AI and Work: Equal to the Task

Can the future of work be better than the present? It can, but only if we are equal to the task. It is fast becoming the consensus that we need to study the future of work at the level of tasks. This is the converging conclusion of a rapidly increasing number of academics, think tanks, consultancies, companies, and governments worldwide. To understand how work is changing in the age of AI, tasks – and work activities (which are categories of tasks) – provide the right resolution. 

 

Why tasks?

Tasks are the right resolution because jobs are disrupted task-by-task and not job-by-job. This is the case for AI because AI remains narrow. As Stanford University’s Artificial Intelligence and Life in 2030 points out, “AI systems are specialised to accomplish particular tasks, and each application requires years of focused research and a careful, unique construction”. It is also the case for globalisation. When companies outsourced and offshored work, they broke up their value chains into tasks. They then parcelled out these tasks worldwide. Parts of the jobs tied to these parcels of tasks followed. 

As MIT economist and professor David Autor points out, “a growing body of literature argues that the shifting allocation of tasks between capital and labour – and between domestic and foreign labour – has played a key role in reshaping the structure of labour demand in industrialised countries in recent decades.” 

A simple word count exercise illustrates this growing role of tasks. When the World Economic Forum (WEF) published its Future of Jobs Report 2016, the word counts for “skill” and “task” were 719 and 9 respectively. Two years later, in WEF’s Future of Jobs Report 2018, they were 614 and 153 respectively. In just two years, “skill” saw a -15% change. “Task” had a 1600% change. The centre of gravity is swiftly shifting. 

 

Making tasks work

In the Lee Kuan Yew Centre for Innovative Cities (LKYCIC) at the Singapore University of Technology and Design, we have been studying the future of work since 2014. We have been developing our own task strategies and databases since then. We did so because we saw the power of tasks to empower everyone who wanted to create a better tomorrow. The diagram below and the accompanying explanations illustrate how. These were first published in our book Living Digital 2040: Future of Work, Education, and Healthcare, and refined through our work with industry partners. 

Take the job of a Cyber or Information Security Analyst. Their job can be broken down into the tasks that they do. For example, they have to discuss user issues, assess risks and execute tests, and document policies and procedures. Recall that AI disrupts jobs task-by-task and not job-by-job. We can thus look at which tasks are being disrupted (i.e. the white Xs on the left of the diagram). We can also look at when they are being disrupted. Combining both, we can assess the speed and scale of disruption on this job. Our task analysis has thus determined the risk profile of a job (i.e. the red rectangle in the diagram). 

Each task is analyzed to determine the risk of the job profile.
Job description: Information Security Analyst

 

We can do more: our task analysis can also help workers see what other jobs they can transition to. The Cyber Security Analyst’s job often shares similar tasks with other jobs. For example, if the former has to discuss user issues, so do the Web Developer, Database Administrator, and Data Manager. The expertise and experience are more likely to be transferable. Hence, by matching jobs by shared similar tasks, we can identify new jobs that a worker can more easily transition to from their current job (i.e. the blue arrow in the diagram). Our task analysis has thus created – even expanded – options for workers. 

Furthermore, we can scale up this analysis to cover entire companies, industries, and sectors. When we do so, we develop two additional insights: the first is a risk profile of all the occupations in that company, industry or sector (i.e. the white Xs on the right of the diagram); the second is a series of transition options between all these different occupations (i.e. the orange arrow). 

 

Empowering everyone

These task analysis techniques form the nucleus of the LKYCIC’s task strategies and databases. We are using them to help workers, human resource practitioners, union leaders, technologists, company strategists, and policymakers. 

Their power lies in how they empower each of us: 

  • Workers can use them to expand job options at every step of their careers. 
  • Human resource practitioners can use them to prioritise the jobs and workers most at risk and recombine tasks to redesign jobs that are more meaningful. 
  • Union leaders can use them to chart clear pathways for their workers and work with companies to transition workers along those pathways. 
  • Technologists can use them to invest in AI that augments the value of the tasks that workers do, thus making workers more resilient to disruption. 
  • Company strategists can use them to track the speed and scale of AI disruption. This forms a common view across the entire company, which can be used to align transformation efforts between employees and management. 
  • And policymakers can use them to make targeted policies according to the risk profiles of industries and sectors. These can then be mapped to national efforts in worker training, skills development, and lifelong learning. 

Recent studies predict that AI can severely disrupt and displace workers and jobs. The destructive effects could cascade across the entire economy. They could even tear societies apart, as recent political events around the world have shown. We must disrupt this disruption. It will not be easy. It might even be an uphill task. But we must. 

Task strategies and databases can help. They can empower each and every one of us who wants to create a brighter future for our workers to better live with AI. They give us clear ways to make sure AI works for workers, and not against them. They also give us a way to align our individual efforts and to work together. Just imagine if we could do all this and more. We will find that we are more than equal to the task. 

King Wang Poon – Lee Kuan Yew Centre for Innovative Cities Singapore University of Technology and Design

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The foundation gathers thought leaders, researchers, decision-makers, from Asia and Europe, to lead working groups and research projects on the positive impacts of artificial intelligence on our society.

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