When skilled people compensate for broken systems
A process can look like it works when capable people are quietly holding it together. I mapped the post-sales consumption motion to find where the operating model was relying on people to compensate for system gaps.
Consumption changes what "success after close" means. Once a customer buys a usage-based product, the job isn't finished: someone has to understand whether adoption is progressing, whether usage is stalling, and when intervention is needed.
The problem was that there was no clear picture of how that work actually happened across teams. I set out to map the real operating motion: the tools, handoffs, workarounds and points where intervention depended on individual effort rather than system support.
Where does the post-sales consumption workflow rely on people compensating for gaps the system should handle?
In a consumption model, delayed visibility is itself a business risk. If teams only detect under-consumption when someone manually spots a trend, a customer raises the issue, or renewal is approaching, intervention can be delayed.
The goal wasn't simply to make tracking easier. It was to understand what would make the motion systematic rather than heroic.
I ran qualitative discovery with the people managing consumption hands-on, using screen-shared sessions to follow the actual tools, decisions and workarounds rather than an idealized process. A consistent pattern emerged. I mapped the breaks across the workflow and translated the diagnosis into 12 candidate use cases across three priority tiers.
Every interview surfaced tool fragmentation, but the deeper pattern was operational: teams had learned to compensate. They moved between systems, kept spreadsheets, manually watched trends and improvised remediation when accounts stalled. Human capability was masking system weakness, which made the motion look more functional than the underlying system really was.
A process that works only because good people compensate for it is not a scalable operating model.
The answer wasn't one feature. The evidence pointed to a sequence: visibility and ownership first, then intelligence, then the supporting capabilities around them:
One of the Priority-1 recommendations subsequently shipped as a native lifecycle-tracking object. The research was one contributing input among several shaping that direction, and it addressed part of the spreadsheet-based tracking problem identified in the work.
The broader roadmap extended beyond native tracking to proactive alerting, cross-system visibility, historical trends and standardized remediation, reflecting that shipping one object addresses only part of the operating-model challenge.
The goal is to make the operating model less dependent on manual vigilance. I'd look for: