Full Terms & Conditions of access and use can be found at
https://www.tandfonline.com/action/journalInformation?journalCode=ctas20
Technology Analysis & Strategic Management
ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/ctas20
Integrating futures imaginaries, expectations and
anticipatory practices: practitioners of artificial
intelligence between now and future
Johanna Hautala & Toni Ahlqvist
To cite this article: Johanna Hautala & Toni Ahlqvist (2022): Integrating futures imaginaries,
expectations and anticipatory practices: practitioners of artificial intelligence between now and
future, Technology Analysis & Strategic Management, DOI: 10.1080/09537325.2022.2130041
To link to this article:  https://doi.org/10.1080/09537325.2022.2130041
© 2022 The Author(s). Published by Informa
UK Limited, trading as Taylor & Francis
Group
Published online: 30 Sep 2022.
Submit your article to this journal 
Article views: 295
View related articles 
View Crossmark data
Integrating futures imaginaries, expectations and anticipatory
practices: practitioners of artificial intelligence between now and
future
Johanna Hautala a and Toni Ahlqvistb
aSchool of Management, University of Vaasa, Vaasa, Finland; bFinland Futures Research Centre, University of Turku,
Turku, Finland
ABSTRACT
Artificial intelligence (AI) is a world-changing technology due to its
abilities to learn independently, process big data, and automate human
work. Imagining the socio-technical future is necessary, but challenging,
in the era of AI that rapidly developing technology has made turbulent.
In this study, we addressed the need to understand the ways AI
practitioners actualise their AI-related futures imaginaries at the
grassroots level and in the present. Our empirical case study concerned
Finland and Singapore, focussing on their AI strategies based on
interviews with 26 AI practitioners. We created a new conceptual
perspective by integrating three concepts: futures imaginaries,
expectations, and anticipatory practices. We showed that imagining
socio-technical futures is an ongoing process in which AI practitioners
repeatedly co-constitute the future in ‘the now’. These practitioners
interpret futures imaginaries as expectations and address AI-related
challenges via anticipatory practices. Whereas some AI practitioners
‘ride the wave’ of the AI hype, others are proactive and critically active
in developing and educating people on AI. AI practitioners hold
significant anticipatory agency that is actualised in the anticipatory zone.
ARTICLE HISTORY
Received 11 January 2022
Revised 13 September 2022
Accepted 23 September
2022
KEYWORDS
Futures imaginary;
expectations; anticipatory
practice; artificial
intelligence; Finland;
Singapore
1. Introduction
Artificial intelligence (AI) is a world-changing technology due to its ability to learn independently,
process big data, and automate work. Attempts to imagine socio-technical futures in the era of AI
range from dystopic warnings of intelligence that surpasses that of humans (Bostrom 2017;
Hawking et al. 2014) to co-creation between humans and machines, which would allow people to
focus on performing creative tasks and creating meaningful lives (Jarrahi 2018). Imagining socio-
technical futures in the era of AI is difficult because AI is a disruptive technology that may lead to
uncertain development paths (Majumdar, Banerji, and Chakrabarti 2018). Moreover, AI development
is prone to hype cycles: a peak of exaggerated expectations followed by the disappointment of ‘AI
winters’ stemming from decreasing funding and slow technological progress, and finally, a revival of
expectations (Dedehayir and Steinert 2016; Floridi 2020). Today, we are experiencing another round
of AI hype.
An increasing number of nations have created AI strategies to imagine socio-technical futures in
such turbulence, prevent unwanted development paths, and ensure competitive global positions.
© 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
CONTACT Johanna Hautala johanna.hautala@uwasa.fi
TECHNOLOGY ANALYSIS & STRATEGIC MANAGEMENT
https://doi.org/10.1080/09537325.2022.2130041
Proliferating research on AI strategies has led to identifying the need to understand the ways AI prac-
titioners actualise these strategies at the grassroots level (Fatima, Desouza, and Dawson 2020). Prac-
titioners include entrepreneurs and researchers who apply and develop AI. Such a need also exists at
the core of futures studies that aim to research, imagine, and conceptualise AI-related futures and
the socio-ethical opportunities and risks (e.g. Aicardi et al. 2018; Turchin 2018; Díaz-Domínguez
2020). So far, AI-related research in futures studies has focussed on the various levels of organis-
ational strategies and state policies (e.g. AI 2030 Study Panel 2016; Makridakis 2017; Farrow 2020),
as well as the fields that apply AI technology (Capatina et al. 2020; Liu et al. 2020). Moreover, a per-
spective that combines far-future imaginaries with AI practitioners’ activities in ‘the now’ is needed
to understand actualising futures.
In this explorative article, we follow Sovacool and Hess’s (2019) suggestion of creating and explor-
ing an integrative conceptual and empirical perspective as the next step for understanding the
actualisation of AI-related futures. Our perspective integrates grassroots and strategic levels, as
well as the time dimensions of the far future and ‘the now’, placing AI practitioners’ activities at
the centre of the research. We integrate three concepts in two areas of the academic literature:
futures studies and socio-technical research (Sovacool and Hess 2019). These concepts are futures
imaginaries (AI strategies) that structure the strategic levels of AI-related futures (e.g. Patomäki
and Steger 2010), their interpretation and mutation into practitioners’ expectations (e.g. Borup
et al. 2006), and anticipatory practices executed in reaction to expectations in ‘the now’ (e.g. Ander-
son 2010). We answer the following research questions: (i) What are the key AI-related expectations
that AI practitioners construct from futures imaginaries? (ii) How do AI practitioners work in relation
to the expectations and imaginaries?
This empirical case study was conducted in Finland and Singapore, which are geographically and
culturally different countries but hold interesting similarities to study AI practitioners. These
countries are technologically advanced, are ranked repeatedly among the top 10 on the digital com-
petitiveness index (e.g. WDCR 2020), and are populated with highly educated citizens. Therefore,
Finland and Singapore present good opportunities for applying and developing AI. For such stra-
tegic development, both countries also need to imagine the future through the AI strategies and
actualise such a future in AI practitioners’ work. Finland was among the first countries worldwide
to launch a national AI strategy in 2017, and Singapore followed two years later with a stronger
knowledge of AI-related development and AI strategies globally. The empirical material in this
study consists of the AI strategies representing futures imaginaries in both nations (National Artificial
Intelligence Strategy 2019; Finland’s Age of Artificial Intelligence 2017), interviews with 26 AI prac-
titioners at companies and universities, and observations of 20 AI-related events in both countries.
