Hypotheses
Over the course of 2 one-day workshops, all 12 foresight files were presented to the members of the group, who were able to comment on them and add to them. Working from the material in the files, hypotheses on how the macro-variables might evolve were built collectively, distinguishing between:
- Trend-based hypotheses: which relate to dynamics already under way.
- Contrasting hypotheses: which take up uncertainties, controversies and the seeds of change.
- Disruptive hypotheses: which relate to disruptions.
Trend-based hypothesis: occupations that disappear and others that call for a renewal of skills in industrial occupations
Individual and societal consequences
The evolution of occupations concerns blue-collar workers but also white-collar workers. Indeed, after blue-collar workers were replaced by robots, white-collar workers are threatened by AI and by the automation, on the one hand, of advanced processes (knowledge-based activities) and, on the other, of cognitive activities (reasoning-based activities). Back-office occupations are the most affected by the rise of artificial intelligence. AI finds applications in many company activities: analysing emails or helping employees find their way around internal documentation.
New forms of employment create greater insecurity, with unpaid periods.
Using new forms of employment makes it possible to fill skills needs. Companies use self-employed workers and freelancers connected through global platforms. The development of multi-employer freelancing would rise from 10% to 20% overall in France.
This skills need accentuates the segregation of the labour market between a "low qualification and low pay" segment and a "high skills and high pay" segment. This distinction also applies among production staff (Jirjahn, Kraft, 2010).
Social tensions intensify with a social divide between qualified, agile people and a block of people who fail to adapt and who remain outside skills development schemes.
Positioning of the players
In industry, which remains an "unloved" sector, the aim is to reduce the gap between the skills available and the skills required to meet needs.
A traditional lever remains training, which is evolving and offering new teaching methods to meet the needs of industry (modularisation, e-learning, etc.). These existing offerings are improved by the use of "digital learning". These developments improve the flexibility required by companies as to the place, time and duration of training and provide the personalised support that learners expect.
Alongside these traditional forms, renewed thanks to the contributions of "digital learning", new structures offer new ways of training and educating. For example, the 42 schools, higher education self-training establishments not recognised by the State, whose aim is to train developers. MOOCs (massive open online courses) can also be mentioned.
The support offered by platforms makes them a new player in matching supply and demand. In relationship-based occupations, bots will respond in place of humans.
Various strategies are developed with a specialisation of skills on a global scale. Specialists in developing Industry 4.0 solutions (mobility, analytics, digital factory, control tower, predictive) are recruited in emerging countries.
Digital platforms provide transparency and the "matching" of skills supply and demand (e.g. Creads, Malt, Codeur, Textmaster, Digicomstory, etc.).
Consequences for production
The flexibility and intelligence of production increase, with an acceleration of transactions and decision-making (the result of automating basic processes, advanced processes and cognitive activities).
In industry, the use of contracted-in work develops with the provision of the necessary skills. In particular, development is outsourced with a significant share contracted in by engineering firms. Co-innovation becomes the norm, with employees benefiting from the engineering firm's know-how.
Contrasting hypothesis: a strengthened regional dimension with a role as skills providers as close as possible to companies' needs
Individual and societal consequences
We are witnessing a strengthening of the regional dimension, which facilitates access to skills close to the company. Strengthening the role of the region makes it possible to optimise the skills of the local labour market.
Despite a degree of insecurity, people manage to better reconcile private and professional life. They show availability to develop skills serving a local labour market to which they are attached. An income smoothed across the year makes it possible to fund periods of skills development.
Organisation around a local labour market leads to skills transfers facilitated by proximity, with intergenerational exchanges that improve the use of new technologies.
Positioning of the players
Training centres are opened in the region by companies.
By working directly with company stakeholders, training organisations have fully incorporated technological opportunities in order to meet companies' needs for flexibility and learners' expectations regarding personalisation.
The ways of acquiring skills are more open: for example "peer learning…"
Consequences for production
Productivity gains come through new processes and the automation of activities. Craft trades find new avenues of development thanks to 3D printing. The regions experience a reindustrialisation movement.
Disruptive hypothesis: a stronger presence of human-assisted AI across all occupations associated with production and the disappearance of a physical production entity
Individual and societal consequences
The difficulty of anticipating developments and their real impacts leads to a break in the logic of predictability (the foundation of the skills management approaches initiated by organisations). Any attempt to anticipate skills needs is delegated to external players offering solutions and skills. What becomes central is no longer vision but the ability to develop a new networked organisation of work.
Owing to changes in employment status, periods of paid work alternate with periods of training and periods dedicated to R&D activities. Working life is also extended because pension funding is called into question by the new forms of employment.
White-collar workers would be more vulnerable in their employment than blue-collar workers. Blue-collar workers manage to maintain the value of their expertise in production, expertise enhanced by access to new technologies. The robotisation of the service sector would not lead to new jobs (for example platforms with bots able to provide the services delivered by support functions such as HR and administrative functions)
Work becomes less arduous and makes it easier for more people to stay in or access employment (for example older employees), with widely disseminated innovations (for example exoskeletons).
Positioning of the players
The change in the expert-learner relationship, since information is accessible to all, leads to a loss of credibility for traditional training players.
In this context, traditional training players that have not themselves transformed will lose their raison d'être and will also be deprived of the resources arising from the traditional funding arrangements.
Consequences for production
The faster development of AI-related technology leads to a new stage of robotisation including the supply chain, support functions, sales and the extensive use of bots to handle relational dimensions. Production becomes personalised.
Production is completely robotised with human supervision. The company becomes virtual, reduced to a platform for managing the value chain. The challenge becomes coordinating independent players working in a network and mobilising technological innovations.
Facilitating this production by facilitating the coordination of the various players in the value chain becomes the role of certain support functions, redeployed and mobilised to support the operation of the platform. The differentiation between functions and occupations fades entirely, with self-employed workers positioning themselves across the whole value chain according to their contributions (expertise, technological innovation, producer, support, etc.).