Generative AI is often pitched as a silver bullet for productivity, but I see teams getting stuck by trying to automate the wrong things. Before you pipe every customer query into an LLM, ask yourself if the source data is actually clean. Automating a process that relies on fragmented or poorly structured information just accelerates the speed at which you generate errors.
The most effective AI implementations focus on the 'last mile'—taking high-quality, structured internal data and using it to provide instant, context-aware answers for your customers. Start by auditing your data quality rather than your prompt library. If your underlying business logic is messy, no amount of machine learning will fix it. Have you audited the quality of the data your team uses daily, or are you still relying on manual entry?
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