Our results show that imagining socio-technical futures is an ongoing process, in which AI prac-
titioners repeatedly co-constitute and actualise the future in the ‘now’. AI practitioners interpret
futures imaginaries as expectations and face AI-related challenges via anticipatory practices. Our
integrative theoretical and empirical perspectives demonstrate that AI practitioners operate in the
zone of anticipatory agency among far-future imaginaries, socio-technical expectations, and antici-
patory practices.
2. Conceptual perspective
2.1 Futures studies’ prospections on AI: two periods
In futures studies, researchers have discussed AI in two distinctive periods: around the 1980s and
from the 2010s onwards. These periods share a few similarities. First, expectations of AI concern tech-
nical development and societal effects, but researchers discuss the future in general rather than indi-
vidual AI practitioners’ expectations and practices. Second, both general and narrow visions of AI
frame the discussion: The former refers to the aim of developing general human-like intelligence,
2 J. HAUTALA AND T. AHLQVIST
and the latter refers to AI that only operates in specified tasks and environments (Boden 1984; AI
2030 Study Panel 2016).
The 1980s expectations of societal outcomes concern the use of general AI in expert systems
where human knowledge could be transferred and applied in medical diagnosis, legal advice,
financial planning, and education (Boden 1984, 63; Gurstein 1985, 664). The development of AI
was expected to change the interaction patterns, lifestyles, and division of labour (Boden 1984,
64). However, expectations for expert systems encountered difficulties because researchers could
not properly explain their reasoning, assess their problem-solving methods, or combine and transfer
knowledge between domains (Boden 1984, 65). Even today, AI remains narrow. This level of devel-
opment was visible in the technical expectations presented in the 1980s that focussed on restricted
AI technologies, such as machine vision, human–machine interfaces, robotics, machine translation,
language processing, and voice-to-text solutions (e.g. Boden 1984; Gurstein 1985).
In the 2010s, futures studies on the topic of AI underwent a renaissance (e.g. Makridakis 2017;
Turchin 2018; Yampolskiy 2019; Farrow 2019; Díaz-Domínguez 2020). The expectations currently
vary between moderate (narrow AI) and threat-oriented (general AI) perspectives (e.g. Hengstler,
Enkel, and Duelli 2016). As a threat, AI could become a superintelligence that threatens human civi-
lisation and alters society completely. Expectations of the technical development of AI still roughly
resemble those of the 1980s with such keywords as ‘learning processes’, ‘language and voice rec-
ognition’, and ‘machine vision’ (AI 2030 Study Panel 2016). Even though AI’s threat to certain pro-
fessions is still debated, expectations regarding some of AI’s societal effects have changed since the
1980s. For example, humans and AI are seen as co-operators, and AI is believed to assume an increas-
ing role in societal governance. In these contexts, building trust as well as ethical and moral codes for
AI is a key issue (Montes, and Goertzel 2019; Hengstler, Enkel, and Duelli 2016).
2.2 From general to grassroots: futures imaginaries, expectations, and anticipatory
practices
We created our conceptual perspective under the umbrella that Sovacool and Hess (2019) estab-
lished in their analysis of 14 theories. The two most powerful theories for explaining socio-technical
change are the multilevel perspective (MLP) and social practice theories. The MLP recognises socio-
technical transitions as transition pathways linking three layers: niche, regime, and landscape (e.g.
Geels and Schot 2007). However, MLP scrutinises historical changes, and other conceptualizations
are required to explain ‘future change mechanisms’ (Sovacool & Hess 2017, 711). Social practice the-
ories primarily rely on theories of human agency and practice to explain how change is implemented
(Sovacool & Hess 2017, 711–713), which we apply in our conceptual perspective. Practice entwines
materials, meanings, competences, and performances in ‘the now’. However, when we concentrate
on practices, we risk excluding structural elements and a future time dimension from the analysis.
According to Sovacool and Hess (2019), two further theories focus on a future dimension to
explain socio-technical change. The first is called ‘sociotechnical imaginaries’, which AI strategies
represent. These imaginaries are ‘collectively imagined forms of social life […] reflected in the
design and fulfilment of nation-specific scientific and/or technological projects’ (Jasanoff and Kim
2009, 120). However, temporal dynamics between the past, the present, and the future are tricky
to trace using the concept of imaginaries only (Sovacool and Hess 2019, 719–720). Second, the soci-
ology of expectations (SoE) explains that expectations about future technology lead to the creation
of narratives (and hype) that channel the development of technology through, for example, shared
beliefs and investments. However, although the SoE explains the formation of hype, it does not
explore how to avoid the costs of hype (Van Lente et al. 2013; Sovacool and Hess 2019).
To take the critique (i.e. ‘howevers’) into account, we apply the strength of the MLP and create an
integrative conceptual perspective from grassroots to the general strategic level(Geels and Schot
2007; Sovacool and Hess 2019). We choose three concepts that integrate time dimensions from
far futures to ‘the now’. First, to address the strategic level and far-future dimension, we apply
TECHNOLOGY ANALYSIS & STRATEGIC MANAGEMENT 3
futures imaginaries: large-scale socio-technical visions that direct varied expectations and practices
in a specific direction. Futures imaginaries map technological discourses into a ‘semiotic order’
(Jessop and Oosterlynck 2008) that frames our expectations (Patomäki and Steger 2010, 1057),
creates a ‘semiotic moment’ for social practices (Jessop and Oosterlynck 2008, 1157–1158), and
brings AI practitioners ‘into an as-if world in which given reality is surpassed and a different one con-
sidered’ (Beckert 2013a, 328). Such construction and the use of futures imaginaries could be called
‘futures semiosis’ (Ahlqvist and Rhisiart 2015, 102). In our empirical example, the national AI strat-
egies represent futures imaginaries. In both Finland and Singapore, policymakers led the creation
of the strategies, but the process also included consultation with experts in (practitioners of) AI,
and discussions with various actors. Thus, the practitioners were aware of the key discussions
around the AI strategies and could contribute to the creation of the futures imaginaries.
