Key Takeaways
- O*NET reports daily email use for 97% and daily telephone conversations for 99% of surveyed executive secretaries and executive administrative assistants.
- O*NET reports constant contact with others for 80% of respondents in that occupation.
- BLS projects 489,300 executive secretaries and executive administrative assistants in 2025, with little overall employment change through 2035.
- No public national dataset supplies a universal workload capacity target for an administrative team.
Administrative workload is often described with a single word: busy. That is not enough to diagnose capacity. A team can handle many requests with predictable quality, or it can handle fewer requests while absorbing constant interruptions, exceptions, and executive-facing decisions. A useful workload analysis measures both volume and the conditions under which the work is completed.
Public data provides occupational context, not a capacity standard for a particular company. This page separates those two things. It uses O*NET and Bureau of Labor Statistics data to describe the administrative role, then gives teams a practical way to measure their own workload.
What the public data says about administrative work
The O*NET profile for executive secretaries and executive administrative assistants describes a role that handles information requests, correspondence, visitors, conference calls, meeting schedules, research, reports, and routine administrative work. The detailed activities include scheduling, travel arrangements, report preparation, business correspondence, records, documentation, and coordination.
Its work-context data shows why volume alone can be misleading. O*NET reports that 97% of respondents use email every day, 99% have telephone conversations every day, and 80% report constant contact with others. Those measures describe the occupation's communication environment. They do not mean that every administrative employee has the same volume, response standard, or workload strain.
| Public signal | Reported figure | Appropriate use | Important limit |
|---|---|---|---|
| Daily email use | 97% | Shows email is a routine part of the occupation | Does not count messages or time spent on them |
| Daily telephone conversations | 99% | Shows frequent communication demands | Does not count calls, complexity, or urgency |
| Constant contact with others | 80% | Indicates a highly interactive work context | Does not measure interruptions for a particular team |
| Executive-assistant employment, 2025 | 489,300 | Provides U.S. labor-market context | Does not measure staffing need at one employer |
The BLS Occupational Outlook Handbook projects 489,300 executive secretaries and executive administrative assistants in 2025 and 489,200 in 2035. The projected near-flat change is a workforce forecast, not proof that administrative demand is flat inside every organization. Changes in industry, executive span of support, technology, service levels, and work design can move a specific team's workload in either direction.
Why there is no universal administrative workload benchmark
There is no authoritative national series that says one administrative assistant should process a fixed number of requests, support a fixed number of leaders, or spend a fixed share of time on email. Such a number would ignore the work itself.
Consider two calendar requests. One may be a recurring internal meeting with a known attendee list. Another may require coordinating international travel, accessibility needs, confidential stakeholders, and several changing priorities. Counting both as one request would hide the actual effort and risk.
The same problem applies to executive ratios. An assistant supporting three leaders with stable routines can have a different workload from an assistant supporting one leader with frequent travel, investor meetings, or customer escalations. Use ratios as a starting question, not a staffing conclusion.
A measurement system that produces usable workload statistics
Start with a defined observation period. Four to eight typical weeks can identify the work mix; a longer period may be necessary where quarter-end, enrollment, events, or seasonal peaks matter. Record the unit of work, the time required, the urgency, the number of handoffs, and the outcome.
| Measure | Calculation | What it reveals |
|---|---|---|
| Incoming demand | New requests by day or week | Whether demand is stable, rising, or clustered |
| Completion rate | Completed requests divided by received requests | Whether the queue is keeping pace |
| Backlog age | Days open for unfinished work | Whether important work is waiting too long |
| Cycle time | Completion timestamp minus request timestamp | Speed from request to completed result |
| Rework rate | Requests reopened or corrected divided by completed requests | Quality cost and unclear instructions |
| Interrupt load | Unplanned urgent requests divided by all requests | How much planned work is displaced |
| Focus capacity | Time available for planned work after meetings and urgent work | Whether capacity exists for higher-value work |
Use categories that reflect actual work rather than a generic list. A useful set might include calendar logistics, travel, meeting preparation, inbox triage, document preparation, expense follow-up, data maintenance, executive correspondence, and sensitive exceptions. Assigning every request to one category exposes where the team is spending time and where a process is too vague to measure.
For time estimates, sample completed work instead of asking people to reconstruct an entire week from memory. A lightweight method is to capture start and completion times for a random selection of requests, then review the median and the high end separately. The median describes a typical item; the high end often reveals exception handling, missing information, or approval bottlenecks.
Distinguish workload from performance
A full inbox is not necessarily evidence of weak performance. A high completion count can also conceal poor quality if requests return for correction. Interpret demand, speed, quality, and employee experience together.
For example, a team may reduce cycle time by closing requests quickly while increasing rework. Another team may report a lower volume because people stopped sending requests after prior delays. Add a short recurring pulse question such as "I can complete priority work within my normal schedule" and compare it with queue data. The pulse is a signal to investigate, not a substitute for operational data.
Where support and automation fit
Workload statistics can help a team decide whether to simplify, automate, redistribute, or add support. The sequence matters. First map the work. Then decide which tasks are repeatable, digitally executable, and covered by clear quality standards. Keep work involving confidential judgment, sensitive records, or executive decisions with an appropriate internal owner and approval path.
The O*NET update record shows that much of the occupation-specific task and work-context evidence was collected in 2017. It remains useful for role context, but it is not a real-time census of every administrative team. A local time study and queue record should govern a staffing decision.
If a team tests outside support or automation, measure the change against a baseline. Compare backlog age, rework, on-time completion, escalations, and the retained internal review time. Do not claim an improvement from a before-and-after comparison if a staffing change, leadership change, or seasonal demand shift happened at the same time.
Caveats
- O*NET's work-context percentages describe surveyed workers in one occupation. They do not establish an individual workload quota.
- BLS employment projections describe national employment. They do not forecast the capacity needs of a company or executive team.
- Request counts are only meaningful when the work categories and complexity are defined consistently.
- Public sources reviewed here do not establish that a virtual assistant, automation tool, or staffing ratio will produce a specific workload reduction.
Related reading
- EA administrative load statistics
- EA and VA workload distribution statistics
- How CEOs delegate effectively
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