Assets were growing and so was the top line, yet the firm's margin kept slipping, because costs were climbing faster than the assets they supported. The usual answer, an across-the-board percentage cut, only bruised the parts of the business that actually made money and left the real waste untouched.
It is the paradox of the large firm: revenue rises steadily while the margin erodes, because operating costs quietly grow faster than the asset base. This asset manager had let budget creep spread across every department for years, and no one could say which of it was justified.
Leadership tried the obvious fix and asked everyone to cut a fixed percentage. It backfired. Flat cuts hit the investment and client-service capabilities the firm depended on just as hard as the genuine waste, and the executives could not reach agreement on where the money was really being lost, so the hard decisions kept being deferred.
Distribution was the clearest symptom. The global sales effort ran on old habits and burned a large share of the budget with no reliable way to measure the return on effort in one territory versus another, so spend kept flowing to places that no longer paid it back.
Score every cost by its value to the strategy, let leadership confirm the cuts, and reinvest the savings into distribution that can prove its return.
We built an analytics layer over the firm's entire expense base that scores every cost line by its value to the strategy, so operational fat that can safely be cut is separated from the growth muscle, the systems and people that win and keep clients, that has to be protected.
Instead of a blunt across-the-board percentage, the model produced a ranked, evidence-backed list of where to cut and by how much, which gave the executives a shared, defensible basis to align on and finally commit to real targets rather than argue in circles.
The engine proposes; it does not wield the knife. Partners and division heads review each recommendation and confirm every cut before it is made, so the judgement about what the firm can live without stays with the people accountable for the consequences.
We moved the freed budget out of wasteful back-office spend and into a data-driven distribution engine that ranks prospects and existing clients by propensity, so the sales force spends its time on the accounts most likely to bring in new money instead of working from instinct.
A dynamic pricing tool sets investment-fee proposals against a hard margin floor, so the firm can compete sharply for a mandate without quietly signing business that loses money, and no discount slips through that would undo the cost work upstream.
Leadership runs the whole programme from a live dashboard that tracks costs, margin and net new client money in real time, so the effect of every cut and every reinvestment is visible as it lands rather than discovered in a quarterly review months later.
The firm cut deep without cutting into the business, and turned distribution into a source of margin instead of a drain on it.
The firm hit a precise, agreed savings target of 16% of total operating costs, implemented in under two years. Because the cuts were ranked by value to the strategy and signed off by the people accountable for each area, the savings came out of genuine waste rather than out of the capabilities that drive returns.
Expensive, instinct-led selling gave way to a data-driven distribution model that lifted the pace of net new client money while protecting the margin on it. The firm came out of the programme financially sturdier and better able to hold its ground through a volatile market, with a leadership team aligned around one validated plan.
Flat cuts fail because they treat every cost as equal, and instinct-led selling fails because it cannot show its return. The value here is grounding both the cut and the reinvestment in the firm's own data, with leadership signing off on what goes and a margin floor guarding what comes in, so the decision is defensible rather than political. It is the same data-grounded discipline behind our process-mining work, aimed here at the strategic cut-and-reinvest decision and the revenue side rather than at automating the back office.
A finance corporation had to cut overhead without knowing where the work really went. Process mining mapped the true workflows, agentic AI took the routine back-office work, and a reporting model drafted the monthly P&L, cutting shared-services cost 30% and month-end close from 14 days to 4.
Read the case → Cost-to-serve & pricing · DistributionA B2B distributor's biggest accounts were quietly unprofitable. A cost-to-serve model plus dynamic pricing to a margin floor lifted margin 4.5% in two quarters, turned 80% of loss-making customers profitable, and cut discount arguments 50%.
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Reef TRH