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An Italian insurer, for example, has developed a “cognitive assistance service” within your IT organization. The system involves employees who use in-depth learning technology to find frequently asked questions and answers, previously resolved cases and documentation to find solutions to employee problems. It uses intelligent routing capacity to transmit the most complex problems to human representatives and uses natural language processing to support user requests in Italian. Versions of automatic learning can perform exploits such as image recognition and speech.

For business leaders, it is imperative to obtain a plan to operate the AI in the organization. Initial AI projects may or may not be delayed, but the risk that companies will not become competitive by Freight measure ignoring AI is high. Natural language processing is today an AI technology essential for commercial operations. In their simplest form, these systems offer reliable translation in a defined context.

CognitiveScale is an AI software company that is building increased intelligence solutions for the health, insurance, financial services and digital commerce sectors. Its solutions help customers benefit from intelligent, transparent and reliable digital systems powered by IA / ML. The CognitiveScale solution allows companies to quickly build, operate and evolve intelligent, transparent and reliable AI systems in any cloud. With their products, companies are increasing the acquisition and commitment of customers, while improving processes such as billing and complaints.

Everyone evolves on their own path and, when applied in combination with data, analysis and automation, can help companies achieve their goals, either by improving customer service, either by optimizing the supply chain. Before embarking on an AI initiative, companies must understand which technologies perform which types of tasks and the strengths and limits of each. Expert rules-based systems and the automation of robotic processes, for example, are transparent in the way they do their job, but none are able to learn and improve. Deep learning, on the other hand, is ideal for learning from large volumes of marked data, but it is almost impossible to understand how you create the models you create. This “black box” problem can be problematic in highly regulated industries, such as financial services, where regulators insist on why decisions are made in a certain way. There are many applications of artificial intelligence in business, from improving relationships with employees and customers to finding extreme data volume models and performing repetitive tasks.

AI systems quickly obtain useful information from massive data sets, which is also a complicated and slow process. By integrating their CRM into IA, companies can have complete customer data and have useful ideas for making smart and timely business decisions. Today, business decisions such as “on which customers should focus” and “the best possible offers” are made using IA. In addition, the implementation of AI in business also identifies potential customers and the most potential opportunities. It feeds customer service, guarantees the defense of cybersecurity, performs data analysis, helps customer service, reduces energy costs, predicts sales, helps companies focus more on customers, etc.