With the frenzied adoption rate of AI, countless workers and businesses are enjoying some great productivity gains. But, on the way, productivity problems are often looked at in the wrong context. The assumption is usually that teams are overloaded because the work itself has become too complex, too fast-moving, or too under-resourced. New research from Zapier suggests a different problem may be doing more damage: The work is arriving in too many places, with too little context, and too much time is being spent just figuring out what needs to happen before anyone can actually begin.
While it may sound like merely an operational nuisance, it’s actually a revenue problem. In Zapier’s Cost of Disconnected Work Report, 63% of operations professionals say their team has experienced delayed or lost revenue because of missing or delayed internal requests, and 30% said they had experienced both.
The source of the problem is less about effort than structure. Most respondents say requests come in through three or four different channels, with email leading the list, followed by phone or text, instant messaging platforms such as Slack or Teams, third-party ticketing or project tools, and verbal asks in meetings. Overwhelmingly, (93%) those professionals say requests routinely arrive with missing information – most commonly without a clear description of what is actually being requested.
That missing context has a measurable cost.
- 42% of respondents say they spend 6-15 minutes organizing a single request when it comes in from multiple sources.
- 32% say it takes more than 15 minutes just to understand and sort the request before any actual execution begins.
For teams already handling high daily request volume, that kind of intake friction compounds quickly. Half of task-focused leaders handle at least 26 internal requests, tickets, or tasks in an average workday, while one-quarter handle 51 or more. That compounds to hours of unnecessary time to decipher request.
It’s not just about the time, though. The mental drain is also a very real concern, and 36% or operations professionals say the mental fatigue of managing requests outweighs the effort of completing the work itself.
- 44% consistently chase updates and approvals.
- 41% are regularly interrupted by follow-ups and context switching.
- 39% say their work feels reactive rather than planned.
It gets even worse when things fall through, with co-workers or managers having to help out nearly half the time, about a third of other requests being delayed, and a third also saying their team experiences burnout.
Here’s the message: Disconnected intake is not just annoying; it acts like a hidden tax on productivity, burning time and attention before work even begins, then creating downstream delays when requests stall, bounce between systems, or arrive incomplete.
Platform sprawl doesn’t help. Teams using one or two request platforms are 81% more likely to process a request in under five minutes than teams using three or more. It’s also important to note that nearly half of those lower-sprawl teams say they have never lost revenue to missing or delayed requests – as opposed to only a quarter of teams using three or more platforms. That’s significant because it raises a real – and solvable issue. The issue is not simply whether teams have too many tools and a lot of software, but whether work enters the organization through a fragmented intake layer that forces people to repeatedly perform manual triage.
This is also where the right AI solution can make a difference – not by replacing the people doing the work, but by reducing the manual triage that happens before the work begins. If requests can arrive from email, chat, ticketing systems, or forms and still be automatically standardized, enriched with the right context, routed to the right owner, and tracked consistently, teams spend less time deciphering requests and more time executing on them. In that sense, AI’s value is not just speed, but structure.
Why does that matter? The real problem isn’t simply tool sprawl; it’s the lack of a consistent intake and coordination layer across those tools. An effective AI-driven solution in this environment would help teams close information gaps, flag incomplete requests, prompt for missing details, and move work into a common workflow before delays and follow-ups start to multiply. For operations leaders, that's where AI becomes less of a novelty and more of an operational lever by reducing friction, lowering cognitive load, and helping teams recover time that is currently being lost before real work even starts.
That doesn’t mean fewer tools alone will solve the problem. In practice, most organizations are not going to eliminate email, chat, ticketing, and project systems altogether. The longer-term takeaway is that companies need a more standardized way to capture, route, and track requests, regardless of where they originate. In that context, the report is less an argument for ripping out tools than for adding a coordination layer between them.
If a team is spending 10-15 minutes sorting a request before the work starts, and doing that dozens of times per day, the problem is not that the team is slow, but that the organization has allowed too much unstructured intake and too little consistent routing. The result is a system where hard work gets absorbed by coordination overhead instead of execution.
It’s really an operational management story that says some of the most expensive inefficiencies inside modern teams are not found in the work or employees, but in disconnected systems, missing context, and manual cleanup. Fixing that may be one of the clearest paths to increased productivity, lower burnout, and faster revenue realization.
Edited by
Erik Linask
