Why So Many AI Initiatives Fail

Many organizations struggle with building successful AI applications due to three main hurdles: wrong data, inappropriate infrastructure, and processing at the wrong time. The challenges are mainly related to data, including having inappropriate datasets, misaligned infrastructure that moves too slowly, and difficulties in identifying proper actions in real-time. Current ML infrastructures also struggle with processing massive volumes of events, leading to latency issues and slowing down AI initiatives. To overcome these hurdles, successful companies like Apple, Google, FedEx, Uber, and Netflix aggregate real-time data from various sources and use it to train and serve their models. They collect data at the most granular level and store massive volumes of event data using a unified data platform. This approach allows data engineers and scientists to work together on feature engineering, model experimentation, training, and inference processes, ultimately improving user experiences and defining market leadership.

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