Second, expectations exist in human cognition and they are interpreted by reflecting the imagin-
aries (future) in today’s realities (now). Expectations are (sequences of) events that individuals deem
likely to occur in the future. Thus, expectations merge the far future and ‘the now’. Expectations
enable or restrict outcomes, and have a ‘structuring effect’ for the development of technologies
and practices (Alvial-Palavicino 2015, 140, 146). Beckert (2013a; 2013b) argues that capitalism is a
system of fictional expectations concerning creativity, credit, commodification, and competition
that can be analysed through strategies, for example (Beckert 2013b, 226–230).
Third, anticipatory practices are actualised in human activity as responses to expectations in ‘the
now’ (e.g. Anderson 2007, 2010; Alvial-Palavicino 2015). Anderson (2007, 158) describes ‘anticipatory
knowledge practices’ as activities that are realised as futures exercises (e.g. Delphi) and that are used
to construct innovation policies. Through such practices, anticipatory agency emerges (i.e. the stra-
tegic ability to construct future-oriented goals and implement them into actions; Ahlqvist et al. 2012,
824). These practices are entangled with affects, such as fear, hope, and anxiety (Anderson 2007,
158–160). When conceptualised further, the anticipatory practices form three categories: calculating
practices, which are realised via an instrument or method; imagining practices, which are realised via
knowledge creation; and place-based performative practices, which are performed with bodies
(Anderson 2010, 783–787). Multiple anticipatory practices catalyse wider engagement with the
future and futures knowledge, as well as construct an anticipatory culture (Ahlqvist et al. 2012,
822–824). Future-oriented planning and development processes include four types of futures knowl-
edge that are relevant for anticipatory practices and cultures (Dufva and Ahlqvist 2015, 253–254).
Codified knowledge can be expressed and transferred in written or visual form; articulated knowl-
edge is expressed in a specific process context (e.g. in a workshop or meeting); embodied knowledge
refers to the skills, competence, and expertise of the participants in a process; and off-radar knowl-
edge refers to the insights created in a process,such as scenarios, trends, and weak signals.
3. Materials and methods
We conducted this empirical study in 2019. University of Turku’s Ethics Committee approved the
research plan (statement 2/2019). The study consisted of interviews with 26 AI practitioners from
Finland (n = 14) and Singapore (n = 12; Table 1), as well as participant observations at 20 AI-
related events associated with the interviewees’ key networks in Turku and Singapore. We documen-
ted our observations in 64 pages of field notes. The interviewees were contacted by an email inform-
ing them of the study and interview content and providing a privacy notice. The interviewees
consented to participate in the interviews (or they could decide not to participate), and they were
given the opportunity to read and comment on the manuscript. In Finland, we identified the inter-
viewees at a self-organised meetup group, Turku.AI, which brought together AI practitioners from
industries, universities, and city administration to meet, share their knowledge, learn, and create
opportunities for collaboration. Turku.AI has grown rapidly since its beginning in 2017 and had
more than 650 members in 2020 (MeetUp 2020). We focussed our observations on Turku.AI
meetups. Turku is an interesting city in Finland with regard to AI because it is currently undergoing
4 J. HAUTALA AND T. AHLQVIST
a positive structural change due to a strong boost in industrial and economic development. As a
result, various industrial companies located in Turku have focussed on the development of AI. In
2019, the University of Turku launched AI Academy, which organises courses for business prac-
titioners to learn how to apply AI in their companies. In addition, in 2020, the new Faculty of Tech-
nology was created at the University of Turku to meet the local job market’s requirements for
engineers with extensive AI-related skills.
We observed the participants in Singapore in May and June 2019 at 10 AI events that the key AI
actors in and among the public sector, universities, and companies had organised. The most impor-
tant of these actors are SGInnovate, AI Singapore, local universities, and Innovfest Unbound. AI was
the focus of Innovfest Unbound, which is Asia’s largest innovation festival. The number of AI-related
groups in Singapore was much larger than that in Turku, which is why we selected interviewees from
all of the key groups. We identified the interviewees at these events. They played active roles in the
events – for instance, by presenting how they apply AI.
Eighteen of the interviewees work at companies that apply AI, and eight interviewees work at uni-
versities but on projects aimed at developing AI for practical applications (Table 1). Four of these
applications were in health care, three in creative fields, and one in job creation measures.
Twenty interviewees had education in the ‘exact sciences’, such as information and communication
technology, engineering, statistics, mathematics, natural sciences, and medicine. They understood AI
from a technical perspective, and most could develop AI through coding as well. Six interviewees (F2,
F3, F4, and F7 as well as M3 and M4) had education in business or the social sciences. As practitioners
of AI, they focussed their roles on leading or participating in AI projects.
We conducted the interviews face-to-face. They lasted for about one hour, and the discussions
progressed along the themes of this article, experiences working with AI, and the future and AI.
We applied the semi-structured interview method, where the conversation follows a predesigned
set of themes and their key questions, but gives the interviewee a possibility to discuss the
answers freely (Longhurst 2003). The interview themes and questions were designed based on
observations and discussions the author had in the Turku.AI meetings prior to the interviews. We
recorded the interviews and later transcribed them verbatim. In addition, we conducted a directed
qualitative content analysis of the interview transcripts (Assarroudi et al. 2018). We began by identi-
fying the core content in response to the conceptual perspective (futures imaginaries, expectations,
and anticipatory practices), and we wrote the core content as summaries to answer the research
questions. Afterward, we discussed and refined the questions based on our discussions. Then, we
visualised the core content in Figure 1. We deepened and focussed on the analysis via a conceptual
perspective in three rounds of visualisations, summaries, and discussions. In addition, we applied
Word (original transcripts and summaries), Excel (identifying the key content), and PowerPoint
(visualising) software.
Table 1. The 26 interviewees and their organisations (n).
