Future of Work – Tasks-Skills Stack, Strategies and Transitions

Commenced on

1 June 2018

ongoing

PI

POON King Wang (LKYCIC, SUTD)

Team

GOH Zi An Galvyn, VINOD Radha, HOTAN Darion, LIU William Shu Yuan (LKYCIC, SUTD)

The rise of artificial intelligence, automation, and digitalization poses both potential risks and opportunities. These trends can be deeply disruptive and destabilising for individuals, families, workers, companies, leaders and society. This includes the vulnerable and/or less fortunate members of society. Concurrently, these trends can augment what these individuals, groups and institutions do daily, they can increase productivity, create better jobs, and drive economic growth, among many other beneficial factors.

 

The study aims to develop understanding of the following:

  1. How a task-based approach can help empower individuals, families, workers, companies, leaders, and society to adapt and keep ahead of the changes in the Fourth Industrial Revolution.
  2. How a task-based approach can help workers transition to new jobs, either within sector or across sectors.
  3. How a task-based approach can inform job redesign to create more engaging and energizing jobs.
  4. How a task-based approach can inform remote and hybrid work.
  5. How a task-based approach can complement pre-existing skills and occupational/organizational frameworks.
  6. How a task-based approach can help give individuals, workers and families a voice and choice in their careers and decisions throughout their lifespan.
  7. How a task-based approach can help educators and learners.

The study employs the following methods:

  1. Jobs are broken down into tasks using a combination of social science and data science methods and expertise
  2. These tasks are analyzed using the LKYCIC Tasks-Skills Stack, which is an AI-powered task database.
  3. Job transitions can be generated to help workers transition to new jobs both within and across sectors and via multi-stage transitions.
  4. Emerging and in-demand tasks can be identified via a synthesis of social science and data science algorithms.

Project email: digitalsocieties@sutd.edu.sg