TL;DR (60 seconds):
Most insurance brokerages cannot tell you what their staff actually spend time on. Partners assume their senior brokers are mostly servicing clients and winning new business. The reality is different. According to , producers spend only 20% of their...
Most insurance brokerages cannot tell you what their staff actually spend time on. Partners assume their senior brokers are mostly servicing clients and winning new business. The reality is different.
According to Stella Insurance Automation research, producers spend only 20% of their time actually selling. The rest disappears into chasing renewals, fixing policy errors, hunting down certificates, and explaining why claims are taking so long. For a brokerage paying a senior broker R80,000 per month, that means R64,000 is going to administrative work that could often be handled by someone earning half as much.
Insurance brokerage billable utilisation tracking reveals exactly where this time goes. Not the theoretical time from job descriptions or the optimistic time from weekly check-ins. The actual time, logged against actual tasks, showing what clients you can bill for and what internal work is eating your margin.
We will walk through how a mid-sized brokerage starts tracking utilisation without creating a surveillance culture, what the first month of data typically reveals, and which patterns point to automation opportunities that pay back within six months. The method is straightforward. The findings are usually uncomfortable. The payback is measurable.
What has to be captured at source
The moment a broker opens a file, takes a call, or reviews a policy, someone has to write something down. Not later. Not at the end of the week. At that moment.
In most brokerages, this happens in three places: the client management system when logging activities, timesheets when recording billable work, and project files when tracking claim or renewal progress. The problem is that these three records rarely match, and none captures what you actually need to measure utilisation.
The minimum viable capture is two fields: client matter and time spent. That's it. Not task categories, not billing codes, not productivity ratings. According to research on billable hour tracking, the more fields you require, the less accurate the data becomes. Complex tracking systems fail because people stop using them.
The physical moment of capture differs by role. Account managers log time when they close a client file or end a call. Claims handlers record it when they update a claim status. Underwriters capture it when they finish reviewing a submission. The key is that it happens immediately, while the work is still fresh.
Most brokerages already have the systems to capture this data. The client management system logs activities. The phone system records call durations. Email platforms timestamp correspondence. The issue isn't technology, it's discipline and consistency.
If someone cannot write down what they worked on and for how long at the moment they finish the work, that time cannot be tracked. This eliminates informal conversations, quick emails checked on phones, and administrative tasks done between other work. Time tracking research shows this represents 15-25% of a typical workday in professional services.
The capture has to be mandatory and immediate. Optional tracking produces incomplete data. End-of-day logging produces inaccurate data. Weekly summaries produce useless data.
This creates the first constraint: you can only track utilisation for work that happens in discrete, identifiable blocks. Everything else becomes overhead, which still matters for understanding true productivity but cannot be allocated to specific clients or matters.
The smallest version that works
Start with a spreadsheet. No new system, no logins, no training sessions.
Create four columns: Date, Client, Hours, Activity Type. Each producer fills one row per client interaction. Daily. That is the entire system.
The mechanics work like this: producers log time at day-end, not real-time. They estimate hours to the nearest quarter-hour. Activity types stay broad: "Client meetings", "Proposal preparation", "Administration", "Internal meetings". Nothing more granular.
The install takes two weeks maximum. Week one introduces the spreadsheet. Week two addresses the inevitable gaps and clarifies what counts as billable versus administrative time.
This version answers exactly three questions: Which clients consume the most producer time? What percentage of each producer's week goes to billable work? How much non-billable time gets absorbed by administration?
According to research on insurance producer productivity, producers typically spend only 20% of their time on actual selling activities. The rest disappears into client service, internal processes, and administrative work. Your spreadsheet will show whether your brokerage sits above or below this baseline.
The mathematics stay simple. If a producer logs 35 hours weekly and 28 hours show as billable client work, their utilisation sits at 80%. If another producer shows 35 hours total but only 14 hours billable, their utilisation drops to 40%. The difference between these two producers costs your brokerage measurable revenue.
This version deliberately cannot answer deeper questions. It will not show which activities within "client meetings" drive revenue versus those that drain time. It cannot identify which administrative tasks could be eliminated or automated. It will not reveal seasonal patterns or track improvements over time with any precision.