Country Organization Practitioner’s relation to AI Identifiers
Finland (14) Company (12) Coder (6), Founder (5), Managera (1) . C1 to C6
. F1 to F5
. M1
University (2) Researcher/coder (2) . R/C1 and R/C2
Singapore (12) Company (6) Coder (1), Founder (2), Managera (3) . C7
. F6 and F7
. M2 to M4
University (6) Researcher/coder (3), Researcherb (3) . R/C5 to R/C7
. R3, R4, and R6
aLeaders of AI projects, most of whom cannot code.
bResearchers or leaders in AI projects, most of whom cannot code.
TECHNOLOGY ANALYSIS & STRATEGIC MANAGEMENT 5
4. Results
4.1 Futures imaginaries in Finland and Singapore’s AI strategies
We use the notion of a futures imaginary as an umbrella concept for the expectations and practices. Rel-
evant state-scale futures imaginaries in AI are depicted as various strategies that are widely realised in
OECD (2021) countries. Therefore, AI strategies represent state-scale AI futures imaginaries, and the dis-
cursive setting of AI in Finland (Finland’s Age of Artificial Intelligence 2017) and Singapore (National
Artificial Intelligence Strategy 2019) in the form of codified futures knowledge (Dufva and Ahlqvist 2015).
In Finland’s strategy, the aim was to become the global leader in the application of AI technology
in the private and public sectors (Finland’s Age of Artificial Intelligence 2017). Singapore’s strategy
followed the country’s wider aim of becoming a ‘smart nation’ (National Artificial Intelligence Strat-
egy 2019). Both identify AI as disruptive and pervasive technology that is ‘re-ordering social and
economic structures’ (National 2019, 5), although, ‘We do not yet know… all the things that [AI]
will have an influence on’ (Finland’s Age of Artificial Intelligence 2017, 11). Strategic actions are
needed to ‘survive and thrive’ (National Artificial Intelligence Strategy 2019, 5) and ‘get along’ (Fin-
land’s Age of Artificial Intelligence 2017, 11) with such disruptive change. AI is considered the new
electricity in both strategies, signalling the idea of a fundamental transformation that affects
society’s core (emphasis added by authors):
Like the advent of electricity, the potential impact of AI on society, economy, and Government cannot be under-
stated. Any nation able to master this technology will be able to create tremendous social and economic value for
its citizens. Societies that cannot adapt will fall behind. (National Artificial Intelligence Strategy 2019, 12)
AI is like a turbocharger… or… Popeye’s spinach… From the perspective of the citizen and user it is like the
new electricity, so usual that one does not recognize its activity, but so irreplaceable that without it, in future,
one cannot get along. (Finland’s Age of Artificial Intelligence 2017, 11)
The key difference between the Finnish and Singaporean futures imaginaries consists of directing
the domains of business and research, where AI is considered most applicable. In Finland, AI tech-
nology is considered an ‘active part of every Finn’s daily life’ and applied ‘in all areas of society –
from health care to the manufacturing industry’ (Finland’s Age of Artificial Intelligence 2017, 14).
Finns refer to groups of citizens and companies – that include also AI practitioners – to construct
the imaginary. Therefore, the message for AI practitioners is not directed via domains of economy
but rather through key terms such as ‘ethical’, ‘safe’, ‘democratic’, and ‘world’s best services’ (Fin-
land’s Age of Artificial Intelligence 2017, 14). The Singaporean vision is more accurate, but it was also
created two years later with a better understanding of the development of AI technologies and their
effects, in addition to understanding many other countries’ AI strategies. The Singaporean strategy
focuses on ‘key sectors of high value and relevance’ (National Artificial Intelligence Strategy 2019,
16). Although the entire workforce is mentioned, ‘engineers and entrepreneurs’ are specifically men-
tioned as the key innovators needed to fulfil the Singaporean imaginary.
4.2 Key expectations for AI
Both the Finland- and Singapore-based practitioners described the ways politicians, civil servants,
and business and event managers communicated futures imaginaries via speeches and media at
various events. Discussions at observed events represented articulated futures knowledge, where
futures imaginaries were open to questions and further argumentation (Dufva and Ahlqvist 2015).
The AI practitioners participated in and interpreted such discussions by positioning themselves in
the future of proposed imaginaries in light of their AI expertise and ‘the now’. They brought the
future imaginary closer to their realities and ‘the now’ into expectations. AI practitioners critically
recognised three key expectations that were similar in Finland and Singapore.
The first expectation (i.e. AI is the new electricity and will be applied everywhere) was discussed
by nearly all interviewees. The narrative that encourages everyone to learn AI is strong. Education for
6 J. HAUTALA AND T. AHLQVIST
‘anyone’ is available to all in both countries, along with support for those applying AI in. However,
interviewees based in Finland described the expectation from a critical perspective more often than
the interviewees in Singapore did. In Finland, the expectation that AI would be the ‘new electricity’,
also referred to as ‘hype’ (FIN: R/C2, F5, and C4), seemed to be based on various misunderstandings
about AI, such as calling it a ‘pseudo-science thing’ (C3, FIN) and ‘50% magic’ (C5, FIN), and the
media spread this ‘hype’. Although a few Singapore-based interviewees raised a similar critique
(SIN: C7 and M4), they more commonly concentrated on one’s role in developing AI. Singapore
was considered an optimal country for developing AI, with the ‘best universities, good resources
for funding, and ability to make quick decisions’ (M2, SIN).
The second expectation is that AI will rapidly be developed until it becomes as intelligent as a
person is. Among the interviewees, developers of AI (coders) were less certain about the technology
as ‘truth’ compared to the appliers of AI (managers and founders), which is common and is recog-
nised as a ‘certainty trough’ (McKenzie 1998; Brown and Michael 2003). However, they had a strong
belief in reaching a general AI that is as intelligent as a human is, with all but one interviewee expres-
sing this (R6, SIN). Determining the time required to reach general AI was difficult, and the proposed
times ranged from 15 to 300 years – ‘from a current hardware perspective, that’s just not feasible…
and the costs don’t make sense’ (M4, SIN). Moreover, the actual meaning of a human-like intelli-
gence varied. Some thought it possible to code ‘all the strange things of the human mind’ into
the general AI: ‘Just collect the basis of experiences that humans have… . I believe… one cannot
tell the difference [between AI and a human]’ (C2, FIN). Several interviewees believed that the differ-
ence between humans and machines will not be resolved because humans are ‘too complex’ (R3,
SIN) and ‘full of mistakes’ (C5, FIN). Therefore, achieving general AI was considered possible
through simultaneous changes in people, technology, and definitions of intelligence.
