Failure is usually operational.
SMB AI projects rarely fail because the model was not clever enough. They fail because nobody designed the workflow around real people and real constraints.
01
No owner
If everyone likes the idea but nobody owns the output, the project will drift.
Decision owner
Workflow owner
Review owner
Maintenance owner
02
Bad data access
AI needs the right context. If the data is scattered or inaccessible, the system will frustrate the team.
Source systems
Permissions
Data quality
Missing context
03
No adoption loop
A launch is not adoption. Teams need training, feedback, measurement, and a reason to change the habit.
Training
Usage metric
Feedback
Iteration
Questions to ask before the first sprint
Who owns the workflow?
What data does it need?
What proves people are using it?
Keep reading on Fabren
External references
Next step
Start with a workflow that can actually land.
Fabren's audit finds the gaps before you spend money building the wrong thing.
Avoid the trap