AI automation is exploding because companies want faster execution with less manual effort. Oracle defines AI automation as combining AI with automation tools to handle complex tasks that previously needed human attention.
But here’s the truth nobody likes:
Most workflows do not fail because AI is weak.
They fail because the product around the AI is weak.
Bad integration.
Loose permissions.
No fallback logic.
Zero trust design.
That is why buyers are no longer asking, “Can we add AI?”
They are asking, “Can this actually survive production?”
For teams asking that question seriously, they probably should get in touch with experts then they should make a decision!!!
Have a Great weekend!
From building automations for SMBs and a few mid-market clients, the failure mode I see most isn't the AI - it's the surrounding plumbing. Brittle webhook chains, no idempotency, no retry budgets, no observability. Teams treat the LLM like a black box and forget basic distributed systems hygiene. The other big one: zero data quality validation upstream, so the AI is "wrong" because the input is garbage. The model is rarely the bottleneck.
Shlomo Friman
Exploring the intersection of software archaeology and modern enterprise intelligence.
"Can this actually survive production?" is the right question and most vendors cannot answer it honestly because their demo environment has none of the edge cases that production has. Clean inputs, cooperative APIs, predictable load. Ship it into the real world and all three assumptions break within the first week.