent work in AI has been especially interested in reproducing human abduction, which has been linked to the ability to
generate new scienti fic discoveries. Human abduction combines logical reasoning, aesthetic judgement (the hypothesis must be
‘elegant ’) and pre-re flexive moves (Peirce speaks of ‘flashes ’)–in other words, it mixes intuition and reasoning ( Lorino, Tricard, &
Clot, 2011 ). It also involves the process of linking single events to the tacit knowing of "family resemblance" between those events
(Adloff, Gerund, & Kaldewey, 2015 : 133) –in other words, identifying relevance between disparate ideas. Human abduction involves
direct perceptual judgements in which "the abductive suggestion comes to us as a flash. It is an act of insight" as well as an inference
(Peirce, 1935 : 181; Anderson, 1986 ).Peirce (1935 : 189) gave the often-quoted formula:
The surprising fact, C, is observed
But if A were true, C would be a matter of course
Hence, there is reason to suspect that A is true
Thefirst step of perceived surprise can be directly related to the tacit, complex aspect of human creativity, namely the holistic and
ambiguous nature of relevance. On the other hand, AI ’s‘efficent re flex’has attempted to dissect out the human abductive process
through a series of statistically derived algorithmic steps, which "entails interfering with the phenomenon through complicated data
massaging" which becomes a form of truncation of the phenomena in question ( Patokorpi, 2009 : 124; Tsoukas, 2003 ). Such an
approach has been viewed as highly reductionist in that assigning ‘correct ’probabilities to events ignores their inherent subjectivities
(Tversky
& Kahneman, 2008: 114). As such, the related question of relevance limits the capacity of machines to reproduce humanW.D. Holford Futures 105 (2019) 143– 154
150

creativity only to what has already been defi ned ( Boden, 2015). Hence, we can at best speak of pseudo-creativity involving com-
binations of related structures that are defi ned and depicted as having clear or strong links, as opposed to a Gestalt type creative
process involving the connection of knowledge structures which were previously unexplored and unrelated ( Sorin, 2013). Further-
more, as Sorin (2013) explains, to be creative, determining varying degrees of relevance (starting with weak relevance) is an im-
portant step whereby humans explore and connect seemingly unrelated structures in which, as argued previously, human symbolic
transformation plays a key role –a step which cannot be defi ned with precision, and therefore, becomes di fficult to emulate in any
algorithmic model.
4. An alternative imaginary on e fficiency and democratic decisional powers
As we have argued throughout this article, organizations, have in both the past and recent past, wrestled decisional powers from
workers towards the managing elite across scienti fic management practices and digital Taylorism –while today, across new aug-
mented technologies are now doing more of the same in regards to professional knowledge workers. These latest manipulations range
from the phenomena of the quanti fied self, in which false senses of autonomy and conditioned identities are instilled across upper
management discourses of hero-like creators coupled with self-monitoring and self-driven practices to achieve imposed objectivesbased in maximization at all cost ( Moore & Robinson, 2015 ); to today’ s (and tomorrow ’s intended) use of AI to replace knowledge
workers ’creativity and tacit knowledge across flawed knowledge truncating and pseudo-creative algorithms based on pure sign
manipulations. Marcuse believed human action could change the structure of technological rationality across a continuous and
creative struggle with technology coupled with a reversal of decisional powers towards the masses from the current neo-liberal elite
(Van Vleet, 2014 ). Marcuse ’s primary argument was that such power must be re-distributed towards more democratic organizational
configurations which manifest authentic and co-active power within both society and the workplace ( Follett, 1924 ). How this could
be achieved, however, was not clearly addressed by Marcuse. Rogers (2008 : 94), posited that the problem of the democratization of
science and technology "is not a problem of science and technology per se, but is a problem with social organization and the way thatthe technological structures of industrial production enforce and reproduce the social structures of industrial society". As such, Dewey
(1927) provided an important first insight into the possible dialectic which exists between knowledge and power. Dewey (1927)
argued that the democratization of knowledge was primordial in the maintaining of democracy; whereby he believed in the capacity
of human beings for intelligent judgment and action when the proper conditions are furnished, and that in turn, was a means for
individual empowerment ( Fenstermacher & Sanger, 1998). Interestingly, Dewey viewed knowledge as consisting of dynamic acts,
