report<\/a> out of Glean\u2019s Work AI Institute, a collaborative effort with AI experts at top universities such as Stanford University, University of California at Berkeley, and Harvard University.<\/p>\n\u201cHigh AI achievers don\u2019t just prompt and pray,\u201d the study\u2019s authors state.<\/p>\n
The 6,000 workers involved in the study estimate that AI automation saved them at least 11 hours every week. At the same time, only 13% say their organizations are performing significantly better as a result. <\/p>\n
The study\u2019s authors separated out the top performers in AI (people who report both productivity and quality gains from using AI) versus the rest. The data shows that the successful companies \u2013 13% of the sample \u2013 aren\u2019t \u201cbuying more AI tools, burning more tokens, or building adoption dashboards that glow a triumphant shade of green. They\u2019re doing the harder work of treating AI as a work-design problem, not a procurement one.\u201d<\/p>\n
The successful AI organizations \u201cstart with the work, selecting tools and platforms that fit the job instead of letting vendor contracts dictate their AI strategy, the authors point out. \u201cAnd they understand that giving AI access to data is not the same as giving it context.\u201d<\/p>\n
Tellingly, more than half of workers, 53%, say critical information they need to do their jobs is not accessible through their AI systems. By contrast, workers in \u201ccontext-rich\u201d AI organizations are 64% less likely to feel worn out by AI, 52% less likely to ship work they can\u2019t explain, spend 9% less of their AI time botsitting<\/em>, and are 31% less time botshitting<\/em>.<\/p>\nSuch organizations are still the exception. \u201cMost organizations will keep learning the hard way that AI\u2019s time savings aren\u2019t free,\u201d the Glean authors state. “The hours workers save come back as botsitting. The judgment they offload comes back as
botshitting. The workplace fills up with work that looks finished, sounds confident, and is hollow enough that some exhausted human \u2014 usually without credit or reward \u2014 still has to mop it up.”<\/p>\n
The AI achievers are 18% more likely to refrain from using AI on certain tasks, the data also shows. And \u201cthey\u2019re also more likely to bend or break the rules to get value from it: 54% use unapproved tools or approved tools in noncompliant ways, and 36% hide how much AI is helping them \u2014 often because they\u2019re working around an official system that is too slow, too narrow, or too disconnected from how the work actually gets done.\u201d <\/p>\n
The companies pulling ahead aren\u2019t just swapping out tasks for AI; they\u2019re actively redesigning work, the study also concludes. In top-performing organizations, 90% say their employer treats AI as a \u201cchance to redesign work,\u201d compared with 54% of the lagging organizations, <\/p>\n
Very importantly, 90% of workers in advanced AI organizations say their employer provides enough AI training and support, compared with 52% at less-engaged organizations. Reward systems also are being redesigned around AI \u2013 84% of the AI leaders say their employer formally rewards AI skills, compared with 48% of the laggards. <\/p>\n<\/div>\n
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