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AI retail tools have moved far beyond simple automation and data crunching. Today's platforms dive deep into the subtle patterns of consumer behavior, market dynamics, and operational efficiency finding hidden opportunities that even experienced retailers might miss.
This type of customer care was a process that could certainly be automated. Lack of automation also raised the issue that digital customer care was bound within specified hours. That was, until the introduction of AIchatbots for business emerged on the IT landscape. Li Qiang, IT Platforms Executive of ENN Group Co.
While the growing popularity of consumer AIchatbots have led many companies to recently enter the artificial intelligence (AI) space, IBM’s journey with AI has been decades in the making.
For example, organizations can use generative AI to: Quickly turn mountains of unstructured text into specific and usable document summaries, paving the way for more informed decision-making. Automate tedious, repetitive tasks. Generative AI is being used to automatically update and maintain code across different platforms.
According to research from IBM ®, about 42 percent of enterprises surveyed have AI in use in their businesses. Of all the use cases, many of us are now extremely familiar with natural language processing AIchatbots that can answer our questions and assist with tasks such as composing emails or essays.
From there, the chatbot uses automation to scan the database of responses and provide the most relevant response. In most scenarios, chatbots offer the option of live chat support with the customer service team if the chatbot responses fail to answer the customer’s question.
Conversational search uses generative AI to free up human authors from writing and updating answers manually; this accelerates time to value and decreases the total cost of ownership of virtual assistants.
Enterprises want to automate frequently asked transactional questions, provide a friendly conversational interface, and improve operational efficiency. In turn, customers can ask a variety of questions and receive accurate answers powered by generative AI.
From voice assistant to automated mail replies to speech recognition, there are myriads of things where we deploy these technologies. Although we talk about AI and Big Data at the same length, there is an underlying difference between the two. The quality of input data greatly influences the effectiveness of AI models.
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