that is, knowing-as-practice, rather than consisting of sterile static objects, "to be judged …by its purposive success rather than by
some supposed standard of accuracy of re flection of its object" ( Haack, 1996 : 652). When we fast-forward to the current decade,
Garrick and Clegg (2001) 's study of stressed-out knowledge workers within project-based learning environments also showsa clear
relationship between knowledge and power. In re-questioning the authenticity of the learning environments involved, they arguedthat ‘true’situations are in fact illusions whereby across "rules of inference, corporate capabilities are transformed into ‘facts ’, social
values as ‘inputs ’and irreducible assumptions become taken for granted as ‘given’… The ‘true’situation
is that such frameworks are
saturated with ideologies of technical-rationalism and market economics" ( Garrick & Clegg, 2001 : 123). Boje (1994 : 447) describes
the ubiquitous nature of such organised authority:
"Learning occurs in the minute-by-minute interactions and the spaces along the hallways, lunchrooms and e-mail networks. The
iron cage of the bureaucratic teaching machine is so ubiquitous and benign that the prisoners of modern learning no longer see the
bars, the gears, or question the learning agenda"
Within this ‘softly ’orchestrated learning comes the loss of power to question and make authentic decisions. Hence, it is primordial
that as a society we become mindful of the importance to retain our knowledge expertise and creativity, and as such, our ability toinfluence and decide. It is here that we canpropose an alternative type of e fficiency as by Feenberg (1999) in which e fficiency is
aimed towards overall human and ecological well-being. It is above all, an e fficiency that does not seek to shortcut or truncate tacit
human experience and meaning, such as we saw within classical scienti fic management ’s quest for ‘the one best way ’across ex-
ecutions and instructions (whether human or automated), as well as with subsequent algorithmic ‘sign’logics used in what is loosely
termed as AI. Feenberg (1999) definition of e fficiency recognizes the human capacity for symbolic transformation, and as such,
human creativity. Similarly, Feenberg (1999) sees technology as a space to be contested in which social groups have the opportunity
to influence and change technological design, uses and meaning. This rejoins, Garrick and Clegg (2001) call for dialogue and de-
construction which periodically puts in question social, political and economic issues –and where the resultant knowledge generated
is not constrained to limited families of thought or paradigms ( Rhodes, 2000). Thus, technology can be publicly questioned, in which
society can demand or carry out changes, such that technology development becomes more accommodating to democratic debate andreconstruction to serve human needs and goals. This thesis is similarly echoed across both Touraine (1992) concept of individuals as
subjects fully capable of being active and transformative agents of society, and Beck (2001) call for needed debate and conversation
between socio-political and technico-economic spheres in regards to the question of technological development. Along these lines,
McLoughlin and Dawson (2003) propose a perspective of mutual shaping of technology and organization. But beyond the mutual
agentive capacities of both humans and technology, McLoughlin (2002 : 7) emphasizes the human aspect of creative technological
change in which humans actively and creatively shape technology across alternative imaginaries or "metaphors" –what Morgan
(1986) refers to as ‘imaginations ’.
In complementary fashion, we also propose work con figurations which will consist of people aided by technologies which fa-
cilitate and/or stimulate one or more of the following key conditions required to stimulate creative organizations according toW.D. Holford Futures 105 (2019) 143– 154
151

Robinson and Stern (2000) :
1 Experimentation/trial and error
2 Serendipity
3 Creative stimulations
4 Communication
5 Authentic collaboration
For example, Stefan Sonnenfeld is a digital intermediate colorist who uses computers to alter the color of movies and TV shows
until they look spectacular. According to Austin (2016) , his artistry would not be possible without the technology. Such digital
technologies provide individuals with the capacity to do rapid trial and error iterations, starting from recon figurations to testing to
subsequent interpretation of results. Other technological systems based in social web are argued to increase the likelihood of in-dividuals experiencing serendipity through a combination of information, context, insight and activity; for example, certain mobile
applications which can send text suggestions to users throughout the day, whereby the contents of these messages is informed by
users ’experiences and interests ( Kefalidou & Sharples, 2016 ). A variety of media technologies can also aid individuals and/or groups
to augment both conversation and collaboration.
