When the major BI platforms were designed and priced, they had a specific type of customer in mind: organizations where a meaningful portion of the workforce was actively building reports, running queries, and working with data as a core part of their job. The pricing model — pay per seat, every user pays the same rate — made sense for that customer. If everyone uses the tool regularly, a flat per-seat fee is clean and predictable.
Most organizations don't look like that. They have an analytics team, a handful of operations staff, maybe a few power users in finance — and then a much larger group of people who need to see data, but who check in once a month, not every day. A regional manager who reviews sales figures before a monthly call. An executive who needs the board deck numbers. A department lead who pulls a specific metric during annual planning. These people get real value from having access to current, accurate data. They're just not using the tool the way a daily analyst does.
The per-seat pricing model treats both groups identically. Every provisioned account costs the same, whether it's used daily or quarterly. That's the mismatch this guide is about.
How BI Tools Were Actually Priced
The major BI platforms — Tableau, Power BI, Metabase — were built in an era when "BI user" meant someone with analytical training. The self-service BI wave of the 2010s broadened who could use these tools, but the pricing model largely didn't change to reflect the new population of users it was serving. Per-seat pricing remained the default because it was simple to understand, easy to bill, and financially attractive to vendors: every new employee who might conceivably need a dashboard is a billable seat.
The result is a pricing model that systematically overcharges organizations with uneven usage patterns — and most organizations have uneven usage patterns.
The 20–30% Reality
Industry research shows that in a typical enterprise BI deployment, only 20 to 30 percent of licensed seats see genuine activity in any given month. The majority of licensed users log in rarely, if at all. Some were provisioned during an initial rollout and never came back. Others check in occasionally but not on a cadence that would count as regular use.
This isn't a failure of adoption — it's a reflection of how data use is actually distributed across an organization. The people who need to look at numbers every day are always a minority. Most people need access to numbers periodically, on demand, when a specific question comes up.
Pull your own organization's login data for the last 90 days and you'll likely see the same pattern: a small, consistent core logging in regularly, a larger group logging in sporadically, and a tail of provisioned accounts that are rarely or never active. On a per-seat model, you're paying the same rate for all of them.
An occasional user isn't someone who failed to adopt the BI tool. They're someone whose job involves periodic data review rather than daily analysis. A manager who checks a dashboard before a monthly leadership meeting is getting real value from that access — the fact that they only log in once a month doesn't make them a failed user. It makes them an occasional user, and they should be priced accordingly.
The Financial Consequence
When only 20 to 30 percent of licensed seats are genuinely active each month, the cost per active user is three to five times the stated per-seat rate. An organization paying $14 per seat for Power BI with 100 licensed users and 25 active users each month is effectively paying $56 per active user — not $14. The math is straightforward once you look at it this way, and it gets worse as you provision more seats for people who won't use them regularly.
For the full mechanics of how per-seat and MAU pricing models compare, including worked examples at different team sizes, see the per-seat pricing deep dive in our BI pricing guide. This chapter is focused on the organizational dynamic rather than the math — but understanding the numbers matters, and that guide covers them in detail.
This Isn't a People Problem
Organizations that notice low active-user percentages often respond by trying to increase adoption: training programs, internal champions, gamified usage targets, management directives to use the tool. These efforts sometimes work at the margin, but they're addressing the symptom rather than the cause.
If 80 percent of your licensed users are occasional users — people who review data monthly rather than daily — then getting adoption to 100 percent would mean the occasional users are logging in more frequently, not that they were previously failing to use the tool correctly. The occasional usage pattern reflects their actual job requirements, not a training gap.
The real question isn't how to make occasional users behave like power users. It's whether your pricing model is appropriate for the distribution of usage you actually have.
What a Better Fit Looks Like
A pricing model designed for organizations with uneven usage charges based on actual activity rather than provisioned accounts. Monthly Active User (MAU) pricing — where the bill reflects the number of users who actually log in each month, not the number of accounts that exist — aligns cost with value. The small core of daily users is paid for every month. The occasional users are paid for in the months they're active, and not paid for when they're not.
This also changes how access decisions get made. On per-seat pricing, every new account is a cost decision — the budget question "do we need to buy another seat?" gates who gets access. On MAU pricing, provisioning an account for a manager who might check in quarterly doesn't cost anything until they actually log in. That structural difference has real consequences for how broadly data gets used across the organization — which is what the next chapter covers.
What percentage of employees typically use BI tools?
Industry research consistently shows that only 20–30% of licensed BI seats see genuine activity in any given month. The majority of licensed users — managers, executives, regional leads — check in occasionally rather than daily. This means organizations on per-seat pricing are typically paying for 70–80% of their licensed seats to sit idle every month.
Is low BI adoption a sign something is wrong?
Not necessarily. Low active-user percentages are often a normal reflection of how businesses use data — a small core of analysts and operations staff work in the tool daily, while a larger population of managers and executives reviews dashboards once a month or less. That's a usage pattern, not a failure. The problem isn't low adoption; it's paying per-seat rates for the entire population when only a fraction is active.
What is the difference between a power user and an occasional user in BI?
A power user builds reports, writes queries, configures dashboards, and logs in multiple times per week as part of their core job. An occasional user consumes existing dashboards — reviewing metrics for a monthly leadership meeting, checking a KPI before a client call, or pulling a specific number during a planning session. Both get real value from the tool. The difference is frequency and depth of engagement, not importance to the organization.
