Digitised and Disembodied: The New Ideal Worker
A Great Undefinable Threat
Despite what attention-hungry headlines of 2025 would have led us to believe, the imminent threat to work wasn’t AI taking everyone’s jobs. For a start, the term itself is too broad to be of any real discursive value, and the tendency to apply extreme language to the topic (e.g., ‘threat’) resulted in many simply switching off. After all, how can we engage in discussion and sufficient discernment when most of the coverage feels like someone running into a crowded room and shouting “Catastrophic event incoming!” - before promptly disappearing with no further information. The air time taken up with broad terms and scare tactics also left little oxygen for what is much more tangible, far less thrilling, and crucially, already in motion - the digitisation of work.
To avoid allegations of not following my own advice, let’s pause and anchor ourselves with a definition. The digitisation of work in the most general sense refers to an increasing use of digital devices, and the integration of Digital Information and Communication Technologies (DICT) into work design and processes [1]. Recent research suggests that digitisation is changing working conditions with employees are already experiencing negative effects [2]. The Future of Work is not some far off destination, nor is it determined. With that in mind, let’s consider what it is we want for our work selves.
The Ideal Worker
Throughout various transformational points in relatively recent history, we’ve watched a keenness for a certain type of worker evolve. One who is devoted to the work and able to log long hours. One who is competitive, resilient, and ambitious. One who is always available and will engage in everything from performative busyness to physical harm if it reflects commitment. Research from the past two decades suggests the Ideal Worker norm plays a role in “exacerbating gender inequalities, increasing turnover rates, and negatively affecting employees’ outcomes such as work-life balance, mental health, job satisfaction, and burnout.” [3]
Unsurprisingly, it’s extremely difficult to achieve this Ideal unless the employee has no responsibilities of care, is celebrated for being ‘ambitious’, can devote time to building necessary social capital, has been socialised to ignore the body in favour of logic (or, at the very least, productivity), etc. The Ideal Worker norm does two things:
It creates a very narrow category which excludes people who are primary carers, desire work-life balance, or are routinely punished for being ‘aggressive’ at work (e.g., women, Gen Z, people of colour, etc.).
It devalues the potential of an embodied approach to work (more in the next section).
The narrow category strengthens the allure of digitisation. That is, turning tasks into data points (to then be used in automated processes) promises to do away with the pesky issue of bodies at work getting tired, hurt, needing holidays, under-performing, desiring autonomy - or worse, job satisfaction! With the increasing use of digital technologies in work design and processes, perhaps this will manufacture only Ideal Workers and organisations can finally overcome the awkward and languid pace of the human form.
Embodied Knowledge
However, there is a slight problem with pursuing the Ideal Worker because complicated things like bodies are required in order to have successful work outcomes. For instance, much of the learning process, knowledge creation and creative problem-solving at work comes from embodied, affective, social configurations. That means people come together with their ideas, memories, concerns, and so on - and they contribute to a shared outcome. To assume the conversion of work tasks into a series of data sets will most necessarily result in better outcomes misunderstands this process entirely. It is via the process that things like knowledge and learning evolve and expand. If the process is to be a generative one, humans will have to show up with their emotions, experiences, curiosities, and quirks in order to add essential input.
Additionally, it’s important not to overlook flawed assumptions about data. Despite scholars of the past insisting as much, data is neither neutral nor objective. Rather, data are produced within unequal social relations and contextualising them as such is the only way to engage in ethical and accurate analysis [4]. In order to appropriately consider context, our assumptions must include a valued form of knowledge that comes from living and feeling in a body.
Attempting to codify knowledge by engaging in unethical and inaccurate data analysis risks doing little more than deepening issues commonly found in technological overreliance. As an example, a recent multidisciplinary review used Acker’s framework of ‘inequality regimes’ to highlight how using AI can exacerbate hiring inequalities. Researchers found that inequalities embedded in the data, design, and broader ecosystem of algorithmic hiring were particularly difficult to undo because they were invisible while appearing legitimate [5, 6].
A different direction
Work has a long history of trying to overcome perceived flaws of the human form. Images of body-breaking labour in fields and factories quickly spring to mind, however I would argue that the current industrial revolution continues with more of the same disdain. After all, what else would motivate the line of thinking that assumes: 1) work is a series of tasks, 2) which can be accurately represented by data points, 3) which can be efficiently managed by algorithm, 4) which can be scaled up to optimize employee output? On the contrary, what happens at work is much more complex and far more human than data sets can solely represent. The hot pursuit of digitisation in the workplace risks systematising this misunderstanding of how positive outcomes appear.
The new year is normally a time to reflect upon useful takeaways from the year prior. In 2025, there was no dearth of breathless headlines and hype about AI replacing jobs and people in the very near future. As it once again did not come to pass, perhaps we can move in a different direction this year. In 2026, let’s instead focus on what we bring to our work as humans, not potential data points.
References:
[1] Fernandez Macias, E., Gonzalez Vazquez, I., Torrejon Perez, S. and Nurski, L., Work in the Digital Era: How Technology is Transforming Work and Occupations, Publications Office of the European Union, Luxembourg, 2025, https://data.europa.eu/doi/10.2760/0956105, JRC141451.
[2] Scholze, A., & Hecker, A. (2024). The job demands-resources model as a theoretical lens for the bright and dark side of digitization. Computers in Human Behavior, 155, 108177. https://doi.org/10.1016/j.chb.2024.108177
[3] Jan Müller, Heejung Chung, (2025). From the Ideal Worker to the Inclusive Worker: Measuring Norm Shifts Within Occupational Contexts > https://doi.org/10.1111/gwao.70038 ; page 261.
[4] D’Ignazio, Catherine, and Lauren Klein, 'The False Binary of Reason and Emotion in Data Visualisation', in Jude Browne, and others (eds), Feminist AI: Critical Perspectives on Algorithms, Data, and Intelligent Machines (Oxford, 2023; online edn, Oxford Academic, 23 Nov. 2023), https://doi.org/10.1093/oso/9780192889898.003.0012.
[5] Acker, J. (2006). Inequality Regimes: Gender, Class, and Race in Organizations: Gender, Class, and Race in Organizations. Gender & Society, 20(4), 441-464. https://doi.org/10.1177/0891243206289499
[6] Hughes, K. D., Konnikov, A., Denier, N., & Hu, Y. (2026). Problematizing the role of artificial intelligence in hiring and organizational inequalities: A multidisciplinary review. Human Relations, 0(0). https://doi.org/10.1177/00187267251403902