Google Study Reveals AI Adoption Is Widespread But Job Automation Remains Limited

Despite AI hype, Google's data shows workers aren't automating themselves away

Anyone following the AI space is by now familiar with lofty claims that AI models will soon be better than humans at everything and capable of replacing vast swaths of the human workforce. In a new study from Google Research, though, a team that looked at how workers are actually using Gemini "[did] not find evidence... to support the claims that AI is about to cause massive automation and displacement of white-collar work..." The paper, released last week, introduces the "AI & Economy ATLAS," an Activity, Task, Landscape, and Adoption Study of 15 million anonymized AI interactions across the Gemini App, Google's AI Mode, and the Gemini API. Their initial review of the data finds that, while AI sees some significant use across a wide variety of occupations, that use "remains shallow and overwhelmingly collaborative in nature, with end-to-end task automation limited in scope." "AI appears useful for a subset of tasks..." To come to these conclusions, Google researchers used an automated classifier to sort work-based AI interactions using the Bureau of Labor Statistics' Standard Occupational Classifications and O*NET's more detailed database of specific work interactions. While this method required some probabilistic classification of "inherently uncertain" interactions, verification by human reviewers found it to be a reliable gauge of how Gemini prompts were being used for work. Unsurprisingly, white-collar jobs in fields like computers, finance, and arts and entertainment were some of the ones where the volume of Gemini use was overrepresented (when compared to their prevalence across the US economy). Financial/market analysts, software developers, and systems administrators were some of the relatively heaviest users of AI for job-related tasks, while salespeople, transportation workers, and food preparation/service workers were heavily underrepresented in the AI use data. The researchers also attempted to measure how deeply AI was being integrated into various jobs, looking at how often individual, granular O*NET work tasks were attempted using Gemini. Across that entire database, the ATLAS researchers only classified 21 percent of all work-related tasks as "Gemini tasks" -- those that met a minimum threshold of 25 related interactions attempted in the massive sample. For many occupations (29%), not a single relevant work task achieved this "non-negligible" Gemini usage threshold, suggesting those jobs have been minimally impacted by the AI revolution so far. For another 30 percent of all occupations, less than one-quarter of tracked tasks saw significant related Gemini usage, suggesting humans were still the ones responsible for the vast majority of the component parts of those jobs. In only 3 percent of occupations was Gemini being regularly consulted for at least three-quarters of that job's relevant tasks. Jobs like software quality assurance analysts and testers, human resources specialists, and document management specialists fell into this bucket and are seemingly the most impacted by AI use in the study. Altogether, the researchers write, these kinds of numbers suggest that "AI is currently serving primarily as a complement to existing work" and that "AI appears useful for a subset of tasks performed within occupations, but they do not currently appear to be comprehensively used for performing the work currently done by humans." While the researchers say that this state of affairs may change "as new AI breakthroughs emerge," it's also possible that new workflows "will maintain a degree of complementarity between workers and AI systems." Give AI the low-expertise, non-routine work Beyond looking at high-level occupations, the Google researchers also looked at the specific kinds of work tasks that Gemini users ask the model to undertake. Cognitive tasks (i.e. those that primarily involve thinking) represented a whopping 86 percent of the Gemini interactions measured (by volume), while interpersonal and manual tasks were underrepresented in the sample compared to their workplace prevalence. That doesn't mean models like Gemini have been useless for more manual blue-collar jobs, though. The researchers found thousands of examples of industrial machinery mechanics using Gemini for "analyzing test results and machine error messages," for instance, on top of tens of thousands of conversations where auto mechanics used Gemini to help with "testing vehicle components and systems, rewiring systems, and inspecting parts for wear." These workers were much more likely than others to feed Gemini a photo for reference, rather than text. When it comes to more cognitive work, the majority of significant Gemini tasks observed were related to either the "drafting and generation" of ideas or "information retrieval and learning." A minority fell into a bucket related to automation of work tasks, even for the parts of these jobs that were judged to be "routine." Crucially, the kinds of cognitive tasks the researchers found workers offloading on AI were overwhelmingly ones that didn't require a lot of expertise (as measured by the complexity and entropy of the words involved in their descriptions). Low-expertise tasks such as rewriting material in different languages and writing and reviewing product specifications were heavily over-represented in the Gemini sample, suggesting that humans are much less likely to use the model for the most complex parts of their jobs. Taken as a whole, all this data suggests to the researchers that workers are not "automating themselves out of existence" with AI. Instead, employees seem to overwhelmingly be using AI to "augment their work by automating routine cognitive tasks and simultaneously collaborating with AI in their performance of non-routine cognitive work." This could change if future models become more useful for high-expertise tasks or if AI-powered robots become better at performing manual tasks, the researchers note. Overall, though, the researchers write that the current usage patterns of Gemini for work-related tasks points to "greater returns to human skill in [the] non-routine dimensions" that still dominate most job descriptions.

