STARA technologies are remapping careers: they can boost learning and productivity while also driving displacement, identity threat and opaque algorithmic gatekeeping; however, evidence is fragmented, skewed toward certain occupations, and needs more long‑term, sector‑specific study.

Significant advancements in smart technology, AI, robotics and algorithms (STARA) are changing how organisations design and implement work for the current and future workforce. Understanding the implications of STARA on work attitudes and behaviours is gaining the attention of scholars and practitioners (e.g. Brougham and Haar, 2018; Raisch and Krakowski, 2021; Tang et al., 2023; Ulfert et al., 2024; Yam et al., 2023), with existing findings highlighting the varied and significant effects that different types of new technologies can exert on people’s performance and wellbeing (e.g. Bankins et al., 2024b). New technologies have the potential to be disruptive in diverse ways, thereby shaping working roles, organisational contexts and people’s working lives and careers (e.g. Selenko et al., 2022). Most existing reviews on STARA’s role in organisational behaviour and personnel management (cf. Bankins et al., 2024b; Köchling and Wehner, 2020) focus on changes to work tasks and organisational systems, while also highlighting the essential role humans play in adopting new technologies. So far, less attention has been paid to how these technological innovations are shaping workers’ careers in both standard (e.g. employment) and non-standard (e.g. the platform economy) work settings. Careers “involve a sequence (or sequences) of work experiences over time … (forming) a complex mosaic of objective experiences and subjective evaluations, resulting in an enormous diversity in terms of how careers can take shape” (De Vos et al., 2020, p. 1). There is already some evidence of how STARA will shape people’s career paths, plans, decision-making and perceptions of employability across career stages (Bankins et al., 2024a). However, more contextualised assessments are needed, as existing research rarely differentiates between occupational groups or types of employment.Previous studies examining STARA’s role in career decision-making (Gati and Kulcsar, 2021; Bartosiak and Modlinski, 2022) underscore the importance of understanding STARA's holistic impact when exploring its influence on careers. Addressing this requires a dual focus: on the one hand, acknowledging the diversity of technologies that comprise STARA and their distinct effects on work and job design; on the other, examining their shared and cumulative influence across the broader spectrum of career-related choices, decisions and experiences.This Special Issue focuses on research investigating the effects of STARA in the context of careers, with attention to multilevel perspectives (Bankins et al., 2024a, b), encompassing both standard and non-standard forms of work and the broad spectrum of technologies that fall under the STARA umbrella (Tang et al., 2022). Together, the contributions advance understanding of how workers and organisations can leverage these transformative technologies to support, rather than hinder, career development – an especially important endeavour given that the future of work and how careers are developed, managed and experienced depend on understanding how technology is integrated into the workplace.To provide a coherent framework for this exploration, this editorial highlights four central research areas. We begin with core career themes (i.e. career transitions, development and success), which capture the principal outcomes and processes influenced by STARA. Next, we consider career stages, recognising that impacts and responses differ across entry, mid-career and later stages. Third, we examine contextual variations across sectors and across standard and non-standard forms of employment, emphasising that similar technologies may produce different career effects in different settings. Fourth, we highlight emerging topics that warrant renewed scholarly attention. Finally, we discuss the individual contributions in this special issue and consider how each advances knowledge on these thematic dimensions.Research on career transitions in the context of STARA has primarily emphasised the displacement of routine tasks and potentially roles, particularly in occupations where automation and robotics can substitute standardised work processes (Bahadure et al., 2024; Oosthuizen, 2019, 2022; Singh and Chandra, 2026; Singh et al., 2026). This has been accompanied by an emerging body of work highlighting the need to upskill and reskill workers in the face of organisational STARA adoption. Even in high-skilled jobs, workers are expected to adapt to technology-driven changes and integrate new digital competencies into their professional trajectories (Hani et al., 2025; Ibrahim and Abiddin, 2024; Singh and Chandra, 2026; Singh et al., 2026; Tariq, 2026).However, one crucial and relatively poorly understood aspect of technology-induced career transitions is their degree of threat or voluntariness. Some transitions may be involuntary, such as job loss due to automation. In contrast, others are voluntary, such as moving into technology-intensive roles or