The spreadsheet also cannot distinguish between high-value client work and low-margin service calls. A producer spending four hours with a R50,000 annual premium client shows the same utilisation as four hours spent chasing a R5,000 renewal.
But the spreadsheet will surface the cost of poor utilisation immediately. If your average producer bills at R800 per hour and utilisation sits at 50% instead of 75%, each producer costs you R7,000 weekly in lost capacity. For a five-producer team, that totals R1.8 million annually.
Start here. The spreadsheet reveals whether utilisation problems exist and their approximate scale. Only
Who touches it, and when
The programme owner is the practice manager or senior partner. Not the office manager, not a junior broker, not someone who rotates between tasks. One person who understands both the commercial pressure and the operational detail.
They run the collection cycle weekly. Monday mornings work best because brokers remember last week more clearly than last month. The owner sends a simple template: client name, hours spent, type of work. No categories beyond billable and non-billable at first.
Collection happens in fifteen-minute blocks, rounded up. Anything shorter gets lost in memory. Time tracking research shows that accuracy drops by 40% when people reconstruct their week from memory rather than daily records.
The practice manager reviews submissions by Wednesday. They check for obvious gaps: eight-hour days with four billable hours logged, or no time recorded against known client meetings. Missing entries get queried immediately, not at month-end when context has vanished.
Friday becomes the reconciliation day. Total hours per broker against their capacity, total client hours against expected revenue. The numbers get compiled into a simple weekly dashboard: utilisation by person, revenue per hour, hours that cannot be billed back.
The failure mode is predictable. Skip two collection cycles and brokers stop logging entirely. According to time tracking studies, compliance drops to under 30% within three weeks of irregular collection. The practice manager starts chasing individual submissions, brokers begin estimating entire weeks, and the data becomes worthless for commercial decisions.
Recovery from a lapsed routine takes six weeks minimum. Not because the mechanics are complex, but because trust in the process needs rebuilding. Brokers need to see that the numbers actually inform capacity decisions, fee negotiations, and hiring choices before they commit to accurate logging again.
The weekly cadence must hold for three months before utilisation patterns become reliable enough to base commercial decisions on.
The first thing it shows
The first cycle reveals something most brokerages do not expect: their senior producers are spending 40-50% of their time on administrative work that generates no revenue.
According to research on insurance producer productivity, producers typically spend only 20% of their time actually selling, with the remainder consumed by policy servicing, claims follow-up, and internal coordination. When brokerages start tracking billable utilisation, this split becomes visible in hard numbers for the first time.
The pattern shows up immediately because administrative tasks are constant and measurable. Every day, senior producers log time coding renewals, chasing underwriters for quotes, updating client records, and coordinating with accounts teams. These activities are necessary but not billable to clients, so they appear as utilisation gaps in the first week of tracking.
Why this surfaces first is straightforward: revenue-generating activities like new business meetings and client advisory sessions are intermittent and planned. Administrative work fills the spaces between. When you measure both against available hours, the administrative load dominates the data.
The financial implication becomes immediate. If a senior producer bills at R2,000 per hour but spends 20 hours weekly on administration, that represents R40,000 in foregone revenue weekly, or roughly R2 million annually per producer.
This finding contradicts the common assumption that utilisation problems stem from insufficient new business activity. The data shows existing business administration as the primary constraint on billable time. Time tracking research confirms that professional services firms consistently underestimate non-billable time until they measure it systematically.
The revelation is uncomfortable but actionable. Administrative tasks can be delegated, systematised, or eliminated more easily than new business can be generated. The brokerage can immediately identify which activities consume the most senior time and cost the most in opportunity terms.
One cycle of tracking typically captures this pattern because administrative work is consistent daily. Unlike seasonal fluctuations or irregular client demands, the admin load appears in every producer's time logs within the first measurement period, making it impossible to dismiss as coincidence or exception.
This is why we recommend starting with utilisation tracking before building complex solutions. The first problem worth solving reveals itself in the
When to graduate off the minimum
The spreadsheet stops working when you hit 15-20 people or when utilisation drops below your target for three consecutive months without an obvious cause.