The third expectation (i.e. AI will lead to a new allocation of work between people and machines)
derives from the second expectation. Repetitive, routine work will be automated, and humans can
focus on being creative and on planning and leading processes (FIN: R/C1 and R/C2, F4 and F5, M1;
SIN: M2, R3, R4, and R6, M4, and R/C6). Although most interviewees considered this a desirable
outcome, they also recognised downsides. For example, interviewees questioned what happens when
people lose the sense of meaning they often obtain from their work (FIN: F3, F2, C4, and R/C1; SIN:
M3 and M4). Some saw increasing unemployment leading to dissatisfaction, rebellion, drug use, the
growth of extremism, andmental health issues (M3, SIN; C4, FIN). New divides between people were fore-
seen, such as the digital divide between older people lacking digital skills and younger people who are
able to learn about AI (FIN: C1 and C3, F4, and M1), or the divide between the ‘first type [of person] will
do what the machine tells them to do, and the other type will tell the machine what to do’ (R/C5, SIN).
Even though AI helps people to become more productive and effective (SIN: R/C5, R/C7, C7, and
R3; FIN: C6, R/C1, and F1), the amount of time required is not changing. Stress and the demand for
creativity go hand-in-hand:
Workers will becomemore stressed because… half the day, we are thinking of new ideas that uses a lot of brain-
power. The other half [is] just doing repetitive tasks… . But if AI were to be taking over repetitive tasks… ideas
don’t just come… . It’s not a forced process. (R6, SIN)
Interviewees considered regulating the development of AI important to managing such unwanted
expectations. They considered it a responsibility that global companies, nations, and practitioners
shared. Finland-based interviewees stated that the EU was important. However, some thought
that the development of AI has already escaped regulation because ‘governments have been
slow to understand what’s going on’ (R/C6).
4.3 Anticipatory practices
The expectations and future meet ‘the now’ via the work of AI practitioners. Their time-consuming
and challenging work is aimed at applying and developing AI. Their work-related practices are
TECHNOLOGY ANALYSIS & STRATEGIC MANAGEMENT 7
anticipatory because they concern the future in relation to expectations. Such practices represent
embodied futures knowledge (Dufva and Ahlqvist 2015). The intersection of the future and ‘the
now’ forms a zone for anticipatory agency (i.e. practitioners’ strategic ability to create expectations
by participating in and interpreting the imaginary as well as enacting anticipatory practices; Ahlqvist
et al. 2012). We identified three main anticipatory practices (Figure 1).
The first is called ‘riding the wave’. Meeting the AI hype and the expectation that AI will become
as important as electricity will require resources (e.g. funding) to develop AI and customers’ growing
demand to apply AI. The AI practitioners have taken advantage of the hype: AI is technically complex
and difficult to define. AI has become a word that is ‘just thrown around’ (C1, FIN) by the public,
customers, and funders without consideration what AI is. Many practitioners branded their compa-
nies and projects with the prefix ‘AI’. For instance, C6 (Finland), stated, ‘It is only AI in the Power-
Point… . In practice… [it is] machine learning.’, and C1 (Finland), “Our PowerPoints include the
word “AI” because, cynically said, it is supposed to be AI’.
The second category of anticipatory practices (i.e. taking up the challenge by being proactive
and critically active, Figure 1) answers the practical challenges of developing AI. These challenges
relate to technology, processes, people (Table 2), and the structure of technological development
(Alvial-Palavicino 2015). They demonstrate that in ‘the now,’ the common expectation that AI will be
the new electricity is not reality. Following Anderson (2007), AI can be considered an instrument or
method, which means that developing AI represents a calculated anticipatory practice. However, our
results demonstrate that calculative, imaginative, and performative (bodily) practices are connected.
Technical challenges have arisen in relation the availability, amount, and quality of data. Working
with data has been described as requiring calculation, imagination, and a bodily practice (Anderson
2007): ‘Wrestling with the data… looking at the data, clearing it’ takes ‘80, 90 percent of [my] time’
(C6, FIN). AI requires extensive high-quality data to train it, and such data are not everywhere but
have to be actively ‘hunted’ (C4, FIN), accessed, and created, which does not fulfil the expectation
that AI is the new electricity.
[Customers think they] have some data… but then we get an Excel with 200 lines − that is not big data… Then
it may also be that it [data] is like in [another country] in some drawer in a USB and no one has keys there… like
literally someone has to go and fetch it… and often, it is difficult to find a person who really knows what it [the
data] contains. (C4, FIN)
Figure 1. Futures imaginaries (strategic level) are interpreted by AI practitioners into key expectations (grassroots level). Antici-
patory practices (now) are formed in relation to expectations (future). Anticipatory agency forms in this zone.
8 J. HAUTALA AND T. AHLQVIST
Human-related challenges concern the difficulties with finding highly educated coders to develop AI.
The coders are expensive, demanded in the job market, and hence difficult to find and keep. Process-
related challenges occur when AI technology and people are brought together. Developing AI tech-
nology from an idea into a viable business product takes time and requires combining various tech-
nical experts (coders) and substance experts (company’s domain, business, etc.). To avoid ‘useless,
senseless, or harmful’ machine-learning processes, sound human learning processes are required at
the team level: It is ‘not what happens inside the machine but what happens around the learning of
the machine’ (F3, FIN). An AI product ‘needs quite a lot of technological infrastructure to be alive
somewhere as a service’ (C6, FIN), and ‘Once you scale it up, things start breaking’ (M4, SIN).
Anticipatory practices to ‘take up the challenges’ are further divided into two sub-practices
(Figure 1: i–ii). Proactive anticipatory practices (i in Figure 1) are aimed at developing AI and
‘riding the wave’, whereas critical anticipatory practice (ii in Figure 1) is aimed at not only developing
technology but also at educating customers and audiences about AI and the misunderstandings sur-
rounding the AI hype. Thus, critical practice risks ‘riding the wave’ in the future.