professions out of personal interest or choice. The extent to which individuals “own” these transitions is likely to shape how meaningful the experience is perceived, how readily they can identify with their new roles and how they adjust psychologically (Bankins and Formosa, 2023; Selenko et al., 2022). Research on occupational groups that have long navigated technological disruption – for example, radiographers, radiologists (Stogiannos et al., 2025; Perez et al., 2024) and librarians (Nelson and Irwin, 2014) – can provide valuable insights into these processes.Moreover, while research has primarily centred on high-skill contexts, less attention has been directed toward low- and medium-skill occupations, where STARA may also fragment or expand existing roles and limit, expand, or generate new career paths. These dynamics could pose unique challenges for workers navigating transitions without established trajectories, weak opportunities to voice concerns, or the necessary institutional support structures to buffer change, thereby potentially widening inequalities across occupational groups and particular cohorts of workers (Zajko, 2022).Career development in the age of STARA highlights the expanding use of digital learning platforms and AI-based training tools as central mechanisms for supporting continuous skill acquisition and professional growth. In parallel, there is a growing recognition of self-managed, technology-supported approaches to career development, where individuals proactively engage with digital resources to source career advice, enhance their employability and navigate dynamic labour markets (Bankins et al., 2024a). For example, Yuan et al. (2026; in this special issue) demonstrate that AI usage at work can simultaneously enhance thriving as well as induce identity threat, with employees’ learning and performance goal orientations driving career growth. This highlights the simultaneous brighter and darker sides of AI usage, and the complex psychological processes through which technology influences professional development.At the same time, important questions continue to challenge and potentially expand our understanding. One key issue is whether STARA-enabled development resources contribute to democratising access to opportunities or inadvertently reinforce existing inequalities by being more accessible to some workers than others (Özbilgin et al., 2025). Career advice and guidance that support career development may become more readily available via technology, but human advisors will still be needed, particularly for unique and complex cases (Bankins et al., 2024a). Another underexplored area is the long-term impact of algorithm-driven systems, such as AI-based career planning platforms and digital portfolio and performance trackers. These tools can influence career trajectories in multiple ways: for instance, they may provide on-demand, personalised feedback, suggest potential skill development pathways, or facilitate the management of digital career portfolios. At the same time, such systems may have limitations and unintended consequences. They can embed biases, amplify pressures for self-optimisation, provide only generic recommendations rather than tailored guidance and risk promoting a narrow view of what constitutes a “desirable” career – raising the question of whether optimal career trajectories can be defined or adequately captured by available data.Research on career success in the context of STARA has increasingly focused on the use of algorithmic systems for productivity and performance monitoring (Giermindl et al., 2022; McCartney and Fu, 2022), highlighting both efficiencies and new pressures in technology-mediated work environments (Yang, 2022). Concurrently, attention has grown around the importance of well-being and work–life balance, as organisations and employees navigate the psychological and social implications of technologically enhanced performance tracking (Norlander et al., 2021), which can include the tracking of employees’ emotional and physiological responses at work and during their non-work time using sensors and other algorithmically enhanced data collection systems (Downie et al., 2025; Weston, 2015).Understanding how STARA reshapes the determinants and perceptions of career success requires further investigation. One underexplored area is the role of algorithmic gatekeeping in career advancement, including opaque promotion processes and AI-filtered evaluations that may privilege certain behaviours or skill sets while limiting transparency and equity (Hillebrand et al., 2025). Metrics of career success – both objective (e.g. income, status) and subjective (e.g. meaningful work, recognition, career satisfaction) – are evolving in the context of changing work environments (Spurk et al., 2019; Shockley et al., 2016). However, when promotion and reward systems rely heavily on algorithmic assessments, such indicators of success risk being overshadowed by easily quantifiable productivity metrics, potentially marginalising less tangible but still meaningful dimensions of professional achievement. This also raises questions about how AI-based evaluation tools shape perceptions of performance and career success and how these technologies may