Google study finds broad AI use, but little evidence of job automation

Why it matters: The report offers a broad snapshot of real-world AI use, highlighting the gap between jobs that could be affected by AI, jobs where workers are using AI and tasks being automated. The big picture: Google found that people mostly use Gemini as a collaborator -- for research, drafting, iteration, troubleshooting and learning -- rather than delegating entire jobs to it. * "Adoption of AI is very, very broad in that it touches a huge range of occupations," said Scott Strand, an economist on Google's Technology and Society team. * Gemini usage covered 70% of jobs, Strand said, adding that those jobs where AI is being used represent 90% of U.S. employment. * At the same time, he said AI use is also "very shallow," with the average worker using it for only 21% of tasks, with less than 10% of Gemini interactions seemingly geared at automating non-routine cognitive work. * That is directionally similar to earlier research from Anthropic and OpenAI, though Anthropic did report a higher level of automation. Methodology: The report, known as ATLAS -- Activity, Task, Landscape and Adoption Study -- analyzed 14.65 million de-identified interactions across the Gemini app, Google's AI Mode search experience and the Gemini API over two weeks in April. * Automated systems classified interactions as work-related or non-work-related, then mapped work-related activity to 4,000 tasks across 800 occupations in 150 countries and 140 languages. * Non-work tasks were mapped to the categories used in the Bureau of Labor Statistics' American Time Use Survey. Yes, but: The study did not include how people are using business-focused tools such as Gemini Enterprise and Google Workspace because the company doesn't maintain logs for those products. It's possible that that additional data could show greater levels of automation. * "We obviously would love to use that data if we could," Strand said. "It just wasn't even an option for us." Between the lines: Workers in higher-paid jobs and in wealthier geographies use AI more, the study found. * For example, Google found that a 1% increase in an occupation's median earnings is associated with a 2.68% increase in the level of AI use. Zoom in: The survey found more use by workers in blue-collar jobs than researchers anticipated, including electricians seeking wiring diagrams and auto repair workers looking for engine maps. * "Blue collar workers tend to be using a lot of what we call multimodal AI, which is AI with images and video," Strand said. * Consumer use was also broader than expected: Non-work interactions accounted for 86% of Gemini conversations, Google found. * Consumers used Gemini not only to complete tasks such as cooking and cleaning more quickly, but also to navigate government services and processes. What we're watching: The open question is whether studies like this can help predict where AI use is headed -- or whether they are mainly snapshots of how people have already used the technology.

