I argue for a collective answer to the instability and uncertainty of work in the IPPR’s book Technology, globalisation and the future of work in Europe: essays on employment in a digitised economy.
When thinking of the future of work, automation and robotisation are often invoked as probable scenarios which will lead to the replacement of humans and the progressive reduction of the numbers of people in employment. Robotisation in particular is presented as a technology of replacement leading to inactivity and exclusion. However, if we consider the transformations that we are already witnessing in the labour market, what is on the rise is precarity, fragmentation, instability and self employment. Many years of automation and digitisation have not excluded human beings form the work process but reconfigured their activities and their life cycles.
Periods of employment followed by phases of unemployment, followed by partial employment, are increasingly common patterns, as are multiple jobs, freelancing, temporary and part time contracts, and self employment. There is a general agreement that we can foresee an even greater instability in the job market which in turn will generate instability in individuals’ income, professional development and mobility. Indicatorspublished for the UK by the Office for National Statistics, such as the amount of time individuals are unemployed (two thirds of job seekers are unemployed for less than 24 weeks), the proportion of people which are underemployed (9.2%) and the decrease of voluntary job turnover, all suggest that we are already in a phase in which employees are living with a considerable degree of uncertainty. Linearity of work experiences are more and more the exception and most people have a myriad of employers, jobs, roles and skills on their CVs.
The question therefore, is not how to face potential replacement by increasingly integrated information systems but rather one of handling instability and uncertainty. Interestingly until recently, one of the shortcomings of automated systems and robots was their stability. Automation started by replacing highly routinised tasks and still works best in stable and predictable environments. Even in much more advanced systems where machine learning can support the adaptation to more unspecified and unpredictable contexts, the interaction with humans still speeds the process of learning how to disambiguate contextual information. This again may change in the future as machine learning becomes more sophisticated and interacts with growing amounts of data. Similarly, all the projections regarding the sectors where we can reliably expect some job growth, point to creative activities, soft relational tasks, managerial and coordination roles. All jobs typically involved in creating new meanings, interpreting contextual conditions and generating alternative, potentially unpredictable, solutions. Creativity and innovation, as we know, relies on the capacity to frame problems differently and integrate a variety of occasionally conflicting points of view.
Managing instability and uncertainty are therefore, not only a going to be an existential condition, but a professional asset. If we look at the history of labour and developing economies, stability, linearity and incremental skill development were not a defining characteristic of individuals’s life trajectories for most of human history. Continuity, regulation and predictability have been a short historical exception. In most developing economies the accumulation of jobs, the periods of low activity, the uncertainty of markets and ecosystems are still the norm. What is however novel to the current analysis of work and employment, is the highly individualistic perspectives that is taken to envisage future solutions. The alternative to the loss of jobs in certain sectors, be they low end services, manufacturing or information processing roles, is the development of new individual skills in emotional, creative or scientific professions. Following the strategy initiated by the industrial revolution, increasing individuals’ skills through education and training, is again put forward as the solution to keep ahead of the logic of replacement by automation.
Flexibility and uncertainty is in other words, countered by embracing cognitive skills that enhance the capabilities of navigating and mastering unpredictable complex situations. Continuous learning, mobility, flexibility, the ability to navigate large amounts of data, are all skills that we are expecting workers to embrace in order to counter the threat of irrelevance. I would like to challenge the assumption that this is a task that can be achieved individually. In the past social organisations, be they families, companies, tribes, towns or guilds, were structured in a way to handle individual contingencies but also in a way to create networks of expertise. If we look at how cognition is distributed among co-workers in most organisations, we cannot fail but observe that single individuals rarely can generate innovative solutions that truly push the boundaries of understanding or creativity. Not only do organisations have networks of people with different skills, they also have processes defined over time, tools developed sometimes over decades, data sets, procedures, roles, which embed and stabilise knowledge that can be then individually mobilised. Individual intelligence and adaptability to uncertainty has therefore always relied on collective and diachronic collaboration.
It is in this collective intelligence that lies the answer, in my opinion, to the question of flexibility and instability. No one will be able to handle either their existential instability or their professional uncertainty alone. The growing complexity of the issues that will have to be addressed professionally, be they in the public services, financial or logistic domains, can only be addressed through innovative forms of collective intelligence. The role of networked society and networked publics has been amply discussed, but I would like to challenge the idea that networks emerge spontaneously and that individuals can by default rely of personal social networks and communities.
Over the last 30 years, in most workplaces, we saw the transition from from task based work to project work. Most jobs at whatever level, involve a higher degree of autonomy than a few years ago, involving a greater control on time, resources (often informational resources) and means, stronger coordination and measures of progress and outcome. This project approach to work has been reflected in the social relations that are constructed on the job. People come together around projects and drift apart after the completion of a project. Groups coalesce around projects, collaborate and eventually splinter. The project as coordinating element is visible in the composition of individuals’ social networks. People’s personal networks are often represented as a set of clusters that coalesce around a place (university), an activity (football), a company, a family
. People’s professional networks are organised around projects completed together. The transition from life long employment to project is also redefining identities and personal professional trajectories and are allowing making self employment and freelancing a more desirable and acceptable model. The existent project approach to work and social relations has influenced traditional discourses around the role of social capital in professional networks. The often cited solution to job instability is leveraging on social capital that can support individuals’ search for employment, capital or contracts. Unfortunately, as much as personal connections are important for finding a job, the scale of personal networks will simply be insufficient to address the complexities and uncertainties we can expect.
The next phase of professional development will demand a change of scale in the size and diversity of the networks that individuals will belong to and leverage. Innovation will, as always, emerge in interaction with tools and systems, the complexity of which however, will require large networks of people to be used and understood. If we accept that instability and uncertainty along with complexity are not only going to be the object of future professional lives but that they are going to be addressed collectively (and that all work will be networked and distributed in ways that we have not yet experienced), we need to think about how the networks will come together. The search for new forms of networked communities, be they for social action, political activity, expertise or co-creation, is an attempt to anticipate what are the mechanisms that will allow large extremely distributed, diverse groups of people to join into collective efforts of problem solving, adaptation and decision making. Interestingly, the extensive experience most people in the world are having with social networking services such as Facebook, Twitter, Weibo or QQ, has laid the ground for forms of networked collaboration that are far more complex than we have seen until now. Increasingly people will be capable of collaborating with diverse, distant and occasional contacts on sparse, incomplete and messy information. The real challenge will be around the governance models of these organisations, the traditional forms of control as we are witnessing are in fact leading to a division of labour where most people produce data and few people aided by sophisticated systems, exploit it. This approach is not only unfair, but also dramatically underuses the extraordinary human potential for understanding and handling uncertainty and generating innovative solutions. Many people in interaction with sophisticated systems can engender far greater knowledge.