The companies had various proactive strategies. One strategy was systematic growth. To start,
organisations offered services that allowed access to AI data and training. Only afterwards did
they begin to apply AI in their core activities. Another strategy was to work in countries in which
data regulation is less strict. One strategy was to shift the company’s efforts from research (and col-
lection of data) to business (and training AI with the data). Another strategy was to shift the original
idea of the use of AI or to create a new and surprising use via collected data. For instance, one
company had unexpected results of human behaviour outside of its original scope but could use
the new results to offer their customers expanded services.
Many companies’ customers easily adopted the expectation of ‘AI as the new electricity’ and were
convinced that they needed AI: ‘Everyone feels like you sprinkle AI on a problem and it gets solved,
but the reality is a lot more difficult’ (M4, SIN). Sometimes the practitioners took on the role of the
customers’ critical educators and patiently explained why other technology might be better.
There are simple algorithms and rules that would solve the problem, but they [customers] just want to do it with
AI… how can we educate the customer groups so they would understand AI is just one way… and in some
cases, you do not need it?. (M3, SIN)
5. Discussion and conclusions
In this explorative article, we address the need to understand how AI practitioners actualise strategic
AI-related futures at the grassroots level and constitute them in ‘the now’ in the technologically
advanced countries of Finland and Singapore (Fatima, Desouza, and Dawson 2020). This need is
at the core of futures studies, but the empirical research on AI-related futures has so far concentrated
on nations, organisations, and fields (e.g. Makridakis 2017; Capatina et al. 2020; Farrow 2020; Liu et al.
Table 2. Number of times interviewees mentioned AI-related challenges in their organisations during the interviews.
Challenge All Finland Singapore
Technical-related 19 8 6
Data 13 3 5
Reliability of the product 2 2 0
Other 4 3 1
Process-related 13 8 5
From idea to a viable business product 7 3 4
Using AI in company’s processes 3 3 0
Time required to create a viable product/train AI 3 2 1
Human-related 12 7 6
AI experts (finding, keeping, costs, collaboration) 7 4 3
Customers’ AI awareness 4 2 2
Getting people to trust AI 1 0 1
TECHNOLOGY ANALYSIS & STRATEGIC MANAGEMENT 9
2020), whereas the perspective of AI practitioners has not been studied sufficiently (Majumdar,
Banerji, and Chakrabarti 2018). We create and explore an integrative perspective that has proven
to be efficient in explaining socio-technical change (Sovacool and Hess 2019). Our perspective inte-
grates grassroots and strategic levels, as well as the temporal dimensions of the far future and the
present, and it places AI practitioners’ activities at the centre of research. We apply three concepts
from socio-technical research, as well as the intersection of socio-technical research and futures
studies: futures imaginaries that structure the strategic levels of AI-related futures (e.g. Patomäki
and Steger 2010), their interpretation and mutation into practitioners’ expectations (e.g. Borup
et al. 2006), and anticipatory practices executed in reaction to the expectations in the now (e.g.
Anderson 2010).
When it comes to our research questions ([i] What are the key AI-related expectations that AI prac-
titioners construct from futures imaginaries? [ii] How do AI practitioners work in relation to the
expectations and imaginaries?), the practitioners in Finland and Singapore hold similar expectations
of AI: AI will be applied widely as well as develop quickly and in a human-like way, and it will trans-
form the allocation of work. To tackle the challenges of developing AI, we describe the anticipatory
practices of ‘riding the wave’ of the AI hype, being proactive and critically active in developing AI,
and educating people about AI. We demonstrate that AI practitioners transform state-led futures
imaginaries into expectations and create anticipatory practices and agency for purposeful work in
the uncertain and disruptive era of AI. In turn, the anticipatory agency of AI practitioners constructs
futures imaginaries, which create a co-constitutive relationship among futures imaginaries, expec-
tations, and anticipatory practices.
The article contributes to research on socio-technical change and futures studies. Our conceptual
perspective, which integrates futures imaginaries, expectations, and anticipatory practices, adds to
related theories. First, the MLP considers socio-technical change to have been actualised among the
levels of a detailed niche, regime, and general landscape (e.g. Geels and Schot 2007), but struggles to
grasp the future dimension (Sovacool and Hess 2019, 711), which our perspective adds.
Second, despite the interest in high-end technologies, empirical research on AI expectations has
been insufficient (except for Hecht 2018; Kerr, Barry, and Kelleher 2020; Meurisch et al. 2020).
Whereas the SoE explains the formation of technology hype, it cannot explain how to avoid related
costs (Van Lente et al. 2013; Sovacool and Hess 2019). We intentionally conducted our empirical
study during the peak of a hype cycle of AI development, which makes the finding of the conflicting
anticipatory practices of AI practitioners a very interesting addition to the SoE. Although the prac-
titioners ride the wave of the AI hype, which boosts the general futures imaginaries of AI, they also
take on the challenge critically: The practitioners educate people about AI and develop better solutions
to their problems that do not involve AI. With such practices, they challenge the futures imaginaries of
AI partly based on the misunderstandings of AI, which deconstructs and avoids the costs of the AI hype.
Third, social practice theories explain socio-technical change through human agency and prac-
tice. The temporal dimension of practice is ‘the now’, which is relevant for actualising futures but
also poses the risk of excluding structural elements and the future temporal dimension from the
analysis (Sovacool and Hess 2019, 711–713). We contribute to the temporal dimension of practice
by analysing practices within the structuring frame that futures imaginaries establish. We do this
by applying the concept of anticipatory practices; that is, human activity as responses to the expec-
tations in ‘the now’ (e.g. Anderson 2007, 2010; Alvial-Palavicino 2015).
Fourth, the study suggests that AI-related futures are being imagined in various temporal dimen-
sions ranging from the far future to the present. We show that AI-related futures that are co-con-
structed as ‘the now’ (actual solutions) affect the perceptions of the future (solutions becoming
possible), and vice versa. Thus, our conceptual perspective seems to be relevant in tracing the tem-
poral dynamics between different time dimensions (past, present, and future), which has been chal-
lenging for current theories of socio-technical transformation (Sovacool and Hess 2019, 719–720).