redefine which skills, behaviours, or outcomes are recognised and rewarded in the workplace.Moreover, in non-traditional labour market contexts, such as the platform economy, performance and career success are increasingly captured through alternative, often real-time metrics, highlighting a growing divergence from traditional indicators and raising questions about how conventional and non-traditional measures can be integrated to fully understand career outcomes. This raises additional questions about how new forms of work – such as hybrid, gig-based, digital nomadism, or AI-enhanced – are redefining what it means to be successful in a career (Reichenberger, 2017).Super’s (1957) work decomposes the career span into stages that reflect three broad periods across the life course (noting that we do not focus on the growth stage): exploration and establishment, where workers crystallise a career preference, undertake training to achieve it and begin stabilising their careers; maintenance, where workers largely consolidate their achievements, but with openness to new challenges and ongoing upskilling; and disengagement, where workers move toward transitioning out of the workforce (Greenhaus and Callanan, 2006). Super’s model is age-agnostic, allowing workers to cycle through different stages regardless of their life stage. STARA technologies are influencing careers across each of these stages (Bankins et al., 2024a).In entry-level careers, STARA technologies are increasingly recognised as offering both barriers and accelerators. In terms of barriers, there are growing concerns that entry-level positions, including in high-skill occupations, may be or by STARA et al., 2025; and thereby traditional for from to more roles and the opportunities for to the that In terms of STARA technologies can enhance entry-level workers’ productivity et al., existing career trajectories, may also generate new et al., 2020) by the importance of some skills, training and over to new of work experiences and careers. may well be that such digital AI and with robotics or algorithmic systems, may access to career such as more complex work, shaping the of professional trajectories (Bankins et al., et al., important The long-term effects of and how career experiences of using these technologies impact professional identity and development are not well understood et al., 2022), raising questions about how technology-mediated influence career attitudes and growth trajectories the of STARA tools may be traditional and potentially opportunities for learning and personalised guidance for the to fully routine or tasks to AI may learning opportunities for their skill development and into the workforce Finally, the of AI and other automation technologies may not only the of entry-level available to high-skill but also their potentially redefining what professional work and the of that are et al., 2025; et al., mid-career research has largely emphasised and as for navigating digital with a particular focus on learning platforms and work to facilitate career and skill (Bankins et al., 2025). These are often as mechanisms to employability and adapt to evolving technological these key further One is the potential for career or from role or which may the of work and opportunities for skill and technology can also for career by and employees to engage in more and meaningful However, more diverse evidence on such outcomes across sectors is there is understanding of the extent to which organisations in and across career stages, or whether they focus on raising questions about access to development resources and long-term to mid-career stages also from STARA often and of working and that workers may to STARA is as important of work or to identity threat perceptions may be organisational to upskill workers to et al., on workers in the context of STARA has primarily digital skill and the risk of from the labour as well as to professional and in the face of technology et al., 2023; Bankins et al., and However, emerging evidence that age and experience can support more use of AI by the technology to knowledge and such as through and et al., 2025). workers a valuable as knowledge and of AI and systems, to organisational during technological to STARA not be as a it may also reflect by feedback, or perspectives that shape more and technology (Bankins and Formosa, of available STARA technologies means their diverse Some such as have been navigating technological changes the professions such as radiologists and in and in and have been occupational for workers have also experienced technological over time that have the and of and other These workers are also AI as a potential to and other et al., but also as a potential for their often examine how technological reshapes and occupational a understanding of will continue to be both (i.e. and (i.e. unique in how different occupations experience changes by this new of STARA which warrant in their For example, in terms of of transitions of technological in these sectors are limiting our to of labour In terms of in the career and in AI-enhanced significant as it is often how and be between human and algorithmic systems 2025; et al., et al., 2025). 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