Understanding the AI economy

Summaries were generated by Google AI. Generative AI is experimental. There is broad agreement that AI's potential to transform the global economy and the way we work is significant. However, the outcomes - what this means for work, for people's lives, and the economy writ large - are not automatic nor guaranteed. A lot has to happen. To get there, we as a society must work together to positively shape how AI impacts our lives, jobs, and economy. In order for this shared work to be effective, it is critical to have a rich understanding of how AI is being adopted and used in the economy. Society needs empirical insights and evidence-based research to inform decisions, initiatives, and actions. To help, Google is launching the first iteration of the AI & Economy ATLAS (Activity, Task, Landscape, and Adoption Study), an ongoing, large-scale, de-identified study of how people are using Google's AI products and tools. ATLAS's first dataset (v1.0) is built from 15 million aggregated and de-identified human-AI interactions across the Gemini App, AI Mode, and the Gemini API, which together are used by more than 1 billion people monthly. ATLAS v1.0 insights span more than 150 countries, 140 languages, 800 occupations, and 4,000 tasks; ATLAS is the most comprehensive look to date at how real people are using AI at scale. ATLAS sheds light on how people are using Google's AI tools for various tasks at work and in their day-to-day lives. The ATLAS v1.0 report provides an early view of a quickly moving landscape: AI's capabilities are advancing, its use is evolving, and tools for observing its impact on the economy are still a work-in-progress. What are we learning from ATLAS v1.0? Here a few of the most interesting observations so far: * AI use at work is broad but shallow: Workplace adoption spans all industry sectors and also 68% of all occupations that collectively represent 90% of total U.S. employment. However within jobs, people are using AI selectively: in a typical job AI is used for only ~21% of tasks. * At work, most AI use is focused on collaboration and assistance with tasks, and so far task automation is uncommon: ATLAS data shows the vast majority of AI interactions at work focus on collaborative uses such as ideation, strategy, information retrieval, and learning. Tasks like creative design and hypothesis testing (categorized in ATLAS as "non-routine cognitive") show up in AI work interactions at a much higher rate than in the economy as a whole (65% vs 35%). Less than 10% of those interactions fully automate tasks. * AI use is not limited to white collar workers, it's also assisting workers in predominantly physical and manual occupations with adjacent tasks: AI use for work is not limited to jobs traditionally seen as knowledge work. While not as prevalent, workers in manual and technical trades (e.g., auto technicians, industrial mechanics) are using conversational AI as a live collaborator for real-time diagnostics, troubleshooting, and on-the-fly learning. When workers in these areas use our AI tools, they're 2x more likely to use multimodal AI (i.e. using AI to create images or video). For example, automotive technicians and industrial mechanics use AI to interpret complex test results, debug electrical wiring, and inspect machinery for wear. * AI is delivering value at home that may be missed in standard economic metrics, particularly around high-friction administrative tasks: Over 86% of interactions with AI tools in ATLAS occur outside of work. People are using AI in new and interesting ways not captured in standard economic metrics including productive household activities (e.g. researching purchases, help with using appliances, and tools) and high-friction administrative tasks (e.g. navigating government services like taxes, licensing, and fines). * Global AI adoption is tracking GDP per capita, with notable exceptions: AI usage has diffused globally. ATLAS data shows AI usage in over 150 countries and territories that represent 99% of the world's population. We also see this in the diversity of languages used in ATLAS. English represents only about a third of global AI conversations, and users do not systematically abandon their native languages for complex tasks. Looking more deeply, on a per-capita basis AI usage closely mirrors a country's relative level of wealth, raising concerns about a persisting digital divide. However this isn't a universal rule: some middle-income countries in South America and the Middle East are adopting AI at rates comparable to higher-income countries. Here are some additional findings:

Global Studies Show Companies Use AI for Collaboration Not Automation | PYMNTS.com

Google found in its inaugural AI & Economy ATLAS (Activity, Task, Landscape and Adoption Study) that while AI is used in 68% of occupations, it is used for only about 21% of tasks in a typical job, the company said in a Thursday blog post. The study used 15 million aggregated and de-identified human-AI interactions across Google's AI products and tools to determine how people are using the technology, according to the post. The tasks for which AI is used tend to be part of a collaboration, rather than automation. Examples include ideation, strategy, information retrieval and learning. Less than 10% of AI work interactions fully automate tasks, per the post. "Only 3% of occupations showed AI usage for over 75% of their tasks; these occupations include software quality assurance analysts and testers, human resources specialists and document management specialists," the report said. Another study released Thursday was commissioned by Switzerland-based talent and technology expertise company Adecco Group. It found that "AI is changing tasks faster than eliminating jobs," Adecco Group said in a press release. After three and a half years of ChatGPT being available, employment rates remain at record highs across 38 countries that are members of the Organization for Economic Co-operation and Development (OECD) and employment in jobs with exposure to AI remains stable. In addition, 1.9 million new AI-related jobs were created, according to the release. Fewer than 10% of firms in the U.S. have integrated AI into their core workflows at scale. Where the technology has been deployed, it is reshaping tasks, workflows and skill requirements faster than it is eliminating entire occupations, per the release. "The next phase of AI will be won by redesigning work -- understanding what people should do, what AI agents can do, how skills are evolving and what skills will be needed in the future, and how value is created and measured," Adecco Group CEO Denis Machuel said in the release. The PYMNTS Intelligence report "Wage to Wallet™ Index: The Resilience Deficit: Labor Workers in an Automated Economy" found that AI and automation are moving beyond higher-paying jobs and into the Labor Economy.