At 15 people, the manual overhead of chasing timesheets, reconciling entries, and calculating weekly rates consumes roughly four hours per week. That's R2,000 in admin time monthly, assuming a R125 hourly rate for the person doing the work. More critically, the lag between work happening and seeing the utilisation number stretches to 10-14 days, making the data less useful for course correction.
The complexity threshold arrives earlier if you bill different rates by service type, client tier, or staff seniority. A simple "everyone bills R800 per hour" calculation works in a basic spreadsheet. Tracking six different rate categories across multiple clients requires more structure than Excel provides reliably.
According to research on agency time tracking, businesses lose an average of 2.9 hours per week to poor time tracking processes once they exceed 12 billable staff members.
Your next step depends on what broke the spreadsheet. If it's pure volume, dedicated time tracking software like Harvest or Toggl typically costs R150-300 per user monthly but eliminates the admin overhead entirely. The calculation: if you're spending four hours weekly on timesheet administration, software pays for itself at around 12 users.
If the issue is complexity rather than volume, you might need custom automation to handle your specific rate structures and client billing requirements. This makes sense when your billing logic is too specific for standard software but too error-prone for manual calculation.
The trigger for considering automation over software: when you're spending more than six hours monthly fixing timesheet errors or reconciling discrepancies, even with proper software in place. At that point, the manual intervention cost exceeds what custom automation typically costs to build and maintain.
What this does not fix
Tracking billable utilisation removes a blind spot. It does not remove the underlying constraint.
Your producers still spend only 20% of their time actively selling. The rest goes to chasing renewal documentation, following up on incomplete applications, and managing existing client queries. None of this changes when you start measuring it.
The operating bottleneck remains where it was. If your constraint is proposal turnaround time because underwriters are overloaded, better time tracking will not speed up underwriting. If new business stalls because compliance reviews take three weeks, measuring producer hours will not accelerate compliance.
What you gain is visibility into where billable time actually goes. Most brokerages discover their producers spend significant hours on work that could be handled by lower-cost staff or automated entirely. Time tracking reveals which activities drain productive capacity without generating revenue.
This creates the foundation for the next decision: whether to redesign the work allocation, hire different skills, or automate the non-billable tasks that consume producer time. The measurement itself changes nothing. It simply shows you what needs changing and whether the cost of changing it makes commercial sense.
The bottleneck stays. You just see it more clearly.
Next Steps
Tracking billable utilisation reveals where your brokerage's revenue capacity disappears into admin, chasing, and rework.
Start with one week of manual tracking using a simple spreadsheet. Record every 15-minute block: client work, admin, waiting for underwriters, chasing documents. According to Forge's time tracking methodology, this baseline shows you exactly where time goes before you optimise anything.
Look for three patterns in your data. First: which admin tasks consume more than 30 minutes daily per person. These are automation candidates. Second: how much time gets lost to rework because information wasn't captured properly the first time. Third: which clients or policy types consistently require more touches than others.
Your success criteria are concrete observations you can measure week-to-week: fewer emails chasing the same document, shorter turnaround times on renewals, less overtime during renewal season. If you're seeing these improvements, the tracking is working.
The numbers will show you whether the problem is worth solving and what fixing it could return. Most brokerages discover one clear bottleneck that costs them 10-15 hours weekly.
Ready to map your actual capacity? We run a free 20-minute diagnosis to identify your biggest time drain and calculate what fixing it could be worth. No audit, no transformation programme. Just the numbers that matter.
About AutoSpark
AutoSpark helps established small and mid-sized businesses find the one place AI or automation is genuinely worth applying, then builds and deploys it. The method is plain: interview the people doing the work, find where work repeatedly gets stuck, rank the problems by what they cost, and only build when the maths shows a clear payback.
AutoSpark is led by Patrick Nesbitt, a CA(SA), CFA and former private-equity investor, so AI is treated as an investment rather than a trend. Not an AI audit. Not a transformation programme. A short, evidence led diagnosis of where the money is leaking and what fixing it returns.
Start here: autospark.ai