Six mistakes in introducing AI automation in SMBs
Six mistakes when introducing AI automation in SMBs Over the past few years, I've been seeing the same pattern. A company of twenty people buys a license, someone builds an impressive demo, everyone nods in agreement, and three months later, no one can say whether anything has been saved. The technology is less of a problem than people think. Mistakes are almost always made before the first line of code. 1. It starts with the tools, not the process that hurts…
When introducing AI automation in small and medium-sized businesses (SMBs), several common mistakes can hinder success. These mistakes include choosing a tool without identifying a specific, painful process to automate, failing to establish clear key performance indicators (KPIs) to measure success, and treating pilots as endless experiments rather than time-bound tests.
Additionally, companies often underestimate the importance of planning for errors and exceptions, and may be misled by vendors who rebrand existing chatbot products as "agents" to make them more appealing. According to a McKinsey analysis, tracking clear KPIs has a significant impact on EBIT, but few organizations actually do it.
Written by urgent.news from Dev.to's report — not a translation of it. Machine-written — may contain errors; check the original before relying on it.
