Model
What actually moves when AI runs the work
Marketing efficiency is not a feeling. Three numbers tell you whether an AI adoption strategy is working: how long a brief takes, what an asset costs and how much the same team ships.
Dra i reglaget. Siffrorna är indikativa och bygger på skiften jag själv har drivit in-house.
Drag: manual → AI-run
Manual
Time to market
6.0 wks
Cost per asset
100
Output volume
1.0×
Same team, same brand standard. What changes is how much of the workflow the system carries. Indicative model based on the shifts I have run in-house.
The three numbers worth tracking
- Time to market
- Calendar days from approved brief to live asset. Waiting time, not working time, is where most weeks disappear.
- Cost per asset
- Total production spend divided by usable outputs. Marketing workflow automation moves this faster than headcount ever will.
- Output volume
- Usable assets per month with the same team. This is the number that proves capacity, not effort.
How to build your own baseline
01
Measure before you automate
Take one month of real briefs and log days, spend and usable outputs. Without a baseline, no ROI claim survives scrutiny.
02
Automate the waiting, not the craft
Intake, versioning, resizing, routing and reporting first. Idea and judgement stay human.
03
Re-measure after one quarter
Same three numbers, same method. The delta is your actual return, not a vendor estimate.
04
Reinvest the recovered time
Efficiency only pays if the freed hours go into strategy, testing and better work.
If you cannot name your cost per asset today, that is the first thing worth fixing.
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