The finding of anticipatory agency actualising in the ‘anticipatory zone’ is specifically interesting
(Ahlqvist et al. 2012, 824). In this zone, the far future, the expected future, and the present come
10 J. HAUTALA AND T. AHLQVIST
together and create an oscillating system that constantly moves back and forth between the pos-
itions of the present and the future (Figure 1). AI practitioners make sense of imaginaries and expec-
tations, and they turn these into practices. The first movement begins with the present; that is, when
practitioners experience AI in the present and the way this experience then channels their future per-
ceptions. The second movement begins with the future expectations, which are outcomes of prac-
tical work with AI and of external impulses, such as industry expectations or even broader socio-
economic AI imaginaries. The future expectations influence the way practitioners perceive AI in
the present. Thus, grassroots practices and wider future imaginaries enable and structure grassroots
expectations to a certain extent. The AI-related futures are envisioned at organisational and indus-
trial levels, as well as in the spatial scales of regions and states. Such oscillating movement is a
process of futures semiosis at the general and grassroots levels in the era of AI (Ahlqvist and Rhisiart
2015).
5.1 Limitations and recommendations for future studies
Three key limitations of and related recommendations for future studies are as follow. First, this
research was conducted only in Finland and Singapore. The countries are technologically advanced
and are populated with significant numbers of highly educated citizens, but they are geographically
and culturally different. This study does not provide a comparative or broad overview of national AI
strategies, and it does not describe the methods AI practitioners could use to actualise AI-related
future imaginaries widely among different nations. However, this article is a first step towards build-
ing a theoretical framework that is suitable for such a comprehensive and comparative empirical
study, which we recommend for future studies. With a comparative analysis, one could investigate
national specificities, strengths, and weaknesses related to developing AI, as well as imagine and
actualise futures. Second, our analysis was based on a selective sample of individuals and used
semi-structured interviews, which means the sample could be sensitive or biased towards certain
kinds of professional profiles (e.g. knowledge of AI technology) or worldview. As a further research
topic, we recommend complementing the empirical sample of this paper with, for instance, a Delphi
analysis involving a larger panel of experts (see Rowe and Wright 2011). Delphi analysis could indi-
cate and quantify AI experts’ consensus regarding futures imaginaries. Third, our theoretical per-
spective was based on concepts published mainly in journals of futures studies and socio-
technical research. This theoretical background and our empirical strategy cannot explain, for
example, imagining futures based on long-term trends in AI development. Such trend data and
‘horizon scanning’ can – and should – be applied to understand further the creation of AI-related
futures imaginaries (e.g. Nemorin et al. 2022). As the final recommendation, research should inte-
grate collective far-future imaginaries with individual anticipatory practices. Our results emphasised
that the adoption and practices of using emerging technologies differ regarding the socio-cultural
and geographical context, as well as between organisations. Even if the Delphi method, trend analy-
sis, and horizon scan could provide a wider pool of experts and a longer timeline of analyses, they are
not suited to understanding the ways imaginaries and expectations are actualised into anticipatory
practices. However, such a perspective importantly develops futures studies that are often focused
more on the average or median of the global or industry-related scales and trends.
Acknowledgements
The authors thank the National University of Singapore, Department of Communications and New Media, for support-
ing our research visit in 2019.
Disclosure statement
No potential conflict of interest was reported by the author(s).
TECHNOLOGY ANALYSIS & STRATEGIC MANAGEMENT 11
Funding
This work was supported by Academy of Finland: [Grant Number 319872]; Finnish Foundation for Economic Education.
Notes on contributors
Johanna Hautala’s recent research is focused on innovation processes in the context of emerging technologies.
Toni Ahlqvist’s recent research focuses on regional planning and policy, and futures studies in the context of emerging
technologies.
ORCID
Johanna Hautala http://orcid.org/0000-0002-4451-6290
References
Ahlqvist, T., M. Halonen, E. Eerola, S. Kivisaari, J. Kohl, R. Koivisto, J. Myllyoja, and N. Wessberg. 2012. “Systemic
Transformation, Anticipatory Culture, and Knowledge Spaces.” Technology Analysis & Strategic Management 24 (8):
821–841.
Ahlqvist, T., and M. Rhisiart. 2015. “Emerging Pathways for Critical Futures Research.” Futures 71: 91–104.
AI 2030 Study Panel. 2016. “Artificial Intelligence and Life in 2030.” Stanford University.
Aicardi, C., T. Fothergill, S. Rainey, B. Stahl, and E. Harris. 2018. “Accompanying Technology Development in the Human
Brain Project.” Futures 102: 114–124.
Alvial-Palavicino, C. 2015. “The Future as Practice.” Tecnoscienza. Italian Journal of Science & Technology Studies 6 (2):
135–172.
Anderson, B. 2007. “Hope for Nanotechnology.” Area 39 (2): 156–165.
Anderson, B. 2010. “Preemption, Precaution, Preparedness.” Progress in Human Geography 34 (6): 777–798.
Assarroudi, A., F. Heshmati Nabavi, M. Armat, A. Ebadi, and M. Vaismoradi. 2018. “Directed Qualitative Content Analysis.”
Journal of Research in Nursing 23 (1): 42–55.
Beckert, J. 2013a. “Capitalism as a System of Expectations.” Politics & Society 41 (3): 323–350.
Beckert, J. 2013b. “Imagined Futures.” Theory and Society 42 (3): 219–240.
Boden, M. 1984. “Impacts of Artificial Intelligence.” Futures 16 (1): 60–70.
Borup, M., N. Brown, K. Konrad, and H. Van Lente. 2006. “The Sociology of Expectations in Science and Technology.”
Technology Analysis & Strategic Management 18 (3–4): 285–298.
Bostrom, N. 2017. Superintelligence. Oxford: Oxford University Press.
Brown, N., and M. Michael. 2003. “A Sociology of Expectations: Retrospecting Prospects and Prospecting Retrospects.”
Technology Analysis & Strategic Management 15 (1): 3–18. https://doi.org/10.1080/0953732032000046024.
