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Role of AI in Real-Time Analytics
- Predictive Analysis: AI is being trained to notice or identify extensive range of inconsistencies. Deep learning's capacity to analyze very huge amounts of high dimensional data which can take existing preventive maintenance systems to the next level, according to Chui and his co-researchers. Layering in additional data such as audio and image data and from other sensors includes relatively cheap ones such as microphones and cameras neural networks can enhance and possibly replace more traditional methods. AI's ability to predict failures and allow planned interventions can be used to reduce downtime and operating costs while improving production yield.
- AI-driven logistics optimization: According to the McKinsey team, AI can reduce costs through real-time forecasts and behavioral coaching. Application of AI techniques such as continuous estimation to logistics can add substantial value across sectors. AI can enhance routing of delivery traffic, thereby improving fuel efficiency and reducing delivery times. One European trucking company has reduced fuel costs by 15 percent, for example, by using sensors that monitor both vehicle performance and driver behavior drivers receive real-time coaching, including when to speed up or slow down, optimizing fuel consumption and reducing maintenance costs.
- Assist in customer service management: Improved speech recognition in call center management and call routing as a result of the application of AI techniques allow a more seamless experience for customers and more efficient processing, Chui and his co-authors state. For example, deep learning analysis of audio allows systems to assess a customers' emotional tone in the event that a customer is responding badly to the system, the call can be rerouted automatically to human operators and managers.