Where the hidden revenue was hiding.
A $220M, five-EIN staffing group recovered $957,000 of annual hard-dollar cost and built the AI-ready operating foundation underneath it. Payback in four months. 30% EBITDA expansion - on the operation they already had.
What seven months of disciplined sequencing produced.
From flying blind to real-time operating intelligence.
One dashboard. Role-based views. Anchored on a single North Star metric. This is what the operation looks like seven months after the first decision.
Three chapters. One sequencing discipline.
Five EINs. One shared operating problem.
The Group operates five corporations under a shared services holding structure built to pool back-office cost across the operating companies. Each corporation runs its own brand, sales motion, and EIN. Corporate handles payroll, finance, IT, compliance, and accounting as a centralized capability. The model has worked for more than a decade. What changed was the cost of running it on the systems that got the Group to its current scale.
4,500+ weekly W-2 workers. Multiple weekly pay runs - some entities running back-to-back batches every week to meet client-specific timing. Branch staff absorbing 30-40% of their selling capacity into payroll prep. No real-time operational visibility anywhere in the system. By the time the executive team began the diagnostic, the chain had been running for over a decade.
Standardize where the data is captured. Everything downstream follows.
The transformation could have started in any number of places - back-office payroll engine, ATS replacement, ERP modernization, AI agents at the top. Each option had vendors lined up to sell it. The Group's leadership made a deliberately tactical choice: begin with the upstream - the one place a single change would propagate across the entire operation.
Before the decision, time was captured on paper at one branch, on tablets at another, on iPads in a third location, and in branch spreadsheets at the rest. None of it validated at source. All of it re-keyed downstream into the back-office stack. Every upstream gap multiplied into every downstream cost.
With standardized native time clocks deployed at the largest operating company first, then replicated across the remaining four EINs in sequence, the Group rebuilt the foundation in three months. One clean upstream data layer now feeds three downstream workflows simultaneously: branch operations get real-time visibility, the back office gets validated data ready to process, and client workflows get faster, more accurate billing and reporting.
- Native time clocks at branch entry · validation at source · zero re-keying
- Branch staff returned to revenue work · payroll prep under 10% of the week
- Timesheet exception rate from above 8% to 2.1% in eight weeks
- Workers per back-office FTE from ~280 to 1,094 in seven months
- DSO from Day 5-7 invoicing to Day 1 invoicing on validated data
Why do anything? Because the cost of doing nothing was higher.
The discovery work produced a bottoms-up business case the executive team could defend, line by line. Three layers, each independently calculated, each cross-checked against the Group's own operational data.
Beyond hard dollars, the diagnostic put a number on every period of continued deferral. $80K every month. $240K every quarter. $958K every year. At mid-market staffing industry multiples of 5-7x EBITDA, the recurring annual contribution translates to $15-28M of enterprise value the platform creates every year it operates. Each year of deferral had been a year of forgone enterprise-value compounding.
14 pages. The diagnostic, the math, and the sequencing.
This page is the summary. The full case study is the operating manual - the line-by-line diagnostic, the bottoms-up business case, the cost-of-inaction math, and the AI-readiness sequencing that turned a legacy operation into a 30% EBITDA expansion in seven months.
- The eight cascading problems · how each one fed the next
- The diagnostic table · the Group vs. traditional staffing vs. AI-native operations
- North Star metric selection · why Revenue per Recruiter
- The three-phase rollout · 7 months from first decision to AI-readiness
- Cost of inaction by month, quarter, year, and 3-year cycle
- The five-layer AI-readiness stack · why foundation precedes deployment
- Eight before-and-after categories with committed targets
- The four selection factors that decided the platform choice
Where the hidden revenue was hiding.
A $220M, five-EIN staffing group recovered $957,000 of annual hard-dollar cost and built the AI-ready operating foundation underneath it.
Get the 14-page case study.
The diagnostic, the math, the sequencing. The same blueprint that produced $957K of annual savings and a 4-month payback at one of the most operationally complex staffing groups in the Midwest.
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