Brit workers waste nearly six hours a week 'botsitting'

Almost all UK workers now have to deal with AI, but few firms report big productivity gains because of all the time lost in hand-holding the systems and cleaning up their mistakes. So says a report by the Work AI Institute, a research arm of AI biz Glean Technologies. It claims there are productivity gains to be had from introducing AI-based tools, yet much of this is being negated by the amount of time employees waste making them work - a phenomenon it has christened "botsitting." The organization surveyed 1,500 digital workers for "The Work AI Index: UK 2026" report, finding 90 percent are now required to use AI in their roles, 80 percent use multiple AI tools every week, and 39 percent use four or more. The workers indicate AI automation saves them roughly 12 hours a week, or just under a third of their working week. Yet only 18 percent agree AI has significantly improved their organization's performance. The time freed up isn't flowing into productive work, it's being absorbed by the unglamorous human labour required to keep those systems running, according to the Work AI Institute. For every hour a UK staffer spends getting output from their AI tools, they spend roughly another hour making it usable. Part of the reason so much time disappears into botsitting is how often the tools fail, with employees finding that more than a third (36 percent) of AI sessions fail outright, requiring a full restart or substantial reworking. On average, Brit workers waste 5.8 hours each week in these botsitting processes, the report says. This time is typically taken up by loading the context window with information the AI should already have, and overseeing the output. The latter involves reviewing answers and trying to catch outputs that are wrong, incomplete, or missing important context. When workers spot a problem with the output, they may have to re-prompt, add more context, swap models, and re-prompt again until something usable comes back, the researchers claim. And if they aren't diligent enough to spot when an AI tool has goofed up, the mess lands on colleagues who weren't involved with the work, but now have to fix something they didn't break. Most of this botsitting effort is grunt work, the report notes, such as reloading context into different tools, catching hallucinations, and verifying outputs that may appear perfectly fine at first glance. In effect, workers are serving as the integration layer for their company's AI tools, having to tell them which information sources to use, which documents are current, and what other key details matter, as well as correcting their mistakes. Interfaces and standards such as APIs and the Model Context Protocol (MCP) were supposed to solve this by letting tools talk to each other and share data, the Work AI Institute says, but they don't solve the context problem. Workers eventually tire and start to cut corners, becoming less diligent in checking outputs, verifying sources, or checking whether the AI's recommendations make any sense, the survey says. 70 percent of UK AI users admit to simply passing on the first output that looks "good enough." According to the Work AI Institute, the UK has moved fast on AI uptake, leading even the US on key adoption metrics. However, it is the depth of adoption that stands out, going beyond using it for content generation and moving it into the activities that shape working life. The report warns AI is now being used in higher-stakes areas where UK law is tightly regulated, such as HR decisions. It claims more than half of UK workers are comfortable with AI playing a role in performance evaluation, and nearly 40 percent say it is already used in reviews. British workers are more comfortable than Americans with AI in hiring, promotion, compensation, and even termination decisions. Even so, local organizations are less likely to use AI in termination decisions because employment law makes dismissal harder to defend than in the US. The report concludes that Britain has built a stronger institutional foundation for workplace AI than almost any other country, and claims this is a potential advantage. Yet the value of this AI investment will come from operational discipline, and measuring whether the work produced is better, not just faster. Otherwise the hours workers "save" are lost again in botsitting. "Adoption alone doesn't equal transformation," said Dr Rebecca Hinds, head of the Work AI Institute at Glean. "If employees are spending the productivity dividend on botsitting, companies haven't eliminated work - they've created a new layer of overhead." (R)