The two hours do not show up anywhere clean. There is no line item in your calendar labeled "coordination overhead" and no ticket in your project management tool that captures the time spent copying data between systems. The time disappears into the texture of the day: pasting a company name from Salesforce into a Notion doc, then opening Slack to find the account executive who owns the account, then returning to Salesforce to update a field, then going back to Notion to add the note you forgot.
This is tool-switching. For ops and revenue teams, it is the background noise of every working day. It does not feel like waste because each individual switch takes only 90 seconds to three minutes. The cost is invisible until you add it up.
Why the Gaps Are Invisible
Productivity tracking tends to measure output: tickets closed, calls made, revenue generated. What it does not measure is the connective tissue between those outputs. The time spent pulling context from one tool to paste into another is not a distinct task. It is a behavior woven into every task.
When we mapped the recurring coordination work of ops teams while building Sauna Labs, we found a consistent pattern: the average workflow that spans three or more tools involves at least four manual handoffs. A handoff here means a human stopping one thing, switching context to another tool, finding or entering information, then switching back. Each handoff takes somewhere between 90 seconds and five minutes depending on how well the tools surface context and how recently the person was last in that tool.
Multiply that across a workday and you can see where the two hours come from. The surprising part is not the per-switch cost. It is the frequency. On a day with normal ops volume, a team member working across a typical stack of six to eight tools will execute dozens of these switches before lunch.
The Specific Anatomy of a Context Switch
Consider a deal handoff from sales to customer success. The sales rep closes the deal in the CRM. The customer success manager needs to pull deal notes from the CRM, find the relevant Slack channel or thread where the account was discussed, check the contract in the document storage tool, create or update a record in the CS platform, send a handoff email to the customer, and add a note in Notion with context about the account history.
None of these steps is difficult. Each one takes two to four minutes. Together they take 20 to 30 minutes. This workflow happens every time a deal closes. In a team closing 15 deals a month, that is 5 to 7 hours of pure coordination work per month, just for handoffs. And that is one workflow among many.
Now consider QBR prep, contract renewals, onboarding sequences, and follow-up cadences. The same pattern repeats across all of them. The problem is not that the individual tasks are hard. It is that the volume of instances, compounded across the team and across the week, adds up to real time that could be spent on work requiring actual judgment.
The False Solution: More Integrations
A common response to this problem is to add integrations. Connect the CRM to the CS tool so data flows automatically. Use a workflow automation tool to push deal closures to Slack. Link the contract tool to the CRM via a native integration.
These integrations reduce some friction, but they do not solve the coordination problem. An integration moves data from one place to another. What ops workflows typically require is a sequence: first check this, then update that, then notify this person, then wait for a confirmation, then take the next step. Integrations are point-to-point. Coordination is sequential.
When teams discover that their integration handles only the first step in a six-step workflow, they end up adding manual steps alongside the automation. The integration becomes one node in a process that still requires human coordination for the other five nodes. The overhead is smaller but it is not gone. And now the process has both manual and automated steps, which means debugging it when something goes wrong is harder than before.
The Cognitive Cost Beyond the Clock
Time is not the only cost of tool-switching. There is also the cognitive cost of context reconstruction.
When you stop a task to switch to another tool, you lose the mental context of what you were doing. Research on knowledge work patterns has consistently found that returning to a task after an interruption takes substantially longer than the interruption itself, because the mental model that supports the original task has to be reconstructed. Context switches initiated by the person doing the work carry a similar cost. You choose to make the switch, but you still pay the reconstruction penalty when you return.
For ops team members handling coordination across multiple accounts simultaneously, this compound context loss shows up as errors: wrong data entered, wrong version of a document referenced, a Slack message sent to the wrong channel because the person was mentally in the wrong account context when they typed it. These are not careless mistakes. They are predictable outputs of a process that requires constant context reconstruction.
What Measuring the Gap Actually Requires
Before any automation makes sense, you need a clear picture of where the context switches are actually happening. This is harder than it sounds because the behavior is habitual. Ask an ops team member to list their recurring manual coordination tasks and they will name four or five. Watch their work patterns for a week and you will find twenty.
At Sauna Labs, we built the observation layer because self-reporting does not capture this accurately. Humans habituate to friction. The ten-second copy-paste between Salesforce and Notion becomes invisible after the hundredth repetition. The observation layer logs the actual sequences of actions, across tools, over time. That is where you see the true shape of the coordination overhead, not in a survey or an interview.
We are not saying all tool-switching is eliminable. Ops work inherently involves multiple tools, and that reflects reasonable choices about how to structure specialized functions. What we are saying is that a significant portion of the context switches happening in a typical ops team's day are purely mechanical: gathering information from one place to enter or act on it in another, with no reasoning required in the middle. That portion is where the time actually lives, and that portion is where removing manual steps creates real returns.
The Patterns Worth Targeting
From the work patterns we have observed across the teams building with Sauna Labs, the recurring workflows that generate the most coordination overhead tend to share three characteristics.
First, they span at least three tools. Single-tool workflows do not generate context switches. The problem compounds with each additional tool in the chain, because every tool boundary is a potential manual handoff point.
Second, they repeat on a predictable schedule or trigger. The handoff workflow happens when a deal closes. The QBR prep happens every quarter. The renewal sequence starts 90 days before contract expiry. The predictability is exactly what makes the pattern visible and automatable.
Third, they involve assembling context from one place and acting on it in another. This is the core of tool-switching overhead. You navigate to a tool to learn something, then navigate to a different tool to do something with what you learned. The work is in the assembly and transport, not in the reasoning about the information itself.
Identify the workflows in your team that match all three of these characteristics and you have found the candidates where removing manual steps will have the clearest impact. That is where the two hours actually live, and that is where it makes sense to start.