အလိုအလျောက်လုပ်ဆောင်မှု / ကွင်းဆင်းမှတ်စု
အေးဂျင့် ပရောဂျက်အများစုသည် မော်ဒယ်အပေါ်တွင် မဟုတ်ဘဲ ဘောင်၊ ဒေတာနှင့် မရှင်းလင်းသော အတည်ပြုချက်အပေါ်တွင် ကျရှုံးသည်။ အလုပ်တစ်ခုတည်းဖြင့် စတင်ပါ၊ နမူနာဒေတာပေါ်တွင် ရှေ့ပြေးပုံစံ ပြုလုပ်ပါ၊ အကျိုးသက်ရောက်မှုရှိသော လုပ်ငန်းစဉ်တိုင်းတွင် လူတစ်ဦး ထားရှိပါ။
ဤကွင်းဆင်းမှတ်စုကို အင်္ဂလိပ်ဘာသာဖြင့် ထုတ်ဝေထားပါသည်။
By 2026 the question has stopped being "can AI do this?" and become "can we trust it to, and who checks?" Business owners now widely believe AI assistants improve customer experience, and a large share of common customer questions can be handled automatically. The gap between that belief and a dependable production system is usually not the model — it is the scope, the data and the approval design around it.
The first wave of AI projects optimised for the demo: impressive, disconnected, hard to trust with real work. The second wave — the one teams are building now — optimises for the workflow. Inputs are defined, sources are named, outputs are reviewable and every consequential step pauses for a person. That is the difference between a chatbot that answers and an agent a business can rely on.
Pick one repetitive job with a clear source of truth. Not "automate the operations team" — something like "compare every product record against the supplier feed and flag missing fields." A narrow task makes the next decisions concrete: what is the input, what is the output, what is an observation versus a proposal, and who is allowed to act on each.
Before connecting any tool, run the workflow on a small, varied set of inputs: a complete record, an incomplete one, a contradictory one and an unavailable source. Watch where the process becomes uncertain. Those moments tell you what to fix in the data, the instructions or the handoff — far cheaper to discover on a sample than in production.
Show the current value beside the proposed one, name the source, and make editing or rejecting straightforward. Reading a public page and publishing a new description are different decisions; the interface should treat them differently. A review screen that reflects the real consequence of the action is what makes an agent safe to leave unattended.
Define what good looks like before launch — accuracy on a held-out set, how often a human needed to correct it, and how long each task took. Add guardrails for out-of-scope questions and an audit trail of what the agent proposed and what happened next. Measure after launch too: an agent that quietly drifts from its brief is worse than one you never trusted.
Human-in-the-loop is not a compromise — it is the feature that makes adoption possible. The controls should match the consequences: a draft can be automatic, a publish decision never should be. Most agents that get cancelled in 2026 will be cancelled because nobody could see what they were doing. Make progress visible, make decisions reviewable and the rest follows.
