Why Some AI Workflows Stick—And Others Don’t

The author explores why certain AI workflows become integral to daily work while others are abandoned, noting that successful workflows offer immediate rewards and solve genuine problems. Many AI tools fail because they are built for an idealized version of the user or address problems that don’t actually exist, leading to abandonment. Workflows that stick, like a compound writing plugin, provide immediate value and support existing work, making them more sustainable than those offering only distant promises.

• Many AI workflows are abandoned because they are built for an idealized user or solve non-existent problems.
• Successful AI workflows provide immediate rewards and solve real, existing problems for the user.
• Habit formation for AI workflows takes longer than the commonly cited 21 days, with medians around two months.
• Symbolic self-completion explains the tendency to adopt tools that represent an aspirational identity rather than current reality.
• Workflows that require excessive maintenance or fail to solve recurring problems are often discarded.
• The author developed a system (Agent Ops) to audit AI systems, categorizing failures and setting rules for automation adoption.
• Key criteria for keeping an AI workflow include automatic running, minimal output review time, and addressing recurring problems.
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