Start with a task, not a project.
In recent years, AI has become more powerful and its applications to business have increased dramatically. As a result, companies that hadn’t seriously considered using AI are taking a fresh look. The appeal is obvious: different forms of AI can enhance performance through, prediction, automation of routines, identification of images essential to operational activities, or the identification of key words, phrases and patterns in voice and text for information management.
Enhancing predictive accuracy in areas such as customer behavior or general forecasting, which applies to a multitude of industries such as insurance, finance, marketing, and even agriculture. Once you’ve identified that adequate data is available for AI to process, you’ll need to ensure that it’s possible to integrate an AI’s output into the target task. In other words, can it seamlessly plug into automated operations that depend on its recommendations? If your model is built with Python, will that be compatible with your systems? This is where IT experts play a vital role.
The need: The company received a large volume of customer service emails. Responding to them involved repetitive processes, which made the task a promising candidate for AI.
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