1-minute tech🧑💻 The reason why your AI automation is spitting out garbage
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Tip: AI won't work with messy data. Why: If you're not tracking things like out-of-stock events or collecting data correctly, your AI will train on garbage and give you garbage predictions. Example: Johnson Goh from Mindhive learned this the hard way while building sales forecasting for Zus Coffee. They were training AI to predict what food would sell the next day. But the predictions kept missing the mark. Turns out the problem was that Zus Coffee wasn't recording when items ran out of stock. So when a popular item showed "low sales" on certain days, the AI thought it wasn't in demand. In reality, it had sold out early and they just didn't record it. The AI was training on incomplete data, learning the wrong patterns and making terrible predictions. Missing just ONE data point distorted the entire model. Do this: Audit your data before touching AI.
AI amplifies whatever you feed it. As the saying goes: garbage in, garbage out. Likewise, feed it clean data, get accurate insights. Want to know if your data is ready for AI automation? I'll audit your data collection process and show you exactly what needs fixing first. Book your free automation audit now: https://thesecondteam.com/get-started Hi, I'm Adrian Ching, founder of Upstack Studio, an app development & AI automation agency that helps entrepreneurs scale their business through tech. AKA the Malaysian dude on YouTube who builds apps. You know, the one with zero shame. Every week, I share a 1-minute tip to help founders like you use tech to 2X your business. Follow along if this sounds cool. |
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