Methodology behind the benchmark.
The honest starting point: this benchmark has not been run. The field period opens in Q2 2027 and the report ships in Q3 2027, so there is no data set to describe yet, only a method we are committing to in advance so a reader can judge it before the numbers exist.
When it runs, the source data will come from two places. First, a structured survey of agencies in the band who opt into the panel, aggregated with identifying details removed. Second, post-handoff measurements from ColabContent commissions where the operator has consented to anonymized benchmarking. It will not be a roll-up (a group of businesses bought and combined by one owner) of public earnings filings, not a re-publication of a third-party industry report, and not an extrapolation from a single named engagement. Where a segment's sample is too thin to report, we will say so and leave the cell empty rather than fill it with an estimate.
The dimensions we will benchmark are the ones that come up most frequently as the constraint in an audit call: workflow velocity, capacity-per-senior-headcount, response-time distribution, and revenue-leakage from operational friction. We chose these dimensions because they are the ones an operator can act on with a commissioned AI build.
How to read your operator's position in the benchmark.
When the report ships, it will split responding agencies into quartiles on each dimension, so an operator can see where their workflow stands relative to other agencies in the band rather than relative to a theoretical optimum. Until the field period closes there is nothing to read: any P&C quartile, percentile, or peer average attributed to this benchmark before Q3 2027 did not come from us.
The comparison we expect to be most useful is the operator's own position against the top quartile on the dimension that is their known constraint. That delta, expressed in dollars or hours, is the upside a commissioned AI build would be asked to close. The benchmark cannot produce that figure for anyone yet. An audit call can produce a rough version of it today, using the operator's own numbers rather than a peer panel.
What the benchmark does not say.
It will not say that every agency should be in the top quartile on every dimension. Some dimensions are not worth optimizing for a specific operator's business model. An agency whose book is mostly complex commercial lines cannot and should not chase the same quote-turnaround number as a personal-lines shop. The benchmark is a yardstick, not a prescription.
It will also not say that AI is the right intervention for closing any specific gap. Some gaps close better with process redesign, some with staffing changes, some with stack changes. We will tell the operator on an audit call when the right answer is not AI.
And it will not carry a result we did not measure. If the Q2 2027 field period does not reach a usable sample, this page will say the study was not completed rather than publish thin numbers dressed as findings.
How the benchmark feeds into an audit call.
Once the report ships, operators will be able to bring it to an audit call and walk through which dimensions they sit high on, which they sit low on, and which of the low ones is worth commissioning a custom AI build to close. Until then the call runs off the operator's own measurements instead of a peer panel, which is slower to contextualize but no less concrete. Either way the audit call ends with the constraint written down in a sentence.
Where to look next.
The reports hub indexes the benchmarks across the five verticals the practice covers, and marks which are fielded and which are still scoped. The best-by-vertical guides rank the AI consultants and platforms relevant to each vertical. The resources section holds the decision frameworks that the benchmark is meant to feed into.
