Quality work in the future: New directions via a co-evolving sociotechnical systems perspective

Source: Sharon K. Parker, Timothy Ballard, Mark Billinghurst, Catherine Collins, Maureen Dollard, Mark Griffin, Wafa Johal, Karina Jorritsma and colleagues; Australian Journal of Management (CC BY) · 2025

We face the situation of radical change in work due to advances in AI and related digital technologies, with uncertainty about how this change will affect workers’ opportunities for meaningful work designs, as well as the flow-on effects for worker well-being, health, skills and productivity. A ‘technocentric fallacy’ assumes that technology itself is the primary driver of successful digital transformation. Yet we have learned from history that technological considerations alone are insufficient for human well-being and productivity. The long-established sociotechnical systems theory of work design advocates that the social aspects of work (e.g. leadership, culture, task allocations) and technical aspects of work (e.g. AI, robots) need to be jointly optimised to achieve quality work. In this article, we expand this theoretical approach to fit current challenges, and to enable its wider-scale application. Our team of social and technical scholars propose a ‘co-evolving’ sociotechnical systems’ (CeSTS) approach to the design, implementation, and use of digital technology in work contexts. CeSTS expands thinking across time and across levels of analysis to create a more proactive, and ultimately more balanced, approach. Achieving CeSTS requires interdisciplinary collaboration, methods that can track dynamic and emergent change, and a multi-stakeholder approach that both informs research and shapes change in work. Altogether, the radical changes in technology demand an equally radical shift in how scholars investigate, and ultimately help to shape, future work.

Imagine you have a crucial decision to make as part of your job, and you can approach colleagues for advice. Your colleague ‘Bluster’ tells you what he thinks you should do, at length, and then justifies why this is the case. Your other colleague, the equally smart and experienced ‘Prudence’, takes a different approach: she asks you what you think you should do, and then provides feedback, with evidence to support the decision and evidence against it. If this changes your mind, Prudence gives feedback on new options. Which colleague would you choose: Bluster or Prudence?

Your answer likely depends on several factors, such as the stakes of the decision and your available time, but for many purposes you are likely to prefer Prudence. Yet Tim Miller, the professor of artificial intelligence who developed this scenario (Miller, 2023), argues that the predominant design of contemporary AI is akin to Bluster: AI provides a recommendation and explains why the AI system made that decision, when in fact what might better support human decision-making is systems more like Prudence, providing – helping decisions makers to find evidence to critique their ideas. It is not difficult to see the benefits of Bluster-like systems: speed, scale, and the possibility to mitigate human errors. However, it is also not difficult to see the benefits of Prudence: the human worker maintains control of the decision, leveraging their human expertise, and the AI generates clearer bounds of accountability, reduces over-reliance on AI systems, mitigates skills erosion, and develops decision-making skills for novices (Le et al., 2024). Conceptualising AI systems as more Prudence-like opens up AI system designs to be interactive tools driven by the human user, rather than relegating the human user to a role of oversight only. Such a new approach would be to create, at the outset, technology that truly enables augmentation of human ability, rather than it being a tool that replaces human skills and delegates people to the role of monitoring technical systems.

This hypothetical example highlights a key point of view that we develop in this paper: that so far, a technocentric approach to technology dominates; one that fails to give sufficient attention to human and work issues and that threatens to undermine the quality of human work. Technological transformation, such as that involving the application of Artificial Intelligence (AI), sensors and robotics, is occurring more quickly and radically than any other technical change in the past 100 years. This change is being driven in part by drastic advances in computing, increasing access to data and unprecedented investments of up to around $1B per day globally. These technical breakthroughs have led to much speculation about the future benefits of AI and related digital technologies for businesses and work. However, exactly how these benefits can be realised has had far less attention. Moreover, the potential pitfalls of AI and associated technologies for work and workers have been glossed over, with a narrow focus on how many and which jobs will be eradicated or a broad concern with ethical issues (House of Representatives Standing Committee on Employment, Education, and Training, 2025). Technological transformation is a grand challenge for academic scholars globally (George et al., 2016) and generates a complex national challenge with crucial implications for local economic, legal, industrial, innovation, and other systems (Brammer et al., 2019).

