The output is different.
An automation agency often ships a workflow. A deployment partner is responsible for whether that workflow survives real use.
01
Automation focuses on the build
This can be enough for simple tasks. But AI workflows often need more than triggers and actions.
Tool setup
Workflow logic
Basic testing
Handoff
02
Deployment focuses on adoption
Deployment includes the work around the build: owners, training, exceptions, measurement, and maintenance.
Workflow owner
Review loop
Team training
Success metric
03
Choose based on risk
The more judgment, data sensitivity, and team change involved, the more you need deployment support.
Low risk: automate
Medium risk: sprint
High change: pod
Complex ops: custom build
Questions to ask before the first sprint
Will the workflow need human review?
Who supports it after launch?
What happens when the input is messy?
Keep reading on Fabren
External references
Next step
Need a workflow your team will actually use?
Fabren designs, builds, launches, and improves AI workflows with your team.
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