The mandate was to cut overhead hard and rebuild the budget from zero. The problem: no one actually knew how long each clerical task took. Finance, payroll and procurement ran as a black box, while staff drowned in copy-paste between systems and exhausting manual month-end closes.
Most corporations chasing a cost advantage reach for across-the-board layoffs, and processes collapse. This one wanted to rebuild its budget from zero based on real need, but management had no idea which clerical tasks were genuinely complex and which were only slow because of internal bureaucracy.
So people did robot work. Finance and HR staff spent around 60% of their time on repetitive routines, keying data from one portal into another system, collecting spreadsheets from different departments, doing by hand what a machine should do.
And the close dragged. Monthly financial reporting took more than two weeks, demanded heavy overtime, and carried errors from manual journal entries, all of it invisible to the leaders who had to decide where the money went.
Map how the back office actually runs, hand the routine to agents, and draft the reports.
We put process-mining monitoring on the core systems, which captured millions of real actions and clicks. The AI mapped how work actually flows, exposed the bottlenecks, and revealed the workarounds staff used to cope with the old systems.
The map told management which processes are genuinely complex and which are only cumbersome because of internal red tape, so the cost-cutting could target the waste instead of the work.
Where the mining found repetitive work, we built autonomous AI agents that pull payroll data, read scanned invoices, match each invoice to its purchase order and receipt, and post journal entries on their own.
A reporting model trained on the firm's financial history pulls month-end data from every department and produces a precise first draft of the profit-and-loss statement, waiting only for the finance chief to review and approve.
Staff moved off data entry and onto control and analysis, so the 30% cost reduction came from removing robot work, not from cutting the people who do the judgement.
The transparent, accurate data let management rebuild the budget from zero on a true understanding of what the organisation needs, rather than on guesswork or internal politics.
Cost out, service intact, and a close that no longer eats the month.
Operating cost across the shared-services centres fell 30% with no drop in service, by shifting staff from data entry to control and analysis rather than by blunt cuts. The month-end close went from fourteen days to four, and the overtime in finance all but disappeared.
For the first time, management had a transparent, accurate picture of how the back office actually works, which let them apply zero-based budgeting from genuine need instead of guesswork, and defend every line.
Automating a process you haven't measured just makes the waste run faster. The value here is mining the real workflow first, so the agents are pointed at the work that is genuinely routine, and keeping a person on the report and the judgement. It extends the agentic, human-on-the-exception discipline behind our agentic-automation work to the finance back office, grounded in what the process actually does.
A chatbot could talk but not act. Agentic AI wired through secure APIs to the core systems, with guardrails and human escalation, lifted no-touch self-service resolution from 15% to 65% and cut contact-centre cost ~30%.
Read the case → Bookkeeping automation · AccountingAn accounting firm's team burned days keying invoices and reconciling ledgers. AI document capture plus a learning categorisation engine cut month-end close 40% and manual entry 80%.
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