Author: IBL News

IBL News | New York

According to The Work AI Index 2026, published by Glean’s Work AI Institute, 75% of digital workers save roughly 11 hours each week with AI. However, they burn an average of 6.4 hours a week making AI usable, including feeding it missing context, checking its outputs, debugging its mistakes, rerunning prompts, and cleaning up the confident-but-wrong answers AI leaves behind. It means two things: for every hour a worker spends getting useful output from AI, they spend roughly another hour making it usable, and, additionally, that most of a full working day every week. Around 37% of the work is unproductive.

This studio, which seeks to understand the hidden human labor that AI has added to the workday, follows a survey of 6,000 full-time digital workers — employees who do most of their work on a computer or digital tools — across the United States, the United Kingdom, and Australia. The research shows that workers are turning to AI first, sometimes before they turn to their colleagues, their managers, or even their own judgment.

Another interesting finding is that 87% of digital workers now use AI at work, but only 13% say their organization is performing well.

Another phenomenon involves shipping AI-generated work (code and words) that workers haven’t reviewed, don’t fully understand, couldn’t defend if asked, or simply makes mistakes or delivers deficient outputs. Today, 69% of AI users admit to doing so at work, and 41% of workers now ship AI outputs they can’t explain. And when AI-generated work fails, 40% of workers blame AI. Only 29% admit it was their own fault.

The main outcome of the research is that AI has arrived in the workplace, but organizational impact has not. In other words, AI is everywhere, and adoption is near-universal, but the gains are not.

“Organizations must build the human infrastructure (not just the technology infrastructure) that makes AI worth using, or they’ll keep paying the bill.”

The Work AI Index 2026 notes that there are three paradoxes that reinforce each other and result in productivity leaks:

• The productivity paradox. AI makes individuals more productive, but those gains don’t translate to teams or organizations due to what researchers call coordination neglect — our chronic tendency to underestimate the work and effort required to coordinate work across people, teams, tools, and systems..However, at the individual level, the numbers look impressive: 75% of workers say AI makes them more productive and 63% of workers say AI lets them do things they couldn’t do before.
• The judgment paradox. AI makes oversight more important and strips away the cues that once triggered it. AI makes bad work look polished. At first, it’s manageable, and people check it. But as the pile grows, they start to skim it, then wave it through.
• The ownership paradox. The more workers fear AI, the tighter they cling to it. Many workers are trapped between two AI-related threats at once: the risk of being replaced by AI and the risk of appearing obsolete if they do not use it enough. So workers double down. ging the appearance of it:
  33% downplay AI’s help.

In the workforce, developers use it to write code, analysts use it to crunch the numbers, and communications professionals use it to draft the content. However, high AI achievers (in productivity and quality of work) learn from everything — but their biggest edge is treating AI itself as a teacher; and they spend a smaller share of their AI time on their primary core task than low AI achievers do.

That’s the choice in front of every organization. Build the human infrastructure that makes AI worth using. Or keep paying the bill — in botsitting, in botshitting, and in the steady departure of the people who got tired of cleaning up after the bots.