Human minds cannot comprehend data as efficiently as Artificial Intelligence can. The digital devices in our lives from office to home are increasing every time there is a new utility application of AI discovered in the market or business world. These devices then continuously collect and transmit data to cloud platforms like Amazon Web Services (AWS), Google Cloud Provider, or Azure, where they are viewed, analyzed, and processed to provide real-time analytics. This is important as many sectors like health diagnosis, customer engagement, acquisition, and other services rely on these analytics for their success. Therefore, it is necessary to optimize computing to meet real-time response requirements. For this, IT structures need to concentrate on diverse and quickly advancing loads and algorithms which are enabled by AI. Plus, there should be an expansion of the integration of computing into storage and networking.
For this, service providers require a foundation structure that provides both differentiation and ability to deliver output at quicker rates, low latency, better power efficiency with flexible software and hardware unit to handle algorithms. These algorithms can range from recurrent neural networks, long term and short term memory networks, convolutional neural networks, and query acceleration that is based on the Apache Spark cluster computing framework. To attain real-time analytics, results, and predictions, the computing world needs to develop and employ AI that is adaptive. As there is a proliferation of demand for real-time services and solutions, complex and unstructured data, and piling work cases, they shall surely pave the way for Real-time AI.
According to a statistics report, AI implementation jumped from 48 percent in 2018 to 72 percent in 2019. This happened since the real-time feature of AI helps with a quick decision-making process when it comes to effectual business strategy for improved operational capacity. Business Brands that focus on the customer services industry gain huge benefits from Real-time AI. Their main objectives are to create a target audience by using data science, applying machine learning, and automating tasks at scale. Based on the targeted customers' behavior adopt measures for personalized engagement which will lead to higher customer retention and loyalty. These business brands also use Real-time AI to switch from reactive strategies to proactive ones. This enhances better customer servicing and experience, thereby expanding the customer base and leads generation.
So if applied appropriately, the Real-time AI can transform the customer journey through the use of real-time decisions. Moreover, irrespective of a business brand's customer goals, the capacity, and accuracy of AI are unparalleled. This is because other than Real-time AI empowering businesses to earn promising leads generation, provide real-time analytics, and optimize customer service; the algorithms also help in getting an in-depth idea of future trends and customer behavior. This can enable brands to drive insight-oriented businesses' goals and campaigns and serve the customers' needs before they arise. And, increased customer retention margins means a significant effect on profit percentages. Hence, it is evident that Real-time AI will find itself spreading over business brands and firms in the coming years.
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