Starting a successful AI initiative requires a focused, practical approach. According to HPE, the journey begins with identifying simple, high-impact use cases that improve existing processes rather than building entirely new solutions. Many organizations use AI to boost productivity by targeting repetitive, manual tasks that slow operations. Leveraging proprietary data is key—cleaning, labeling, and preparing it carefully ensures better outcomes when training models. After training and limited user testing, AI applications can be rolled out and monitored in real-world conditions. Continuous iteration is essential to maintain performance and value. Finally, scaling AI across the business requires a robust data infrastructure and often benefits from expert guidance to meet evolving performa

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