{"id":127132,"date":"2025-10-17T02:53:14","date_gmt":"2025-10-17T02:53:14","guid":{"rendered":"https:\/\/www.europesays.com\/ie\/127132\/"},"modified":"2025-10-17T02:53:14","modified_gmt":"2025-10-17T02:53:14","slug":"this-it-company-just-laid-off-11000-workers-who-couldnt-be-retrained-on-ai-here-are-the-jobs-most-at-risk","status":"publish","type":"post","link":"https:\/\/www.europesays.com\/ie\/127132\/","title":{"rendered":"This IT company just laid off 11,000 workers who couldn\u2019t be retrained on AI \u2014 here are the jobs most at risk"},"content":{"rendered":"\n

Accenture just delivered a jolt to white-collar workers everywhere. The global IT and consulting giant announced more than 11,000 job cuts tied to an $865 million \u201creinvention\u201d effort. (1)<\/p>\n

But while large-scale restructuring may not be uncommon, here is what sets this mass layoff apart: Executives said a share of employees would be \u201cexited\u201d because they simply could not be retrained fast enough for AI-heavy roles. On a recent earnings call, CEO Julie Sweet said, “The workforce needs new skills to use AI, and new talent strategies and related competencies must be developed.\u201d<\/p>\n

Positions connected to legacy projects and repeatable tasks were the first to go, while hiring and investment are tilting toward data, automation and AI deployment. But will automating knowledge work be a successful strategic pivot?<\/p>\n

Accenture believes it will boost the bottom line in the near term, but how much service work can be automated is raising questions elsewhere.<\/p>\n

Initial speculation suggests the safest jobs going forward will blend human judgment with AI tools, while that the riskiest seats belong to jobs that software can absorb.<\/p>\n

The discourse around AI since OpenAI launched ChatGPT 3 in November 2022 has been nothing short of polarizing. It seems as if AI is either going to usher in an era of limitless plenty<\/a> (but not without taking jobs with it<\/a>), or as some people have argued, AI is just another bit of boring, overhyped tech. (2)<\/p>\n

One of the most prominent examples of AI doomerism that has not come to pass is a prediction made by Geoffrey Hinton \u2014 widely considered the Godfather of AI.<\/p>\n

In 2016, Hinton suggested radiologists would soon be obsolete. Almost a decade later, demand for radiologists remains strong even as AI tools proliferate in their field. (3) Though Hinton\u2019s speculation might yearn for a hospital full of autonomous robots, the pedestrian reality is that clinicians have, in fact, folded FDA-cleared algorithms into daily workflows as tools even as the human element can\u2019t be as easily replaced. But there are other realms of health care, such as insurance and medicare, where AI is being woven in<\/a> with very real impacts on Americans.<\/p>\n

Story Continues <\/p>\n

Read more: Robert Kiyosaki warns of a ‘Greater Depression’ coming to the US \u2014 with millions of Americans going poor. But he says these 2 ‘easy-money’ assets will bring in \u2018great wealth\u2019<\/a>. How to get in now<\/strong><\/p>\n

Amazon\u2019s Andy Jassy has also said generative AI will reduce parts of the company\u2019s corporate workforce over the next few years and British Telecom\u2019s CEO Allison Kirkby has mapped tens of thousands of cuts for her organization by 2030, with a portion tied directly to AI and automation. (4)<\/p>\n

However, one nuance often lost in headlines is that adoption is uneven and slower than the hype implies. Companies have invested heavily in AI over the past three years, but reworking day-to-day processes<\/a> to integrate the new technology takes time. What’s more, the concrete payoff has remained elusive.<\/p>\n

In fact, a widely cited recent study from MIT showed that 95% of organizations spending heavily in generative AI products could not report measurable return on investment. (5)<\/p>\n

Part of the problem is that AI is not like other enterprise software inventions that had a clear impact on productivity. For example, think of how much time bookkeepers saved when they went from entering numbers manually into paper ledgers versus automating data capture into a digital spreadsheet.<\/p>\n

Part of the problem is the slow-moving nature of most organizations (what one paper calls \u201centerprise time\u201d). (6) But the other part is that for all the hype, AI still can\u2019t do jobs beyond its training data well.<\/p>\n

Jobs that involve repeatable digital tasks are most likely to face pressure from employers looking to reduce headcount through automation.<\/p>\n

These are most often \u201cback office\u201d jobs like data entry and entry level coding jobs. Some kinds of content production are also likely to be automated with AI, though recent wins protecting copyrighted material may raise the demand for talented graphic designers, rather than lessen it. (7)<\/p>\n

Jobs that require in-person work, variable physical tasks or high-stakes judgments are the least likely to be affected by AI implementation. This could be partly why more young people are deciding to skip the dorms and degrees, opting instead for hands-on work<\/a>.<\/p>\n

Jobs in health care and skilled trades<\/a>, for example, are also unlikely to be replaced by AI \u201cagents.\u201d For the same reason, jobs that depend on context and coordination such as client-facing roles and compliance won\u2019t be offloaded to an AI until AIs prove their consistent performance in this capacity.<\/p>\n

Even if the revolution has been televised, it has yet to be computerised. And \u2014 for now at least \u2014 that means it\u2019s still just sci-fi.<\/p>\n