When managing e-commerce operations across multiple marketplaces, one lesson has become clear to me: don’t start by dumping every SOP on the AI agen. Mula by asking what it sees in our dashboards. Most of the time, we already know the outcome we want - move slow stock, prevent stockouts, protect margin, win campaigns. But when we give AI overly detailed instructions too early, we also force it to follow our existing way of working. The agen may still hit the target, but the process can become unnecessarily rigid, complex, or slow.
I now prefer to begin with a simple request: “Brief me on what you see across Shopee, Lazada, and TikTok Shop data, what looks off, and what you recommend for inventory, pricing, or listing operasi.” Only after that do I decide what should be executed. This changes the AI agen from an SOP follower into an ops partner. It can spot redundant listing updates, late stock alerts, manual repricing gaps, campaign timing issues, SKU cannibalization, or a simpler way to manage inventory across channels.
The workflow I use is simple: observe, diagnose, recommend, challenge, and execute. First, let the AI scan the marketplace environment - sales velocity, stock levels, campaign performance, return rates. Then let it identify inefficiencies, suggest improvements, challenge the current approach, and only after that proceed with execution.
One of the biggest mistakes in using AI agents is assuming that better prompting always means giving more instructions. In marketplace operations, better prompting often means giving the agen enough freedom to find patterns in our data. The objective should remain clear - sell the right SKU, at the right price, at the right time - but the method does not always need to come from us.
That is where AI agents become genuinely useful in e-commerce operations - not just automating replies or stock updates, but helping redesign how we run marketplace stores from listing to fulfillment.
#E-dagang #Marketplace #AIAgents #AgenticAI #Automasi #InventoryManagement #OnlineSales #OpsStrategy #NeuralOps
