70/100

Moderate evidence

18-study review shows robots cut routine manual work, lifted analytical tasks in rich nations

Study title: EFFECTS OF ARTIFICIAL INTELLIGENCE AND ROBOTIC TECHNOLOGIES ON THE FUTURE OF WORK: A META-ANALYSIS STUDY

Evidence type
Systematic review or meta-analysis
Sample
n = 18
Sources
1 sourcePeer-reviewed
Published
3 min read

Study title: EFFECTS OF ARTIFICIAL INTELLIGENCE AND ROBOTIC TECHNOLOGIES ON THE FUTURE OF WORK: A META-ANALYSIS STUDY

A meta-analysis by researchers at Yıldız Technical University pooled 18 empirical studies published between 2015 and 2024 on how AI and robots affect work. Pooling five studies on automation risk gave an average susceptibility rate of 21.3%, well below the figures behind many public warnings about robots taking jobs.

Read the study abstract

This meta-analysis examined the impacts of artificial intelligence and robotic technologies on the labor market through 18 empirical studies. Findings were evaluated under three main categories: (1) the average automation susceptibility rate was determined to be 21.3%, with methodological approach and publication year having significant effects on results; (2) while technology had a statistically insignificant slight negative effect on total employment, developed economies showed a decrease in routine manual tas…

EFFECTS OF ARTIFICIAL INTELLIGENCE AND ROBOTIC TECHNOLOGIES ON THE FUTURE OF WORK: A META-ANALYSIS STUDY
"Construction workers at a site on Södra Hamngatan, Gothenburg" by W.carter, CC BY-SA 4.0.

The problem

A meta-analysis of 18 studies found that robot use in developed economies was linked to a significant decrease in employment in routine manual tasks (g = -0.441, p < 0.001).

In brief

The effect on total employment was slightly negative but not statistically significant (Hedges' g = -0.155, 95% CI -0.323 to 0.013, p = 0.070, seven studies). The shifts showed up by task type: in developed economies, robot use cut employment in routine manual tasks and raised it in non-routine analytical tasks, while developing economies showed no significant effect for any task type.

The authors conclude that technology reshapes jobs more than it removes them and call for policies tailored to each country and sector. The pooled studies differ widely in method and the results varied a lot between them, so the figures describe an average across very different studies.

Main points

  1. Five studies on automation risk gave a pooled susceptibility rate of 21.3% (log odds ratio -1.312, 95% CI -2.042 to -0.581), with very high heterogeneity (I² = 99.4%).
  2. Studies using a task-based approach reported significantly lower automation susceptibility than occupation-based studies (β = -1.364, p < 0.001); post-2017 studies also gave lower estimates (β = -0.826, p = 0.014).
  3. Across seven employment studies, the overall effect was slightly negative but not significant (g = -0.155, p = 0.070); studies published from 2020 onward found more negative effects (β = -0.279, p = 0.027).
  4. In developed economies, robot use cut employment in routine manual tasks (g = -0.441) and raised it in non-routine analytical tasks (g = 0.501); developing economies showed no significant effect for any task type.
  5. Four studies on skills gave a pooled Fisher's z of 0.382 (r = 0.364, p = 0.016), a positive and significant link between these technologies and skill requirements.
  6. IBM and IFR reports show STEM skills falling from 1st place in 2016 to 12th in 2023, while communication, teamwork and time management rose from 10th to 1st.
  7. IBM (2023) is cited as estimating that 40% of the workforce will need to reskill within three years because of AI and automation.

What the review found when it combined the studies

These are the review’s own pooled results, copied from its abstract. Truvace did not recalculate them.

  • Skill requirement changes with AI and robotic technologies

    Fisher's z 0.382 (95% CI 0.072 to 0.691)

    The review’s own words
    The combined effect size (Fisher's z') of four studies examining skill requirements and changes using a random effects model was calculated as 0.382 (95% CI: [0.072, 0.691], p = 0.016).
  • Employment effect of robots and artificial intelligence on total employment

    Hedges' g -0.155 (95% CI -0.323 to 0.013)

    The review’s own words
    The combined effect size (Hedges' g) of seven studies examining the employment effect (Graetz & Michaels, 2015; Bachmann et al., 2024; Acemoğlu et al., 2022; Klenert et al., 2023; Bordot, 2022; Fu et al., 2020; de Vries et al., 2020) using a random effects model was calculated as -0.155 (95% CI: [-0.323, 0.013], p = 0.070).
  • Automation susceptibility of jobs (log odds ratio, random effects)

    Log odds ratio -1.312 (95% CI -2.042 to -0.581)

    The review’s own words
    The combined results of five studies reporting automation susceptibility rates (Frey & Osborne, 2017; Arntz et al., 2016; Nedelkoska & Quintini, 2018; Hawksworth et al., 2018; Manyika et al., 2017) using a random effects model show that the mean LogOR value is -1.312 (95% CI: [-2.042, -0.581], p < 0.001).

Code checks every number above against the review’s text before it is shown. The short labels are an AI model’s reading of that text, so the review’s own sentence sits under each one. A pooled figure is only as strong as the studies behind it. How Truvace uses pooled results

The full story

The authors searched Web of Science, Scopus and Google Scholar plus grey literature, following PRISMA guidelines. They found 1,874 records, removed 423 duplicates and screened 1,451 titles and abstracts. They then read 142 full texts and kept 18 studies, with two independent reviewers at each stage and a third resolving disagreements. Included studies had to be in English, published 2015 to 2024, and contain quantitative data on automation risk, employment or skills.

Studies were scored 1 to 5 on five quality criteria and analysed with random-effects models in Comprehensive Meta-Analysis software. Heterogeneity was high in every category (I² of 99.4% for automation risk, 82.18% for employment and 98.9% for skills). Funnel plots, Egger tests and trim-and-fill tests found no significant publication bias.

The automation-risk estimates pooled five studies: Frey and Osborne, Arntz and colleagues, Nedelkoska and Quintini, Hawksworth and colleagues, and Manyika and colleagues. Meta-regression with four moderators explained 83.4% of the variation between them. The authors read the task-based results as a sign that technology tends to automate specific tasks rather than whole occupations.

On skills, six studies were reviewed but only four could be pooled, because the IFR report is qualitative and the Fossen and Sorgner study is focused on entrepreneurship. The authors recommend country-specific strategies, training in both technical and social-emotional skills, support for new kinds of jobs and gradual management of workforce transitions.

Sources

1 on file
  1. Peer-reviewed2025-06-01

Talk it over

Four questions to start on
Who carries this cost, and what would have to change to prevent it?

Start with a question

18-study review shows robots cut routine manual work, lifted analytical tasks in rich nationshttps://truvace.com/item/effects-of-artificial-intelligence-and-robotic-technologies-on-the-future-of-wor

  1. Who carries this cost, and what would have to change to prevent it?

    The problem A meta-analysis of 18 studies found that robot use in developed economies was linked to a significant decrease in employment in routine manual tasks (g = -0.441, p < 0.001).

    Answer in the thread
  2. How do we know? What can this kind of evidence show, and what can’t it?

    The evidence This rests on a systematic review or meta-analysis with a sample of 18, from Journal of Global Strategic Management.

    Answer in the thread
  3. What does it leave open? What else would you want to know before acting on it?

    The record’s own caveat The included studies differ in method, which makes comparison difficult, and the literature does not yet adequately cover generative AI or the long-term effects of shocks such as COVID-19.

    Answer in the thread
  4. Have you seen AI used this way where you study, work or live? Did it go the same way?

    Answer in the thread

The questions are the same on every record. The details under them are taken from this record as written.

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