Where 35.5 Hours Were Hiding: How the MRB Automation Actually Worked
Total effort on a recurring readiness brief fell from 35.5 hours to 13.5 per reporting cycle, and the brief's principal owners now carry 2.5 of those hours. The dashboard is the least interesting part. The interesting part is where the hours were hiding.
Where 35.5 hours goes
Nobody budgets 35.5 hours for a readiness brief. It accumulates in fragments across everyone who touches it: pulling maintenance data, reformatting it by hand, reconciling numbers that don't match between sources, rebuilding the same slides, and re-checking everything because manual consolidation invites manual error. Multiply that by every reporting cycle, and skilled maintenance Marines become part-time data clerks.
What the automation replaced
RIG's founder built the PowerBI Materiel Readiness Brief while serving as a battalion maintenance officer. It replaces the consolidation layer and leaves the judgment layer alone. Maintenance data flows into a prebuilt Power BI model and the brief assembles itself. Total effort drops to 13.5 hours, and the brief's principal owners spend their 2.5 of them on what the data means instead of where it goes on a slide.
Three design choices did the heavy lifting:
- Low barrier to entry. Usable with minimal Power BI proficiency, because adoption can't depend on a training pipeline that units don't have time for.
- Adaptability over prescription. Piloted with 2d LAR Battalion in November 2024, then tailored by 3rd Battalion, 2nd Marines. A tool that can't flex to a different unit's structure gets treated as a mandate, and mandates get worked around.
- A scaling roadmap. License upgrades, custom GCSS reports to align data across the hierarchy, and direct-query capability give it a defined path from pilot to enterprise.
The part that outlasts the dashboard
The 22 recovered hours are the headline. The lasting value is what recovering them required: standardized, structured, consistently defined readiness data. That is the real prerequisite for AI adoption. Units that automate their reporting today are building, whether they intend to or not, the clean data foundation that any future AI readiness tooling will need. The dashboard pays for itself now; the data discipline pays again later.