๐๐ and ๐๐๐๐ผ๐บ๐ฎ๐๐ถ๐ผ๐ป ๐ถ๐ป ๐ฅ๐ฒ๐ฝ๐ผ๐ฟ๐๐ถ๐ป๐ด: ๐ง๐ต๐ฒ ๐ฅ๐ฒ๐ฎ๐น ๐ข๐ฝ๐ฝ๐ผ๐ฟ๐๐๐ป๐ถ๐๐ ๐๐๐ปโ๐ ๐๐ต๐ฒ ๐ฅ๐ฒ๐ฝ๐ผ๐ฟ๐
Automating the report sounds like the obvious win, but it usually misses the point. This post looks at whatโs really happening behind monthly reporting: chasing inputs, fixing data, and stitching together a story under time pressure. It shifts the focus from the report itself to the flow around itโwhere delays, inconsistencies, and rework actually sit. Thatโs where AI and automation start to make a dent. When the inputs are cleaner and the starting point is already there, reporting becomes refinement instead of a rebuild every month.
Dawn Thiart
4/16/20261 min read
A common place people look for AI and automation opportunities is reporting.
Makes sense. It happens every month. It feels repeatable.
So the assumption is: "Letโs automate the report"
But thatโs not where the real problem is.
๐ช๐ต๐ฎ๐ ๐๐ต๐ฒ ๐ฝ๐ฟ๐ผ๐ฐ๐ฒ๐๐ ๐น๐ผ๐ผ๐ธ๐ ๐น๐ถ๐ธ๐ฒ ๐ผ๐ป ๐๐ต๐ฒ ๐๐๐ฟ๐ณ๐ฎ๐ฐ๐ฒ
โPrepare monthly reportโ
Sounds simple, but itโs not.
๐ช๐ต๐ฎ๐โ๐ ๐ฎ๐ฐ๐๐๐ฎ๐น๐น๐ ๐ต๐ฎ๐ฝ๐ฝ๐ฒ๐ป๐ถ๐ป๐ด
When you sit with the team and ask the right questions, a different picture shows up:
โข We wait on multiple teams for inputs
โข Everyone sends data in different formats
โข We spend time reconciling numbers before we can even start
โข Then we rewrite the same commentary each month
At that point, itโs clear.
The report isnโt the problem.
๐ง๐ต๐ฒ ๐ฟ๐ฒ๐ฎ๐น ๐ถ๐๐๐๐ฒ
Itโs everything around it.
โข the back-and-forth to get inputs
โข the inconsistency in data
โข the time spent validating before anything can be used
โข the effort to turn numbers into a narrative
Thatโs where the time goes and where the frustration is.
๐ช๐ต๐ฒ๐ฟ๐ฒ ๐๐ ๐ฎ๐ฐ๐๐๐ฎ๐น๐น๐ ๐ต๐ฒ๐น๐ฝ๐
Not by โautomating the reportโ
But by fixing the flow around it:
โข Standardise and ingest inputs automatically
โข Flag inconsistencies early
โข Generate a first draft of commentary based on trends
โข Reduce the time spent getting to a starting point
Now the team isnโt starting from scratch every month theyโre starting from something usable.
๐ง๐ต๐ฒ ๐๐ต๐ถ๐ณ๐
Before:
โข reporting is slow
โข effort is front-loaded
โข quality depends on manual checks
After:
โข inputs are cleaner
โข issues are flagged early
โข reporting becomes refinement, not reconstruction
๐ช๐ต๐ฎ๐ ๐๐ต๐ถ๐ ๐๐ต๐ผ๐๐
This comes up a lot, people assume the opportunity is in the obvious task, but it rarely is. Itโs usually in the friction around it.
๐๐ถ๐ป๐ฎ๐น ๐๐ต๐ผ๐๐ด๐ต๐
If you look at reporting and think โautomationโ, youโre only seeing part of the picture.
Step back.
Look at the flow.
Thatโs where the real AI opportunity sits.