Workload Visibility Before Hiring
Revenue-generating activity represented only 18% of team hours — revealing both automation priorities and role redesign decisions simultaneously.
The challenge
The team felt stretched but couldn't isolate where the drag was coming from — and the bonus model was rewarding effort rather than outcome.
A bottom-up workload analysis across nine function categories estimated total monthly team load at approximately 610 hours — the equivalent of 3.8 full-time employees — against a team of three people operating at roughly three FTEs of capacity. The mismatch was being absorbed through overwork and reactive task-switching rather than acknowledged and addressed structurally. Property sourcing alone consumed 150 hours per month (0.94 FTE); post-letting issue management consumed a further 130 hours (0.8 FTE). Together those two functions — neither of which directly generates revenue — accounted for 280 hours, or 1.75 FTEs per month. Revenue-driving activity (sales, business development, closing) represented only 110 hours, approximately 18% of total team time. The bonus structure compounded the problem: it was not connected to this distribution, meaning effort in low-revenue functions was implicitly rewarded at the same rate as effort in high-revenue ones. Role overlap — multiple people working on the same sourcing or presentation task without clear ownership — was documented in three separate adviser interviews as a 'natural' pattern, which is the accurate description of what happens when accountability is never made explicit.
The system
Decision system built
We ran structured 1:1 interviews with all three core team members, using a consistent protocol covering role definition, time distribution, bottleneck identification, quality failure patterns, and growth scenario stress-testing. Interview transcripts were synthesised into individual reports and then aggregated into a workload model. Function categories were defined bottom-up from the actual tasks described — not from job titles. FTE equivalents were calculated against a 160-hour monthly baseline. The bonus structure was audited separately against the role breakdown and a revised framework was designed that tied variable pay to measurable contribution by function, with clear ownership of each revenue category.
System components
Nine-category bottom-up workload model with FTE equivalents and revenue-generation classification per category
Structured 1:1 interview protocol with synthesis into individual role-clarity and bottleneck reports
Role-overlap and double-work mapping: identifying tasks performed by multiple people without defined ownership
Revenue vs support work distribution analysis (18% revenue-driving vs 82% operational and support)
Revised bonus framework tied to measurable per-function contribution rather than undifferentiated effort
How we worked
Engagement scope
Structured interviews with all three core team members, workload modelling, role-clarity documentation, bonus structure audit and redesign, and growth scenario planning.
Timeline
Three-week diagnostic with a one-week structured output session covering the workload model, role documents, and bonus framework.
Operating model
All interviews conducted individually to ensure candid responses. Findings were triangulated across accounts before being presented — not attributed to individuals in the final output. The workload model was built with the team, not presented to them, to ensure the numbers reflected operational reality rather than our assumptions about it.
Outcomes
Business impact & measurable results
Revenue-generating activity represented only 18% of team hours — revealing both automation priorities and role redesign decisions simultaneously.
Quantified that post-letting issue management and property sourcing — both operational support functions — were consuming 1.75 FTEs of capacity per month, making the case for targeted automation and a dedicated rental care role unambiguous
Role clarity redesign: each of the three core team members received a documented primary responsibility set with explicit handoff points, reducing the reactive 'whoever is free handles it' dynamic that had been generating double work
Bonus structure rebuilt around four measurable contribution categories, eliminating ambiguity about what performance actually meant in variable compensation terms
Lead qualification gap identified: 1 in 10 inbound leads was progressing to close — not a volume problem but a qualification mechanism problem — flagged as the highest-ROI fix for the sales function
Growth scenario modelling: defined the specific workload thresholds at which a fifth team member becomes necessary and what that hire's function should be, avoiding a premature or mistargeted hiring decision
Governance
Trust, collaboration & governance
Individual interview findings were never attributed by name in shared outputs — what people said stayed in the analysis, not in the room
The bonus redesign was developed collaboratively with the founding team, with the logic explained so it could be maintained and updated without external support
Growth scenario outputs were deliberately framed as thresholds and triggers rather than recommendations — the decision on when and who to hire remains with the business
Reframe
Most overstretched teams are misallocated, not understaffed. Answering where capacity goes reveals automation priorities and hiring specs at once.
Across every engagement, the goal is the same: engineer a system that makes better decisions — faster, more consistently, and at scale — than the process it replaces.
Start a discovery
Most engagements begin with a conversation about context.
We do not send a proposal before we understand the problem. Start by telling us about your decision context — we will identify the highest-leverage intervention areas before any scope is agreed.