When DIY stops making sense

What this solves

DIY can be excellent for proving demand, but it breaks once workflow quality, risk, or integration complexity rises.

• The workflow affects revenue, trust, or compliance.
• Multiple tools and owners are involved.
• Manual review is still happening but without clear design.

What AI is good at here

• Helping prove the concept before formalizing it.

What AI should not own

• Once risk or complexity climbs, human judgment needs better system design around it.

Best first implementation

• Use DIY to validate the shape of the opportunity, then decide whether the workflow justifies an analysis or formal design step.

What good looks like

• You stop using duct-tape automation for core workflows.
• The next investment is made because the case is clear, not because the experiment is fun.

Common mistakes

• Scaling a rough prototype into a production workflow.
• Mistaking a prompt test for an implementation plan.