In this issue, we explore the value of focusing on job tasks rather than job titles when forecasting how work may evolve. We also examine the limitations of this task-based approach and why it may offer only a partial view of the future of work.
Economist and author Daniel Susskind (2020) argues that focusing on the concept of “jobs” can be misleading, as jobs are not uniform or indivisible units. Instead, people perform a wide range of tasks and individual activities within any given role.
In our job forecasting work at the LKYCIC, we break down job description into specific tasks by translating them into Intermediate Work Activities (IWAs) — a standardized set of tasks categorized based on the O*NET database.
Figure 1 below shows an example of a job description and its corresponding translation into IWAs.
The original job description outlines the specific responsibilities and requirements of a role within an organization. These descriptions are often highly detailed and may reference specific tools or software. However, job descriptions vary widely in language, structure, and specificity. Translating them into Intermediate Work Activities (IWAs) provides a consistent and granular framework to describe what a worker actually does. This standardization enables more objective comparisons across different roles.
When we examine the IWAs, it becomes clear that many work activities are shared across seemingly different jobs. For instance, both a financial analyst and a digital marketing manager may engage in the task of analyzing business or financial data. This illustrates why focusing on individual job tasks—rather than broad job titles—can offer a more accurate and actionable perspective for job forecasting.
Viewing jobs as bundles of tasks is helpful in recognizing that jobs consist of multiple components, some of which can be automated or enhanced by AI (LeCun, 2025). However, this perspective does not give a complete picture. It risks overlooking the nuanced, harder-to-define aspects of work — the interactions, that often occur between or across tasks and are far more difficult to automate (Narayanan, 2025).
A more complete approach to job forecasting must therefore combine structured task analysis while incorporating the human, contextual, and relational aspects of work that remain uniquely difficult to automate.
References
LeCun, Y. (2025) Most jobs involve performing many tasks, only some of which can be enhanced… [LinkedIn]. Reshared 24 June. Available at: https://www.linkedin.com/posts/yann-lecun_i-find-the-story-of-ai-and-radiology-fascinating-activity-7342572373931356160-BPWD (Accessed: 27 June 2025).
Narayanan, A. (2025) I find the story of AI and radiology fascinating… [LinkedIn]. Posted 24 June. Available at: https://www.linkedin.com/posts/randomwalker_i-find-the-story-of-ai-and-radiology-fascinating-activity-7341445270099976192 dgF_ (Accessed: 27 June 2025).
Susskind, D., 2020. A world without work: Technology, automation and how we should respond. Penguin UK.