{"id":105348,"date":"2025-08-16T10:30:00","date_gmt":"2025-08-16T00:30:00","guid":{"rendered":"https:\/\/en.econostrum.info\/au\/?p=105348"},"modified":"2025-08-16T08:35:09","modified_gmt":"2025-08-15T22:35:09","slug":"automation-wont-take-jobs-says-report","status":"publish","type":"post","link":"https:\/\/en.econostrum.info\/au\/automation-wont-take-jobs-says-report\/","title":{"rendered":"Automation Won\u2019t Take These Jobs: Cleaners, Builders and Hospitality Workers Remain Safe, Says Australian Report"},"content":{"rendered":"\n

Artificial intelligence is expected to transform nearly every corner of the workforce, but not all occupations are equally exposed. According to a new report by Jobs and Skills Australia<\/em> (JSA<\/a>), jobs involving physical labour, such as those in cleaning<\/strong>, construction<\/strong>, and hospitality<\/strong>, are among the least vulnerable to AI automation.<\/p>\n\n\n\n

The research, described as the most comprehensive of its kind in Australia, assessed how AI technologies may automate or augment a range of job tasks. Its findings suggest that while the workforce will see significant change, fears of mass job displacement are exaggerated.<\/p>\n\n\n\n

Clerical and Analytical Roles Most Exposed to Automation<\/strong><\/h2>\n\n\n\n

The JSA report ranked occupations based on how susceptible their core tasks are to automation<\/strong> and AI augmentation<\/strong>. Jobs heavily reliant on digital or repetitive information processing showed the highest potential for disruption. At the top of the list were roles such as data entry<\/strong> (0.72 automation score), documentation and record keeping<\/strong> (0.65), and financial services<\/strong> (0.57).<\/p>\n\n\n\n

These functions are typically characterised by structured, rule-based processes\u2014ideal for automation through generative AI<\/strong> and machine learning systems. According to the report, \u201cmany clerical tasks \u2013 that were not affected by previous waves of automation \u2013 could now be undertaken in large part by Gen AI\u201d.<\/p>\n\n\n\n

Roles at risk include book-keepers<\/strong>, receptionists<\/strong>, marketing professionals<\/strong>, and software developers<\/strong>, all of whom face declining employment <\/a>projections by 2050. The report also highlighted anecdotal evidence of job losses already occurring. A talent agency reported an 80% drop in demand for voice-over artists<\/strong> due to AI-generated narration.<\/p>\n\n\n\n

Manual and People-Facing Jobs Remain Stable<\/strong><\/h2>\n\n\n\n

In contrast, occupations involving physical work or close human interaction are expected to remain relatively stable. The JSA found the lowest automation risk scores in sectors such as cleaning and sanitation<\/strong> (0.15), construction and fabrication<\/strong> (0.18), and manual labour<\/strong> (0.19). These roles are typically harder to automate due to their reliance on motor skills<\/strong>, spatial awareness<\/strong>, and context-sensitive decision-making<\/strong>.<\/p>\n\n\n\n

Employment in areas like hospitality<\/strong>, public safety<\/strong>, and healthcare support<\/strong> is even projected to rise by mid-century, based on modelling that compares future job growth in scenarios with and without AI adoption.<\/p>\n\n\n\n

\n

JSA Commissioner Barney Glover said, \u201cThe overarching message is that almost all occupations will be augmented by AI. It doesn\u2019t make a difference which sector you are in, or at what skill level: you will be influenced by AI.\u201d<\/p>\n<\/blockquote>\n","protected":false},"excerpt":{"rendered":"

A sweeping Australian study challenges fears of widespread job losses from automation. While office roles may face sharp disruption, not all professions are equally exposed. Jobs in cleaning, construction and hospitality appear to stand firm against the AI tide.<\/p>\n","protected":false},"author":10,"featured_media":105350,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[44],"tags":[],"class_list":["post-105348","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news","generate-columns","tablet-grid-50","mobile-grid-100","grid-parent","grid-33","no-featured-image-padding"],"_links":{"self":[{"href":"https:\/\/en.econostrum.info\/au\/wp-json\/wp\/v2\/posts\/105348","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/en.econostrum.info\/au\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/en.econostrum.info\/au\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/en.econostrum.info\/au\/wp-json\/wp\/v2\/users\/10"}],"replies":[{"embeddable":true,"href":"https:\/\/en.econostrum.info\/au\/wp-json\/wp\/v2\/comments?post=105348"}],"version-history":[{"count":2,"href":"https:\/\/en.econostrum.info\/au\/wp-json\/wp\/v2\/posts\/105348\/revisions"}],"predecessor-version":[{"id":105351,"href":"https:\/\/en.econostrum.info\/au\/wp-json\/wp\/v2\/posts\/105348\/revisions\/105351"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/en.econostrum.info\/au\/wp-json\/wp\/v2\/media\/105350"}],"wp:attachment":[{"href":"https:\/\/en.econostrum.info\/au\/wp-json\/wp\/v2\/media?parent=105348"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/en.econostrum.info\/au\/wp-json\/wp\/v2\/categories?post=105348"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/en.econostrum.info\/au\/wp-json\/wp\/v2\/tags?post=105348"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}