Series
AI content spend
Trace model cost, review time, rework, and the assets that never make it to publish.
8 articles
Reading list
In this series
- 01
· Cost · 9 min
Your AI content program is paying people to clean up cheap drafts
The model bill is the smallest line in most AI content budgets. Measure rejected drafts, review time, rework, coordination, and accepted output before calling the work cheaper.
- 02
· Cost · 7 min
One API key cannot explain a marketing budget
A shared AI key can produce a correct invoice and an unusable marketing budget. Tie each request to a process, task, asset, owner, and approval outcome before reconciling spend.
- 03
· Cost · 5 min
Cost per token is not the cost of a finished asset
A practical way to measure AI-assisted content by accepted assets, review time, rework, and launch delay instead of provider usage alone.
- 04
· Cost · 9 min
Your AI budget is wrong before finance sees the invoice
A budget built from provider dollars misses retries, review, agency work, and unapproved tasks. Set limits by process, forecast from accepted output, and name the action behind every threshold.
- 05
· Cost · 4 min
What to ask when AI content spend is over budget
A practical monthly review for explaining AI-assisted marketing variance by volume, mix, rework, review, and attribution.
- 06
· Cost · 9 min
The review queue is where your AI budget goes to die
AI can make a draft fast and still make the campaign expensive. Measure active review, handoffs, rework, waiting time, and the reasons assets fail.
- 07
· Cost · 6 min
The content you paid for but never published
AI makes drafts cheap enough to ignore. That is how marketing teams pay for research, review, and rework on assets that never reach a customer. Measure the waste before producing more.
- 08
· Cost · 6 min
AI saved us time. Why did the marketing budget grow?
Time saved is not the same as money saved. Build an AI marketing business case that separates capacity, cash savings, output, and risk reduction, then prove which one happened.