Analytics had become central to the investment process, and per user Power BI licensing had grown faster than anyone was tracking. The estate was paying premium per user fees for a population whose real need was viewing, not authoring. This is how a capacity model sized to consumption reset the run rate.
A global investment management firm where Power BI had become embedded across portfolio analytics, risk reporting, and client reporting. Licensing had scaled through per user assignments, with a large share of seats on premium per user tiers that the firm had defaulted to as adoption spread. The mix no longer matched how people actually used the platform. The practice was engaged to restructure the analytics estate around real consumption.
Power BI licensing punishes organizations that scale by default. The per user model is simple to start with, every person who needs the platform gets a license, but it becomes expensive at scale and especially so when the premium per user tier is treated as the standard assignment. In an asset manager, where analytics had moved from a back office function to a core part of how portfolios and risk were understood, adoption had spread across hundreds of professionals, and each new user had been handed a premium seat as a matter of routine.
The result was a run rate that had climbed quietly past $3M without anyone making a deliberate decision to spend that much on analytics licensing. The growth was the sum of many small, reasonable assignments, none of which triggered review. Critically, the assignment did not match the usage. A large portion of the population consumed reports and dashboards built by a much smaller group of authors, yet the consumers and the authors held the same premium per user license, with the firm paying author level pricing for what was, for most people, a viewing relationship.
The firm sensed it was overpaying but lacked a usage based view to act on. The licensing reflected a count of people who touched Power BI, not a model of how they used it, and those are very different things to pay for.
The practice profiled real usage across the analytics estate, separating the population into genuine authors who built and published content, power users who interacted heavily with it, and the larger group who primarily consumed finished reports. That profile was the foundation for everything that followed, because it converted an undifferentiated headcount into a usage model the licensing could be matched to.
From that model the practice designed a capacity based structure. Rather than paying premium per user fees across the whole population, the firm moved its consumption heavy workloads onto a dedicated capacity sized to actual demand, which let the broad audience access content without each person carrying a premium per user license. The smaller author and power user population retained the per user licensing they genuinely needed. The capacity was sized to the real consumption pattern rather than a worst case peak, which is where capacity models usually leak money, with headroom for growth built in deliberately rather than by overprovisioning.
The restructure was timed to the firm's agreement cycle so the new model was locked in at favorable terms rather than bolted on mid term. A capacity model only saves money when it is sized to consumption. Size it to headcount and it simply moves the overspend to a different line.
The analytics run rate fell from $3.1M to $2.05M, a thirty four percent annual reduction delivered inside ten weeks. The savings came from matching the licensing model to the usage model: a capacity sized to real consumption served the broad audience, while per user licensing was retained only for the authors and power users who needed it. Because the structure reflected genuine usage rather than a one time concession, the saving recurs every year rather than eroding at the next renewal.
The structural value extended past the number. The capacity model gave the firm a single, governable analytics platform with room to grow built in, so expanding adoption no longer meant linear growth in premium per user fees. The usage profile became a standing asset the firm could refresh, keeping the licensing matched to consumption as the analytics estate evolved rather than drifting back into per user sprawl.
The engagement reflects the firm's broader record across Microsoft contracts: more than $420M in cumulative client savings, over 340 engagements delivered, and an average 79 percent reduction in audit financial exposure, built on 20+ years of combined practice depth across the Microsoft estate. The figures above are verifiable on a reference call arranged through the practice.
The practice supports enterprises on restructuring Power BI and Power Platform estates around real consumption, sizing capacity correctly, and locking the model in at renewal. Two analyst calls, no pitch, and an honest read on where the overspend sits.