In brief
- 01
Volume 2 will track 6 measures of how owner-led companies run, and none of them has a result yet.
- 02
In US professional services, companies with 20 to 499 staff outnumber those with 500 or more by about 14 to 1, yet the research owners usually read describes large companies.1
- 03
You can count 2 of the 6 measures in your own company this month, with a 2-hour review of your tools and a 5-day tally of retyping.
State of Operations 2027 is in progress, and this page contains no results. It sets out what the volume will measure and how: how many tools owner-led companies pay for, how many hours a week go on retyping information between them, how much of the owner’s week goes on administration and where work stalls between people. The data will come from anonymized Verticulate diagnostics, and the same definitions will be used every year so companies can compare themselves with peers and with the year before.
The studies owners read describe much larger companies
Owner-led companies with 20 to 250 staff are numerous. In US professional, scientific and technical services alone, companies with 20 to 499 staff outnumber those with 500 or more by about 14 to 1.1
The research these companies read is mostly about the large ones. Bain’s widely cited estimate of where software development time goes, for example, draws on surveys of developer teams of about 2,000 to 20,000 people.2 A single one of those developer teams is larger than an entire company with 20 to 250 staff.
Size changes the answers. In Capterra’s survey of US software buyers, 51% of buyers at companies with 5 to 249 staff regretted a recent purchase, against 66% to 67% at larger companies.3 Owner-led companies have fewer layers of management, and more of the operation is held in the owner’s head. With little data on how they actually work, owners fall back on vendor surveys and anecdotes.
Volume 1 of this series, What business owners ask before buying operations help, described what owners worry about before they buy. Volume 2 will measure what their operations look like underneath.
Six measures, each defined so a company can count it
- Tools per company. The number of paid software tools, and the share used by fewer than half of their paid seats.
- Hours of re-entry per week. Time spent typing information that already exists in another tool.
- Owner time on administration. The share of the owner’s working week spent moving, checking or chasing work, instead of selling, leading or doing client work.
- Handoffs per job. The number of times a typical job changes hands, and the share of those handoffs with no named owner.
- Days from sale to start. The elapsed time from a signed agreement to the first piece of delivered work.
- Conflicting systems of record. The kinds of information, such as job status, that more than one tool claims to hold.
Four of the six can be counted directly from a map, an inventory or a pair of dates. The other two rest on estimates by the owner and the team, and the volume will report them as ranges instead of false precision.
| Measure | Taken from | Reported as |
|---|---|---|
| Tools per company | Tool inventory | Count |
| Hours of re-entry a week | Staff estimates | Range |
| Owner time on admin | Owner's estimate | Range |
| Handoffs per job | Operating map | Count |
| Days from sale to start | Signing and delivery dates | Count |
| Conflicting records | Tool inventory and map | Count |
Source: Verticulate research plan for Volume 2; no results yet
The data will come from diagnostics clients agree to share
Every Verticulate diagnostic produces a process map, notes from the mapping calls and an inventory of the tools in use. With each client’s permission, we will extract the six measures, remove anything that identifies the company and pool the results.
The plan for the sample and the period is:
- Who is in it. Owner-led companies that complete a Verticulate diagnostic and agree to share their anonymized measures. Companies that chose to work with an operations consultancy are not a random sample of companies, and the volume will say so on its first page.
- When the data is from. The volume will state the window of diagnostics it covers, so that later volumes can be compared with it.
- How large it must be. The volume will state its sample size on the first page. Breakdowns by industry or size band will appear only where a group is large enough to protect anonymity and support a conclusion.
Data collection is under way. None of the six measures has been reported, and this page shows no partial results.
Medians stop one unusual company from setting the number
Small samples are easy to distort. A single company with an unusual tool count can move an average a long way while the typical company has not changed. The volume will therefore report medians and ranges, not averages.
- Average
- Median
| Companies included | Average | Median |
|---|---|---|
| 8 typical companies | 11.1 | 11.5 |
| Add 1 company with 60 tools | 16.6 | 12 |
Source: Verticulate illustration
Note: Tool counts for the eight typical companies: 8, 9, 10, 11, 12, 12, 13 and 14.
The definitions will stay fixed from year to year. If a measure proves unreliable in practice, we will drop it and explain why, instead of quietly changing what it counts.
The volume will take no sponsorship and publish nothing it cannot trace
The volume will carry no vendor sponsorship. It will not publish a figure that cannot be traced to its source, and it will not present a self-reported estimate as a measurement.
You can count two of these measures yourself this month
Two of the six measures need no outside help. To count your tools and the share that go unused, run the keep, connect or replace review, which takes about two hours. To estimate hours of re-entry, ask your team to tally a week of retyping, as described in issue 2 of our newsletter.
A test you can run
If your volume doubled next quarter, how many more hours of retyping would you pay for?
Count the tools you pay for and the share with fewer than half their seats in use. Then ask everyone on the team to tally, for five working days, the minutes they spend typing information that already exists in another tool. Double the weekly total for the doubled volume. Keep both numbers. When this volume is published, you can compare them with the medians and ranges for companies like yours.
Where this is the wrong answer
If you already know which step breaks, do not wait for a benchmark to fix it. Peer comparisons help you decide where to look; they cannot make the case for a fix on their own. A company with fewer than 20 staff also sits outside the population this volume will describe, so the volume's medians may not apply to it.
The volume publishes only when the sample can support it
We will publish when the sample is large enough to report honestly, and the first page will state the sample size, the period and the limits. Until then, this page describes a plan, and nothing on it is a finding.
Sources
- 1
Statistics of U.S. Businesses, 2022: firms by enterprise employment size. US Census Bureau, 2025.
Sample: All US employer firms in 2022, released April 2025; figures here are for professional, scientific and technical services (NAICS 54)
StrongA full count of US employer firms. It shows how many firms of each size exist, not how they operate, and its size bands (20 to 99, 100 to 499) do not match the 20 to 250 range exactly.
- 2
From Pilots to Payoff: Generative AI in Software Development (Technology Report 2025). Bain & Company, 2025.
Sample: Bain's industry experience, based on surveys of software-as-a-service developer teams of about 2,000 to 20,000 full-time staff
DirectionalA consultancy estimate without a published method. Cited here as an example of the population such studies describe, not for its figures.
- 3
2024 U.S. Tech Trends Report: 58% of Businesses Regret a Recent Software Purchase. Capterra (Brian Westfall), 2023.
Sample: 700 US respondents involved in software purchasing at companies with 5 or more employees, surveyed online in July 2023
DirectionalSelf-reported regret in a survey run by a software marketplace, across all industries. It shows that results differ by company size; it cannot size any measure in this volume.
- Strong
- Large or audited sample, published method, and a population close to owner-led companies.
- Directional
- Survey, vendor or expert estimate. Trust the direction; treat the exact number as soft.



