Fissured workplace – Data Big and Small

The Millennium Institute for Authority and Social Regulation (ASOR), recently created in Chile, aims to conduct cutting-edge interdisciplinary research on the decline and reconfiguration of authority relationships in contemporary society and their impacts on social regulation. Authority, claim the founders of the Institute, is a pervasive social mechanism that is in place whenever there are power asymmetries. How people manage authority – in the family, school, workplace etc. – affects social cohesion and the extent to which (un)healthy relations of domination may arise.

The rise of algorithmic management, especially AI-supported, is widely thought to affect labour relationships, endowing our bosses with new tools or even – as in the exemplary book by Aloisi and De Stefano) that algorithms actually are our new bosses. At the same time, the rise of platform labour – also closely linked to the AI boom – has promoted the model of independent workers who have no boss. Authority in the workplace changes gets more fragmented, disrupting traditional relational models, not only vertically (the hierarchical chain of command), but also horizontally (team composition, collegial work). What are the effects?

My presentation at the ASOR Institute on 2 July 2026. Prof. Antonio Stecher chaired the session.

I was invited to give a talk at the ASOR Institute in Santiago in early July, and had the pleasure of sharing some results of my research in order to deepen dialogue on these important topics. I discussed how algorithmic management comes with a promise of higher productivity, but also increases surveillance in companies, while the apparent autonomy of self-employed workers on platforms—from Rappi delivery drivers to Workana designers—proves to be illusory. My presentation, largely though not exclusively based on this open-access book chapter, analyzed the consequences of these transformations on job quality at two key stages of the AI life cycle: its production through human labor (so-called “data work”) and its deployment in workplaces across various sectors, from logistics to healthcare. Taken together, they reveal how AI accelerates preexisting trends in the global economy: the outsourcing of business functions while maintaining control over production – a phenomenon studied by D. Weil as the “fissured workplace” – and a “regime of dispersion”, first theorized by C. Datchary, in which workers manage multiple disconnected tasks. To protect job quality, corporate strategies and labor regulations must address the cognitive overload associated with fissuring, develop worker-centered organizational models, and implement safeguards for all users.

Contemporary debates on AI and labour often focus on quantitative impacts, such as potential job losses. Yet, the qualitative question of how AI reshapes the experience of work is equally critical. A recent open-access book chapter that I co-authored with Antonio A. Casilli examines these transformations, highlighting their already tangible, if not yet final, effects on job quality.

New book: *Job Quality in a Turbulent Era*, edited by A. Piasna & J. Leschke, 2026, Edward Elgar (open access)

The analysis starts with AI deployment, that is, the introduction of AI solutions into the workplace – for example, use of automated transcription tools. It is typically framed as a productivity enhancer that can improve job quality by making work more engaging – and in some cases, even safer. However, critics argue that firms often use AI to intensify labour through tighter managerial control and surveillance. In this scenario, AI may degrade job quality by restricting autonomy, reinforcing algorithmic management, and limiting workers’ ability to negotiate their roles or pace.

But before AI tools reach the workplace, they must be designed, built, and marketed. Today’s AI production relies heavily on machine learning, which requires vast datasets. This process involves extensive “data work,” performed by myriad lower-level workers in three key functions: preparation (data generation and annotation), verification (quality control of AI outputs), and impersonation (manually performing tasks meant to be automated). While this process increases labour demand the quality of data work is often poor, with low wages, job insecurity, and misrecognition of skills. The root of these issues lies in labour platformization.

The Preparation – Verification – Impersonation functions of data work in AI production. Source: Tubaro et al. 2020.

To see this, let’s first take a step back and consider that most data work is procured through outsourcing (shifting lower-value functions outside the firm) and offshoring (relocating work to lower-wage countries). These practices exclude externalised workers from the resources of lead firms, creating a “planetary market” where AI companies in the US and Europe source labour from countries like Venezuela or Madagascar. Digital platforms amplify these trends, enabling on-demand outsourcing to precarious micro-providers globally. Platformization thus exacerbates the negative effects of outsourcing and offshoring, degrading job quality.

Platformisation is best understood at the crossroads of two broader socioeconomic tendencies. First, the “fissured workplace“, where companies market goods or services without employing all those who contribute to their design, assembling or delivery. This model heightens insecurity, as metrics and surveillance systems erode autonomy and increase stress. Offshore platform workers, in particular, face isolation, losing traditional workplace camaraderie and support. Second, “dispersion“, the constant need to manage interruptions, multitask, and prioritise competing demands. AI-driven tools, such as real-time scheduling algorithms and productivity trackers, intensify dispersion, blurring boundaries between professional and personal life and increasing mental strain.

Overall, then, the effects of AI on job quality are mixed. While AI deployment can enhance productivity and safety, it often intensifies labour and undermines autonomy. Meanwhile, AI production creates jobs, but these are frequently precarious and low-quality. Understanding these dynamics is essential for shaping equitable futures that leverage the potential of contemporary technologies while safeguarding labour rights and well-being.

A detailed digital collage inspired by the aesthetic of medieval manuscript illustrations, depicts a vibrant scene of construction workers building a structure. The traditional elements, such as figures in traditional Persian gowns, wooden ladders, ropes, and woven sacks, are interwoven with modern technological motifs like circuit boards, QR codes, and cloud icons. Bright, glowing, golden networks of interconnected nodes and symbols of digital technology overlay the scene, blending the past and present. On the left, Arabic calligraphy is imprinted onto the paper. The composition uses contrasting textures and colours to juxtapose and interweave the themes and eras. — Shady Sharify / Who is AI Made Of / https://betterimagesofai.org / https://creativecommons.org/licenses/by/4.0/

This is not a story of technological determinism. AI alone does not destroy jobs or degrade their quality; rather, its impacts stem from the broader economic forces that shape its presence and role in today’s economies. The above artwork captures this idea by foregrounding humanity’s collective endeavour in building artificial intelligence, drawing inspiration from Persian miniature painting. In sum, the future of work in the age of AI will hinge on political choices about production organisation and the distribution of its benefits and costs.

The book chapter can be found as: Casilli, A.A. & Tubaro, P. 2026. “What is AI Doing to Job Quality? Platformization, Fissured Workplaces and Dispersion”. Chapter 3 in A. Piasna & J. Leschke (eds.) Job Quality in a Turbulent Era. Cheltenham, UK: Edward Elgar Publishing, pp. 45-60, https://doi.org/10.4337/9781035343485.00009

A video of the launching event of the book – including my presentation of the chapter – is available here.