Capatina, A., M. Kachour, J. Lichy, A. Micu, A.-E. Micu, and F. Codignola. 2020. “Matching the Future Capabilities of an
Artificial Intelligence-Based Software for Social Media Marketing with Potential Users’ Expectations.” Technological
Forecasting & Social Change 151: 119794.
Dedehayir, O., and M. Steinert. 2016. “The Hype Cycle Model.” Technological Forecasting and Social Change 108: 28–41.
Díaz-Domínguez, A. 2020. “How Futures Studies and Foresight Could Address Ethical Dilemmas of Machine Learning
and Artificial Intelligence.” World Futures Review 12 (2): 169–180.
Dufva, M., and T. Ahlqvist. 2015. “Knowledge Creation Dynamics in Foresight.” Technological Forecasting & Social Change
94: 251–268.
Farrow, E. 2019. “To Augment Human Capacity.” Futures 108: 61–71.
Farrow, E. 2020. “Organisational Artificial Intelligence Future Scenarios.” Journal of Futures Studies 24 (3): 1–15.
Fatima, S., K. Desouza, and G. Dawson. 2020. “National Strategic Artificial Intelligence Plans.” Economic Analysis and
Policy 67: 178–194.
Finland’s Age of Artificial Intelligence. 2017. “Ministry of Economic Affairs.” Accessed 12 November, 2021 https://www.
tekoalyaika.fi/en/reports/finland-leading-the-way-into-the-age-of-artificial-intelligence/.
Floridi, L. 2020. “AI and its new Winter.” Philosophy and Technology 33, (1–3).
Geels, F. W., and J. W. Schot. 2007. “Typology of Sociotechnical Transition Pathways.” Research Policy 36 (3): 399–417.
Gurstein, M. 1985. “Social Impacts of Selected Artificial Intelligence Applications.” Futures 17 (6): 652–671.
Hawking, S., S. Russell, M. Tegmark, and F. Wilczek. 2014. “Transcendence Looks at the Implications of Artificial
Intelligence - but are we Taking AI Seriously Enough?” The Independent 2014 (05–01): 9313474.
Hecht, J. 2018. “Meeting People’s Expectations.” Nature 29 (563): 141–143.
Hengstler, M., E. Enkel, and S. Duelli. 2016. “Applied Artificial Intelligence and Trust.” Technological Forecasting and
Social Change 105: 105–120.
12 J. HAUTALA AND T. AHLQVIST
Jarrahi, M. 2018. “Artificial Intelligence and the Future of Work.” Business Horizons 61 (4): 577–586.
Jasanoff, S., and S. H. Kim. 2009. “Containing the Atom.” Minerva 47 (2): 119–146.
Jessop, B., and S. Oosterlynck. 2008. “Cultural Political Economy.” Geoforum; Journal of Physical, Human, and Regional
Geosciences 39 (3): 1155–1169.
Kerr, A., M. Barry, and J. Kelleher. 2020. “Expectations of Artificial Intelligence and the Performativity of Ethics.” Big Data
& Society 7 (1): 1–12.
Liu, J., H. Chang, F. Yi-Lin, J. Yi-Lin, and B. Yang. 2020. “Influence of Artificial Intelligence on Technological Innovation.”
Technological Forecasting & Social Change 158: 120142.
Longhurst, R. 2003. “Semi-structured Interviews and Focus Groups.” In Key Methods in Geography (3rd ed.), edited by N.
Clifford, M. Cope, T. Gillespie, and S. French, 143–156. Los Angeles: Sage.
Mackenzie, D. 1998. “The certainty trough.” In Exploring Expertise, edited by R. Williams, W. Faulkner, and J. Fleck, 325–
329. London: Palgrave Macmillan.
Majumdar, D., P. Banerji, and S. Chakrabarti. 2018. “Disruptive Technology and Disruptive Innovation.” Technology
Analysis & Strategic Management 30 (11): 1247–1255.
Makridakis, S. 2017. “The Forthcoming Artificial Intelligence (AI) Revolution.” Futures 90: 46–60.
Meurisch, C., C. Mihale-Wilson, A. Hawlitschek, F. Giger, F. Müller, O. Hinz, and M. Mühlhäuser. 2020. “Exploring User
Expectations of Proactive AI Systems.” Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous
Technologies 4 (4): 1–22.
National Artificial Intelligence Strategy. 2019. “Smart Nation Singapore.” Accessed 12 November, 2021. https://www.
smartnation.gov.sg/inititatives/artificial-intelligence.
Nemorin, S., A. Vlachidis, H. Ayerakwa, and A. Panagiotis. 2022. “AI Hyped? A Horizon Scan of Discourse on Artificial
Intelligence in Education (AIED) and Development.” Learning, Media and Technology. In print, doi:10.1080/
17439884.2022.2095568.
OECD. 2021. “The Organisation for Economic Co-operation and Development.” Accessed 15 November, 2021. https://
oecd.ai/en/dashboards.
Patomäki, H., and M. Steger. 2010. “Social Imaginaries and big History.” Futures 42 (10): 1056–1063.
Rowe, G., and G. Wright. 2011. “The Delphi Technique.” Technological Forecasting and Social Change 78 (9): 1487–1490.
Sovacool, B. K., and D. J. Hess. 2019. “Ordering Theories: Typologies and Conceptual Frameworks for Sociotechnical
Change.” Social Studies of Science 47 (5): 703–750.
Turchin, A. 2018. “Assessing the Future Plausibility of Catastrophically Dangerous AI.” Futures 107: 45–58.
van Lente, Harro, Charlotte Spitters, and Alexander Peine. 2013. “Comparing technological hype cycles: Towards a
theory.” Technological Forecasting and Social Change 80 (8): 1615–1628. https://doi.org/10.1016/j.techfore.2012.12.
004.
WDCR. 2020. “IMD World Digital Competitiveness Ranking 2020.” Accessed 10 May, 2022. https://www.imd.org/
globalassets/wcc/docs/release-2020/digital/digital_2020.pdf.
Yampolskiy, R. 2019. “Predicting Future AI Failures from Historic Examples.” Foresight (los Angeles, Calif ) 21 (1): 138–152.
TECHNOLOGY ANALYSIS & STRATEGIC MANAGEMENT 13