Artificial intelligence (AI) has mostly been an obsession for research departments and development shops. Recently, however, the potential business ROI for the enterprise community in the form of amplified customer/employee digital experience extended intelligent capabilities, reduced support costs have become clearer. From addressing simple FAQ’s to making intelligent conversations, chatbots have progressed significantly in understanding and solving problems.
While AI is becoming a new tool in the C-suite tool belt to drive revenues and profits, it has become clear that the deployment of chatbot encompassing specifics of business applicability stands critical.
According to a study, 87% of CEO’s and business leaders trust AI, but employees trust (33%) was cited as one of the greatest barriers to AI Adoption.
One such bottleneck that is toning down the employee’s trust might be chatbots IQ. It’s a fact that chatbot answering basic questions irrelevant to the context and learning from the previous conversations is trolled by smart employees constantly.
There is a high need for the chatbots to deliver more appropriate results by scaling up its intelligence in handling customer conversations.
Though we know that chatbots have a high potential in becoming intelligent, we don’t understand what goes into making intelligent chatbots.
Enterprises are, by now, aware that chatbots aren’t smart at the beginning of their deployment. They are made intelligent by leveraging technologies like machine learning, big data, natural language processing (NLP), etc. – which helps chatbots to understand and interpret context, intent and continually enhance its knowledge base.
During the process of becoming smart, there is a high need for an effective chatbot builder platform in place to train it with the appropriate skill matching the organization needs.
Bots built using intelligent platform enables organizations to train, build and launch customized conversational chatbots powered by artificial intelligence.
In customer engagement, real-time contextual understanding is essential to deliver meaningful conversations. To have a good understanding of context, a chatbot needs to analyze inputs like time, day, date, conversation history, tone, sentence structure, intent, identity, etc. These inputs are then fed to empower chatbots to comprehend the context in the conversation.
For instance, in the sentence, “Fantastic! Presently the flight is delayed by one more hour”, the customer isn’t happy about the flight being late but it can trick the interpretation of the machine encouraging to commit an error if it is unable to understand the context.
Apart from self-learning, it is wise to provide or feed chatbots with different contexts based on situations, linguistic preferences, persistence, emotional context, etc so that they can utilize the context when required.
The ability to learn is a pivotal factor in building an intelligent chatbot.
A well-honed chatbot is one that learns from the conversations to enhance its performance metrics. There are 2 steps involved in this learning process. One is handling the end-user appropriately when there is no relevant answer found in the knowledge base and the second is recording learning from the failed conversations. Also, managing standard responses stands a key in handling user frustration.
User modelling, machine learning, and natural language understanding modules can help achieve better conversations and avoid expectations mismatch.
Leveraging neural networks, deep learning, Machine Learning (ML) algorithms and human supervisors ensure the AI chatbot becomes a good learner.
Learning is key to ensure that the chatbot identifies patterns in data it receives and answers to user queries in the most appropriate way. Thus, learning abilities are a must-have if a chatbot is to be made intelligent.
Handling a user when there is no answer dictates the satisfaction scores. Amplifying these scores can be better achieved when the conversation is transferred to a human agent rather than annoying the consumer/employee with the same repetitive questions/responses.
According to a report, 88% of consumers said they expect a natural transition between a virtual agent to a human agent while making a purchase decision or contacting customer care.
It goes without mentioning that humans will remain an integral part of contact centres. However, chatbots automate the triage requests or help in troubleshooting, helping the human agents become more productive.
So the real challenge is to train the bot when to transfer it to an agent. Using the advancements in AI, train the chatbot with user sentiment analysis and preference during an interaction and transition the conversation to a human agent.
Voice bots are an integral part of almost every function that focuses on providing a positive customer experience. One can skip the interaction with a complicated UI and ask the voice assistants to do the job. This adds both convenience and error-free interactions with the chatbot reducing the chance of failure. Enable friction-free conversations while freeing up employees to focus on more meaningful work.
With significant advancement in the fields of natural language processing (NLP) and machine learning (ML), voice assistants have become much more intelligent and useful in guiding customers to meet some of their needs.
Utilizing the best of technologies like voice recognition, speech synthesis, and natural language processing (NLP), chatbot parses a spoken phrase and translate it into written text.
According to Comscore, American media measurement and analytics company, more than half of the total searches will be voice-enabled searches by 2020.
With the power of AI and conversational UI, voice assistants can deliver more personalized experiences and can automate interactions by adding intelligence and insight to the conversation.
According to Gartner, by 2023, 25% of employee interactions with applications will happen via voice, up from almost 3% in 2019.
Realizing that chatbots are evolving technology in providing intelligent conversations, organizations need to focus on automating their communication system. If your chatbot is intended to address the specific problem then it is better to go for predefined communication flows. This will help you with a pleasant experience and a high conversion rate.
One thing organizations miss out is that a number of complexities involved in making these AI chatbots intelligent. Using advanced intelligent platform you can build a chatbot with ease and can enhance the level of chatbot’s intelligence.
If you’re looking for some personalized guidance on creating and incorporating intelligent chatbot for your business feel free to get in touch with one of our artificial intelligence and chatbot experts. We have built numerous chatbots for different industries like retail, insurance, banking etc. and would be more than happy to do the same for you.
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Looking to build intelligent chatbots? Check out Acuvate’s enterprise-ready chatbot builder platform, BotCore.