Part 1:The fourth industrial revolution and the future of work Artificial intelligence and automatisation Impact of automation on employment rates The impact of automation on the labour force in terms of number of jobs and employment rates is open to debate. Some experts say an overall net gain in jobs creation is possible in scenarios of economic growth and enlightened government policies (McKinsey 2017). Others forecast a substantial net loss in jobs (ILO, 2018). Regional differences in levels of employment also depend on barriers to migration, economic growth and corporate investments in automation, with the possibility of automation increasing in the industrialised economies, leading to the closure of factories in the global south as industries “reshore” to the north (ILO, 2018). Table 1: Estimates of technological unemployment
Impact of automation on the types of jobsMost experts warn of massive changes both in the types of jobs and in their quality. With the advent of AI and automation of more complex tasks, many white collar and service jobs will disappear (e.g. legal clerks, radiologists, fastfood workers), others will be transformed as they are enhanced with AI (e.g. doctors, lawyers). Jobs that are least likely to disappear are those that require creativity, imagination, social skills or high level IT/engineering training (McKinsey, 2017). In high and middle income countries, the decline in manufacturing employment in response to automation are outweighed by service sector job growth (ILO, 2018). Finally, many expect new jobs that are unheard of today to emerge with new technologies and lifestyles (McKinsey, 2017). “By one popular estimate, 65% of children entering primary school today will ultimately end up working in completely new job types that don’t yet exist. (WEF, 2016)
Pace of changeModels and scenarios make assumptions on the pace of innovation and social change. A quick, accelerating pace is more disruptive as organisations and individuals have less time to adapt, more firms go bankrupt and more people are laid off. Older people find it difficult to find jobs that require their outdated skill sets and worldviews; the generational divide widens, as does the divide between rich countries that innovate, and poor countries that depend on cheap labour. A slower pace in the adoption of new technologies allows schools and vocational training to either keep up – or radically transform with new technologies. A slower pace also allows individuals to strategize their careers by engaging in lifelong learning and acquire new skills and experience on the job – before they are laid off. The best employees realise they can be self employed, start their own business or join the gig economy, and become their own “employer of choice”. Experts expect investments in automation and AI to be quickest in rich countries where labour is more costly and, therefore, the return on investing in automation is higher. The pace of change is thus likely to be quicker in high and middle income countries, leaving behind the poorest countries, the failed states and sites of protracted crises. For the shorter term, there is a general agreement that the technological drivers of employment disruptions are here and the organisations need to transform and adapt NOW.
MobilityThe future of work is all about mobility. Mobility within the organisation, where employees can learn new skills at work, feel increasingly more competent and valued, and are less averse to change as they continually learn to adapt to new ways of working (e.g. with AI) and new responsibilities. Networked organisations such as the RC movement can provide relatively seamless mobility across teams, geographies, and professions. The future of work is also about mobility within and beyond a specific sector or industry, because the same soft skills and critical thinking are needed in all sectors. Finally lifelong learning and a highly volatile job market will encourage mobility between education and employment, and simultaneous learning and employment – the daily gymnastics of learning on the job lifelong learning and agile organisations. Employees need to develop “cross-professional” skills in order to remain competitive on the labor market. Employers who invest in talent management and encourage employee mobility not just in a diversity of positions but across professions will be the employers of choice. Agile organisations with agile employees who master a diversity of skills, experience and perspectives are more likely to adapt if their teams are mobilised by a common vision and values they hold dear, motivated by a mission they make theirs, and inspired by transformational leaders. In the absence of these essential drivers the best employees are likely to jump ship – and join an organisation that is more likely to meet their aspirations of continual learning and psychological fulfillment. Hence the need to develop and demonstrate soft “people” skills like empathy, listening, etc as well as creativity and critical thinking, the very skills that can complement AI. Transformational leaders and managers create the conditions for their staff to use these skills and fulfill their fundamental psychological needs of autonomy, mastery and relatedness (Kovjanic et al, 2012, Deci and Ryan, 2007). They create and nurture an organisational culture of creativity, innovation, risk tolerance, and life-long learning. “As digital transforms the business landscape, the successful organizations of the future will likely be those that can move faster, adapt more quickly, learn more rapidly, and embrace dynamic career demands”(Deloitte 2017, italics ours). Bureaucratic legacy organisations that enforce a culture of command and control are the least likely to adapt and survive. The Red Cross is a legacy organisation.
Part 2 IFRC and the future of workThe future of work has two type of consequences for the Red Cross and Red Crescent. First, job displacement and unemployment, erosion of social protection and the increasing vulnerabilities of white collar workers and the middle aged members of the workforce will require new types of social programs and services. Second, IFRC as an organisation needs to adapt to the new economy, and this will require a radical transformation from the way we currently work – inherited in large part from the defunct industrial era. This note addresses this second aspect.
How are National Societies adapting?National Societies from high and middle income countries are more likely to access new technologies built on AI, blockchain, automation, etc. They will be able to implement these on their domestic programs and in international operations. Those that do not adopt these new ways of working will become irrelevant and be replaced by new actors. Among the many NS from high and middle income countries we can expect at least a dozen to adapt, transform and lead the way. Low income countries: In an optimistic scenario, AI, drones, satellite imagerie, big data, blockchain, etc. allows NS to be effective in saving lives and peacebuilding. NS transform into highly effective relief and development organisation with the support of the NS that master the new technology. New services become feasible, like micro-insurance using blockchain and smart contracts. NS with tech capacity will need to be innovative, risk tolerant to co-design these new services with smaller NS, providing training opportunities for HNS staff and volunteers without fearing they will be “poached” by other organisations. What will volunteering look like? In low income countries volunteering will be a means to have access to technology, training, experience and opportunities. Volunteers will be attracted to the RC because they offer relevant learning opportunities, rather than per diem (even if these may be required). In high income countries, the current trend whereby people volunteer for a cause rather than an organisation is expected to continue (Hazeldine and Bailey Smith, 2015, Samochowiec et al, 2018). Traditional volunteer organisations, such as National Societies, are losing volunteers because they are using them for tedious, low profile tasks rather than empowering them with responsibilities and inspiring missions. National Societies that meet the basic psychological needs of autonomy, mastery and connectedness are more likely to grow their volunteer base (Samochowiec et al, 2018). In this respect motivating volunteers is not very different than motivating paid staff.
Red Cross Red Crescent networkWill we evolve from a structured membership organisation to a self-organising, resilient network? Or to a scalable social franchise with pooled AI resources?
How will the secretariat adapt its mission and strategy? Its human resource approaches and staff policies? What are the implications for IFRC staff, including talent management, mobility, lifelong learning? Implications for IFRC leadership?