Where AI projects actually break: handoffs
A practical look at why AI value disappears between teams, tools, and ownership boundaries.
AI systems rarely fail because the model was incapable.
They fail because work crosses boundaries:
- one team captures the input
- another team interprets it
- a third team owns the outcome
Every handoff creates delay, ambiguity, and loss of context.
The real bottleneck
Most organizations treat handoffs as administrative details. In practice, they are where operational knowledge gets dropped and where automation ROI gets diluted.
The question is not just "can this task be automated?"
The better question is "what upstream and downstream work must stay coherent for automation to matter?"
Why this matters for Dystil
Dystil is useful because it maps the surrounding workflow, not just the isolated task. That makes it possible to identify where AI creates actual leverage instead of local optimization.