Altogether, our aim in this article is to develop and advocate a work design perspective for understanding and shaping the effects of digital change on work and workers. Specifically, we propose a co-evolving sociotechnical approach (CeSTS) as a crucial foundation for building quality human work in the future. In what follows, we first discuss the value of a work design perspective for understanding technological change. Next, we delve more deeply into the history and application of a specific work design approach: sociotechnical systems theory, which advocates that the social aspects of work (e.g. leadership, culture) and technical aspects of work (e.g. AI, robots) need to be jointly optimised to achieve quality work, rather than prioritising the technological over the social, as commonly occurs. Next, while existing sociotechnical systems theory provides an important foundation, we argue that this theory needs expanding to be fit for the purpose of understanding contemporary technological change. We unpack our CeSTS approach to the design, implementation, and use of digital technology in work contexts, suggesting that researchers need to expand thinking across time and across levels of analysis to create a more proactive and genuinely balanced approach to technical change. Finally, we discuss how a CeSTS might be realised, suggesting the need for an interdisciplinary collaborative approach, the application and development of new dynamic methods, and a multi-stakeholder approach that both informs research and shapes change in work practices and policy.

From the perspective of productivity, many implementations of AI fail to generate benefits (The Economist, 2020). Scholars have identified the ‘fallacy of AI functionality’ because of the large amount of evidence of failed applications (Raji et al., 2022). A classic case is that IBM Watson recorded an 80% failure rate in healthcare applications, with little or no positive impact on care and billions of dollars wasted, largely due to an inability to integrate the technology into the existing work systems and culture, including a failure to consider work design (Strickland, 2019). Dysfunctional AI has caused, among other problems, wrongful arrests, lost access to healthcare, and misdiagnosed medical images; events that not only harm individuals but that waste resources and are expensive to rectify (Raji et al., 2022).

In 1986, Kranzberg (1986: 545) stated that ‘technology is not inherently good or bad; nor is it neutral’. Our analysis above confirms this idea: research shows that intelligent technologies can positively and negatively influence key work characteristics, affecting how much work is stimulating, has mastery and autonomy, is relational, and has tolerable demands. Since these aspects of work design, in turn, consistently affect critical individual and organisational outcomes, understanding the impact of technology on work design – and shaping this impact – will go a long way towards improving the consequences of new technologies for workers and society. However, while there is a recognised and strong imperative for designing, implementing, and using technology in ways that generate positive work designs, exactly how to achieve this outcome is much less well-understood. In other words, when there are technological applications at work, how do we avoid the bad and achieve the good? To address this question, it is necessary to move from a rather passive perspective focused on understanding the impact of technology on work design to a more proactive perspective focused on change.

A sociotechnical approach continues to be highly relevant to the intelligent technologies currently being implemented (Guest et al., 2022; Makarius et al., 2020). Once again, we are witnessing a ‘technocentric fallacy’ – an assumption that technology itself is the primary driver of successful digital transformation (Kane, 2019). Indeed, of the approximately $1B spent on technology daily, most of this spend is for advancing the technology. The technocentric fallacy, and the consequent lopsided resource allocation by government and industry, must be challenged because AI or similar technologies alone do not improve productivity nor generate healthy work, as our examples above have shown. Indeed, the pace of technological change means integration of social and technical systems is more important than ever for a functioning society, with some scholars advocating for a sociotechnical perspective as ‘the cornerstone in discussions about smart working in Industry 4.0 and 5.0” (Bednár and Welch, 2020: 291).

However there needs to be expansion of STS theories to make them fit for the contemporary purpose. Let us return to the idea that technology is changing the ‘what and how’ people work. One of the crucial emerging and unresolved issues is around human autonomy. AI systems lack transparency because of they rely on patterns in huge volumes of data and moreover may have self-learning capability, creating continuous change over time; even the developers of the systems do not know how they work. Crucially, the opaqueness of these systems reduces the possibility for humans to actively control the technology (Castelvecchi, 2016), with autonomy by workers being a crucial principle of sociotechnical systems theory and integral to the idea of ‘human in the loop’. If workers do not understand how a technical system works, they cannot effectively manage the autonomy they have been given, nor, at least in normative terms, can they be held accountable (Boos et al., 2013; Grote et al., 2024). We do not yet understand how to address this issue of control versus accountability with new intelligent systems. Next, we turn to the ways that that sociotechnical system theory needs to develop to help achieve better quality work in a digital future.

