Managers who use AI every day spend twice as many hours on coordination work as those who don't, according to new research from In Parallel, a Helsinki-based company that helps organisations share context with their AI systems.
The finding comes from In Parallel's inaugural Coordination Tax Index, which surveyed 247 managers across five countries. On average, respondents reported losing 16.5 hours a week to status meetings, re-explaining context, and searching for information that already exists elsewhere in the organisation. That works out to roughly $64,600 per manager per year in lost productivity.
The AI adoption paradox sits at the centre of the findings. Daily AI users reported spending 20.3 hours a week on coordination activities, compared with 9.1 hours for non-users. In Parallel's interpretation is that the problem isn't models falling short on capability; it's that today's AI tools lack access to the shared organisational context needed to understand what teams have already decided, discussed or committed to. Individual productivity gains from AI are running ahead of the organisational infrastructure needed to make AI genuinely useful for coordination.
Managers also significantly underestimate the scale of the problem. Respondents believed coordination consumed around 21% of their working week; their own itemised responses put the figure at 41%, with nearly half of that time spent in status meetings.
"Everyone expected AI to eliminate busywork. Instead, we're seeing organisations invest in increasingly capable AI while managers continue spending enormous amounts of time simply keeping work aligned," said Markku Mäkeläinen, CEO and co-founder of In Parallel. "Our customers tell us they didn't invest in AI just to work faster. They invested because they expected it to reduce the time spent chasing information, rebuilding context and coordinating work."
"The next generation of enterprise AI won't be defined by better models alone," added Kristian Luoma, co-founder of In Parallel. "It will be defined by how well AI understands the organisational context it operates in."
In Parallel's own product is an AI context layer that connects meeting decisions, project history and ownership information into a shared store accessible to both people and AI. The research serves as both a market signal and a case for that approach.
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