The situation
A global medical device company had the feeling most operating teams recognize. Costs were higher than they should be, everyone agreed on that, and nobody could point at the line where the money was going.
The candidates were everywhere. Travel and expense data nobody monitored in anything like real time. Report generation that took long enough to make routine questions expensive to ask. The suspicion was well founded. The evidence was scattered across systems that did not talk to each other.
What was in the way
Monitoring was manual, which meant it happened at whatever cadence someone could find time for. The data that did surface was inconsistent and opaque enough that two people could pull the same question and get different answers.
The company had no internal AI capability to change that. Nobody on staff knew how to filter data at that scale or automate the workflows eating the hours, and the case for hiring for it could not be made before the value was proven.
Where it stands
We picked one area of maximum impact and built a proof of concept there rather than proposing a program. Proving the mechanism in one place is what makes the rest of the roadmap fundable.
Alongside it we built a demo dashboard for the COO that brings data from across the enterprise into one place, so questions that used to wait for a reporting cycle can be asked directly. The remaining opportunities are ranked by bottom-line impact, so the company has a sequence rather than a wish list.
The work is still under way. The full case study, with results, will follow once it is complete.
Full case study coming soon.
Efficient filtering of large, inconsistent data is what turns a cost suspicion into a cost decision.
From the client
It's obvious Mind Mechanics and their partners know what they are talking about. Building AI agents through them will save us millions of dollars.