In this section, we draw on the past innovations in STS and the ongoing challenge of changing environments to propose a new perspective on STS. We introduce the concept of ‘co-evolving’ sociotechnical systems (CeSTS) as an integrating perspective for the future. Our co-evolving perspective highlights that both human and technical roles are undergoing continuous and interdependent change as they interact in a dynamic system. Our perspective extends the ‘human in the loop’ imperative in two ways. First, CeSTS incorporates a time perspective in which people and technology adapt together in a process of continuous learning. Second, CeSTS incorporates a multilevel perspective that considers the level of analysis where change unfolds. We describe these extensions next, summarised in Figure 1.

To date, attempts to elevate social priorities within STS tend to assume a stable technology that is already in place. The focus of sociotechnical experts, then, is on how social factors such as leadership, work roles and team structures fit into or around the existing technology. These efforts primarily aim to correct techno-centrism through a focus on the social aspects of work after technology is deployed. This approach can control some variances at the source when technologies are relatively stable. However, even in relatively fixed technology implementations, research shows the actual work frequently involves workers engaging in multiple work arounds, adaptions, and improvements over time, often because the technology design was not sufficiently suited to its purpose. In what follows, to develop our CeSTS approach, we first recommend a need to adapt the initial technology design by applying a social lens to the design. Second, we advocate the need to consider technology design, development, and use as co-evolving and dynamically affecting each other. Third, we recommend research reflects how people’s attitudes and skills change over time, and is mutually dependent on, and affected by, the wider work and technological system.

With respect to the first point, failures that have left humans ‘out of the loop’ can often be traced back to design flaws, such as technology inhibiting people from overriding incorrect decisions, as occurred in a case of algorithms incorrectly cutting health care support to particular groups (Raji et al., 2022). The design of technology for work systems often proceeds with minimal worker input. Instead, business effectiveness and efficiency dominate the decision-making processes involved in commissioning and designing technology. By temporally expanding sociotechnical systems thinking and applying it to the design phase of technology, new research questions are stimulated. For example, with respect to what tasks people do, and how they do them, we need to better understand the perspectives of different actors in the system. Designers and developers often have a job-replacement mind-set rather than an augmenting mind-set (Bailey and Barley, 2020), so understanding fully the attitudes and orientations of designers, as well as how to shift these mind-sets, is needed. Such reasoning also applies to the ‘where and when’ of work. For instance, the design of online platforms in which workers carry out their tasks and are managed through algorithms can significantly reduce the degree of autonomy afforded to workers (Lehdonvirta, 2018). In essence, the technological design might unnecessarily constrain the work design options. Exactly what sorts of alternative design options are feasible, and what it takes to get them into place, needs much more investigation.

More generally, there is much advocacy around the need for greater technological transparency as a solution to human and work challenges. But, showing the situation is more complex than this, one study highlighted that, even when the risks and uncertainty about an AI-assisted decision systems are communicated, users tended to over-rely on and over-trust AI systems (Schömbs et al., 2024). This suggests that transparency alone is not sufficient, and that autonomous system needs to embed human actions to ensure human oversight (Holzinger et al., 2025), along with governance principles (Australian Voluntary AI Safety Standard, 2024). If human oversight is ultimately impossible, then other changes in the work system – such as new approaches to accountability–will need to be designed. For example, Grote et al. (2024) argue that, as machines become more autonomous, and as the line between AI development and use increasingly blurs, it becomes more and more difficult to identify who is in control of the system and, consequently, who is accountable. The authors argue that managing these uncertainties requires decentralised forms of stakeholder governance, with governance issues needing to be addressed across the whole AI life cycle.

In sum, CeSTS defines the continuous adaption of people and technology as a central feature of any sociotechnical system. We advocate a need to investigate how to extend sociotechnical systems principles earlier in the process, including: to be part of the initial commissioning and design phases; how to better understand technology design, development and use as mutually co-evolving; and how to prioritise and recognise not only machine learning but also human learning and adaption in the work system.

However, despite the criticality of addressing sociotechnical concerns at the team and organisational level, we further propose that consideration of lower and higher levels of analysis is also crucial, both from a research perspective and to bring about real change. Thus, in CeSTS, our second point is that we advocate for equal consideration of factors at multiple levels of analysis including: technology design as discussed above; the level of individual workers (such as how individuals’ cognition, physiology, personality, and background affect their responses to new technologies); work teams (such as how team members share knowledge and learn, the trust between workers, or the impact of algorithmic management on team processes); organisations (such as how psychosocial safety climate, finance, size, and other organisational or technical systems affect technology implementation), and societal systems (for example, the role of regulations, industry and occupational factors, union density, labour markets, and advances in technology).

One especially powerful reason for considering sociotechnical aspects at multiple levels of analysis is that, to date, sociotechnical approaches have not achieved hoped-for outcomes at scale. Scholars have recognised that, on average, existing approaches have failed to embed the ideas into wider power structures and knowledge systems, such as into educational practices, union and worker agendas, and legislation (Guest et al., 2022). A focus on macro-level issues that shape power dynamics is especially important in the field of technology because key corporate stakeholders, such as CEOs or IT department leads, tend to align with a technocentric agenda in which the emphasis is on replacing human labour and efficiency (Bailey and Barley, 2020). In other words, cost-cutting business interests tend to drive technology development and application among large corporates, with the impact of technologies on workers’ safety, mental health, and inclusion being of much less concern. The need to counter these macro trends are gradually being acknowledged. An illustrative example in Australia are the recent changes to law that have strengthened the legal requirements to eliminate and control psychosocial risks (mental health risks for workers), which include the psychosocial risks that might arise from new technologies, such as new forms of worker surveillance and control (Safe Work Australia, 2024). Hopefully, these laws will be utilised as a lever to centre corporate decisions around human needs. Legislation to protect workers is an especially relevant topic in contexts like gig work, in which precarious workers are subjected to algorithmic control and other negative work conditions. Indeed, currently, there are multiple legal battles around the world concerning whether gig workers are classified as ‘employees’ or ‘independent contractors’, with this decision directly shaping access to workers’ legal rights.

We advocate that we are at a critical juncture for intervening to create quality human work. Industry and government are rushing to implement new technology without the evidence, care or safeguards needed to protect workers, or even to achieve the promised productivity and efficiency gains from the various technologies. There is a need to ‘redouble efforts to drive more humane and stimulating workplaces that benefit society more broadly’ (Collins et al., 2020). We hope that our Co-evolving Sociotechnical Systems perspective, and our recommendations for an approach to realise this perspective, helps scholars to move forward on this crucial agenda.

Critique of technocentric fallacy and expansion of Sociotechnical Systems Theory. The manuscript challenges the assumption that technology alone drives successful digital transformation, emphasising the need to consider social aspects of work. We extend the long-established sociotechnical systems theory to address current challenges posed by AI and other intelligent technologies.

Introduction of CeSTS Approach. The authors propose a ‘co-evolving’ sociotechnical systems (CeSTS) approach, which integrates social and technical aspects over time and across different levels of analysis. To address issues over time we need to: adapt the initial technology design by applying a social lens to that design; consider technology design, development, and use as co-evolving and dynamically affecting each other; and consider how people’s attitudes and skills change over time along with the wider work and technological system. To address different levels of analysis we need to consider: technology design, individual workers, work teams, organisations, as well as broader societal systems.

Interdisciplinary collaboration. Collaboration across disciplinary boundaries is needed to address meta-problem of digital work. Disciplinary boundaries currently exist on this topic create unhelpful divides (see Figure 2 for machine vs human autonomy).

Innovative methods are recommended to capture sociotechnical complexity. Recommendations include exploring new methods beyond action research, such as by collecting neuro-physiological and audio/visual data, in-depth field studies, computational models, and social network analysis.

A multi-stakeholder approach is needed. Close collaboration with designers, managers, educators, policymakers, regulators and other important work stakeholders will allow for a co-evolutionary process to support desirable future work scenarios.