We realise that the alternative imaginary presented above is nascent and preliminary in nature, both in terms of vision details and
feasibility. Its prime objective is to provoke further discussion and debate. The spirit of the proposed alternative, however, furtherhighlights the main argumentation of this paper –namely, that we are currently caught within an Ellulian-like phenomenon of
efficient knowledge truncation and pseudo-creativity across the use of algorithmic logics consisting of pure explicit sign manip-
ulations. It is a phenomenon which enacts further knowledge impoverishment and limited creative outputs in the form of finite
paradigms across unending machine generation of structured speech and rhetoric. By presenting itself as thelegitimate replacement
to our unique human capabilities for symbolic transformation, it discourages us to exercise and tap into our full creative capacities.
Reversing the current knowledge-creativity transfer away from workers (with its associated decisional powers) most certainly means
facing barriers with deep historical roots.
While power is a human need ( McClelland, 1961), "in situations with large power di fferentials, people with more power have the
freedom to defi ne reality, which means they need to spend less time trying to understand how reality is constructed" ( Weick, 2009:
161). No doubt we are speaking of coercive power and control when we look at the Marcusian dimension of digital Taylorism(Deleuze, 1992; Fisher, 2014 ), and as such, organizational authorities that have adopted it wholeheartedly will most certainly be
resistant towards adopting an alternative view. Digital Taylorism, as discussed earlier, is also being applied towards the creativeworkforce, leading to either exhaustion and demotivation or out and out worker elimination (AI). Digital Taylorism ’s pernicious and
all-pervasive Ellulian dimension towards ever-increasing e fficiency, also means that decisional powers are now being shifted towards
technological entities (AI and learning algorithms) who’ s new-found autonomies also defi ne our realities. Becoming conscious of this
requires mindfulness on our part ( Weick, 2009 ). Mindfulness involves an expansive ‘attentional breadth ’or directing attention
toward external events and phenomena as well as internal states ( Dane, 2011). It is the first step towards making sense of things or
phenomena ( Weick, 2009 ). And it is here that we would like to terminate on an encouraging note –namely, that making sense is
recognized to be a highly politicised process whereby " …in reconstructing reality through discourse, actors in the field take part in
the redistribution of power itself" ( Zilber, 2007 : 1037).
5. Conclusions
Current and near future organizational strategies are placing great emphasis on machines, robots and so-called AI –with the
general aim to improve e fficiency (productivity), and maximize pro fitability.
This has manifested itself in various forms, including
automation to reduce menial or repetitive jobs, digitization of work to render remaining workers more e fficient (productive) and AI
to provide more reliable and productive top-end professional work –all inter-related neo-liberal initiatives enacted by current
dominant imaginaries of e fficiency and maximization. Yet we have argued that this dominant imaginary also goes beyond a
Marcusian reality of organizations to a ffect various domains such as CSCW, where, for example, there is an explicit or formal
‘cooperation ’between humans and technology. This is because of the Ellulian phenomenon of e fficient techniques spreading within
technical logics that go beyond neo-liberal frontiers across algorithmic approaches which attempt to capture allmanners of meaning
across the e fficient explication, formalization and manipulation of signs as representations of explicit knowledge. Such purely ‘ef-
ficient ’, analytical attempts to dissect and reduce all knowledge and experiences prevent us from recognizing one of humanity ’s
inherent complexities and strengths, namely, creativity and its associated tacit knowledge. By becoming aware of the underlying
instrumental imaginaries in both the technical as well as the economic domains of the digital economy, we can better understand the
current contradictory logic of seeking to possess knowledge and creativity within systems of command and control that fail to foster
its full richness by muzzling human creativity and its associated tacit knowledge in the first place. In turn, this has led to reduced
decisional or democratic powers within the hands of workers, and now reserves the same fate to the professional creative knowledgeworking class.
Inspirations from more holistic interpretations of Jungian symbolism allow us to provide a possible starting point towards
comprehending the complex, ambiguous, constantly emerging and essentially hard-to-de fine aspects of human creativity and tacit
knowledge. This, along with the argument that there exists a relationship between a democratization in knowledge and democraticdecisional processes ( Dewey, 1927 ;Garrick & Clegg, 2001), provides us the basis to present an alternative imaginary of e fficiency asW.D. Holford Futures 105 (2019) 143– 154
152

proposed by Feenberg (1999) . Such an imaginary, allows for the active and democratic participation of humans in the decisional
process and development of technology; and also allows us to view and enact humans as full legitimate partners with technology in
both their mutual shaping capacities, and their respective strengths and contributions –thus, leading towards human-centric or-
ganizations. It is acknowledged that this alternative vision is preliminary in nature, and is meant to be a starting point for furtherdiscussion and debate.
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