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While it sounds like fiction, we\u2019re closer to this scenario than you might think. Artificial intelligence (AI) has been incorporated across various sectors in recent years.<\/p>\n
According to Gartner, organizations<\/a> will have 35 AI projects in place by 2022, up from only four in 2019. However, it\u2019s not an accessible technology to implement in your business, so you\u2019ll need to turn to professionals. For example, if you own a Magento store, you\u2019ll need to hire specialists providing custom Magento development services to properly integrate a chatbot plugin into your website and modify it.<\/p>\n What AI projects and applications<\/a> will we see in the future? Let\u2019s look at what we should expect from conversational AI and customer service in 2022 and beyond.<\/p>\n The following technologies are transforming the way humans interact with bots, including:<\/p>\n How can all this influence customer experience? Combined with conversational AI, they allow us to converse with robots as we do with each other.<\/p>\n Over the last decade, AI<\/a> has progressed to the point that we can\u2019t always recognize whether we are talking to a human or a robot. But it\u2019s not just about the \u201chuman\u201d level of interactions. AI apps can also make informed decisions based on the actionable insights gathered from data.<\/p>\n According to a survey published by Markets and Markets<\/a>, the global conversational AI industry is predicted to increase by more than three times in five years. Namely, it will grow from $4.2 billion in 2019 to $15.7 billion by 2024.<\/p>\n Do you still doubt that AI will be the next phase of customer service and a decisive competitive advantage in the coming years? Then, read on to find three conversational AI trends of 2022 and beyond.<\/p>\n Traditional chatbots are like conventional programming techniques. They work on a set of rules, such as \u201cif this, then that, else this\u201d.<\/p>\n Suppose a customer wants to know about the order status. The chatbot goes through its rules, discovers the right scenario, and reacts with a programmed response. It matches terms in the client\u2019s text with keywords in its memory to choose the suitable reply.<\/p>\n For example, the customer specified the \u201cwhere\u2019s my order\u201d phrase. The relevant response may include the map or line with the place of the order on it. It works for simple questions, but the chatbot doesn\u2019t fully comprehend the dialogue, limiting the customer\u2019s interaction.<\/p>\n Here\u2019s how such chatbots look, using the example of the prAna Facebook chatbot. It allows you to communicate with the help of short phrases to get the most essential information. Unfortunately, you won\u2019t get a human-like experience from this bot.<\/p>\n Screenshot taken on the official prAna<\/a> Facebook chat<\/small><\/p>\n Conversational AI leaves keyword and script restrictions behind<\/strong>. It carries on a full conversation via text or speech, imitating real-life specialists. What\u2019s more, it can translate speech in real-time to sustain a dialogue in the customer\u2019s native language.<\/p>\n What happens when a bot doesn\u2019t understand the input? Traditional chatbots would return the default \u201cCan\u2019t understand the request\u201d answer. And the conversation stops with a customer left frustrated. Case in point, I tried to interact with the Kayak chatbot on Facebook, but the chat appeared to be a dead-end.<\/p>\n Screenshot taken on the official Kayak<\/a> Facebook chat<\/small><\/p>\n Conversational AI can intelligently grasp the context of discussions to request clarification when needed and then explore a large knowledge database for the most relevant responses.<\/p>\n Another crucial factor is how customers feel during the conversation. Powered by associated technologies, conversational AI can determine human emotions with sentiment analysis<\/strong>.<\/p>\n For example, NLP allows chatbots to recognize and solve consumer discontent. It includes examining the choice of words and routing customers to the proper human assistance for fast and effortless processing.<\/p>\n Many CRM and customer data platforms (CDP) now leverage conversational AI and ML. For example, here are a couple of AI-powered CRM platforms:<\/p>\n Why do they make such a move? It gives them functionalities, such as:<\/p>\n This enables sales teams to better understand and engage buyers.<\/p>\n CDPs have also incorporated AI to unify customer data<\/strong>. Conversational AI gives marketers a better grasp of their customers\u2019 wants, how they feel, and what they\u2019re most likely to do or buy.<\/p>\n What do the AI-enhanced platforms consider? They store the present interaction, purchase and browsing history, past customer service inquiries, and demographics to tailor the next best action for each client. For example, they can predict which products you may find interesting. Explore it in the screenshot of the Ultra\u0300chic store below.<\/p>\n Screenshot taken on the official Ultra\u0300chic website<\/small><\/p>\n Customers crave seamless and hyper-personalized experiences across all channels. That\u2019s why call centers should equip their human agents with intelligent helpers to improve the customer journey<\/a>.<\/p>\n Below you can see an example of an AI employee at 7-Eleven. The company employed a Korean startup, Deepbrain AI, to create an assistant with the help of real-time video synthesis. It allows for engaging discussions with consumers and realistic lip and tongue sync. It gives faster responses, resulting in a better user experience.<\/p>\n Image credit: Deepbrain AI<\/a><\/small><\/p>\n What can she tell? She advises consumers on using automatic stores, finding products, restaurants, public transportation, and other topics. She also relies on conversational AI and thousands of pre-recorded lines (such as client names) to make more personalized connections.<\/p>\n What can spoil the customer experience more than having to repeat yourself? When you speak with a new member of a company\u2019s customer service team, you expect recognition. The same applies to interaction with a company through various channels.<\/p>\n How can an AI customer service system help clients? It can specify initial inquiries and send users to the appropriate person for additional support<\/strong>, thus saving their time, effort, and frustration.<\/p>\n A chatbot or virtual agent records all the data during the customer\u2019s initial contact. As a result, consumers don\u2019t have to explain their problem to a real person once again. It also tracks their journey to enhance current service and support processes and structure.<\/p>\n That\u2019s not all AI is capable of. AI systems may detect when client inquiries go ignored<\/strong> and prioritize them accordingly.<\/p>\nTechnologies Behind Conversational AI and Customer Experience Automation<\/h2>\n
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3 Ways How Customer Experience Automation and Conversational AI Evolve<\/h2>\n
1. Going Beyond Predefined Scripts and Rules<\/h3>\n
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<\/p>\n2. Facilitating Hyper-Personalization<\/h3>\n
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<\/p>\n3. Creating Connected Customer Journeys<\/h3>\n
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