Jan 23 • General discussion
What’s the hidden cost people underestimate in AI projects?
Something I’m starting to notice:
Most AI projects don’t fail because of the tech — they fail because of a hidden cost that shows up later.
From your experience, what’s the most underestimated cost when building or deploying AI automations / agents?
Could be:
- Maintenance & edge cases
- Monitoring & incident response
- Client education / trust
- Prompt drift or model changes
- Over-engineering too early
Curious what ended up costing you more time or money than expected.
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What’s the hidden cost people underestimate in AI projects?
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