One of the biggest reasons for organizations to invest in chatbots is the convenience of connecting with their customers. Moreover, the same implementation and management of information using chatbots also enable organizations to organize their in-house process simultaneously. Thus, organizations can optimize their overall operational cost and upscale business operations with ease and efficiency. Despite this, only 4% of enterprises have currently deployed communication systems with AI-chatbots as of now. The cherry on top is a study by Chatbots Magazine that claims that enterprises can save up to 30% in customer service after deploying AI chatbots. However, it is a good thing… The fact that haunts are there in one of Gartner’s studies, which has found that 99% of the AI-based initiatives by the organizations will fail in 2020 due to lack of proper knowledge base foundation. Since chatbots are one of the most common AI-initiatives among the organizations, chatbot knowledgebase management is one such requirement that might prevent this poised failure. How knowledge management relates to chatbots? Take a customer-initiated chat or query for instance. The customer support chatbot aims to narrow down the customer’s actual intent by asking the least possible but enough questions from the user. The bot uses Natural Language Processing (NLP) to understand the user’s query, and some information from its chatbot knowledge base to find the right solution, content, answer, or a question to respond against the query. Hence, if the knowledge base lacks the right content or information, the chatbot fails to do any of this. Thus, knowledge management (KM) is the foundation of any AI initiative, and it defines the strength of your AI chatbot to manage your customer support. Not just chatbots but KM is a critical element of every other AI application, which might range from managing the customer support to fetching data-extensive responses from CRM or guiding a customer to find the right kind of product on an online store. Unfortunately, most organizations, in a hurry to implement AI in their current operations, overlook the fundamental requirement of knowledge management to improve their chatbots. Knowledgebase management and operation of chatbots are a co-dependent phenomenon. Both require each other to pull meaningful insights for your organization. Chatbots can unify your information neatly Generally, on a website, we organize the files in a directory or metadata structure. Depending on the standards used for the hierarchy and neatness of the structure, it works well for website management. If you have enough time, and you know the location of a file, you can traverse through the hierarchy and find any information you need. However, this method proves ineffective when you are dealing with a huge database. It takes time to traverse a directory manually and find the intended information. Alternatively, you may adopt a similar method to that of search engines to fetch a piece of information. Based on the keywords, metadata, and popularity of the files, a search bar can return you a set of multiple files that might contain the intended information, and it will work decently for huge datasets, too. However, this information organization method is valuable only when the user has time to traverse through the search results to find the information. That’s where chatbots can organize your information better and provide direct responses; no directory to parse manually or no search result to traverse. With NLP in action, the bot can understand a query, and employ deep learning to analyze the entire dataset in a blink and return a direct response. As a user, it might seem to you that information is unorganized on the front, but it actually is organized in the back-end. As a user, you care about mere information that you want to retrieve, and there is no point pushing you to the back-end, file hierarchy, or database to get that. Direct responses from bots eliminate hassles in huge data sets Both the site hierarchy and search method fail at a certain point when a huge amount of information overwhelms your database. Even if somehow you manage to retrieve some information manually from a well-organized but huge dataset, you can’t be sure about its precision and correctness. On the other hand, the search-based organization too can return an overwhelming number of results, which would not be easy to traverse. You still have to deal with unimportant results. Overall, it will degrade the data organization and retrieval experience that will eventually reflect on your support quality. In contrast to this, the chatbot will handle the back-end processing for you, while you can enjoy a direct response, that too in a conversational flow. Less work for you to organize and retrieve the same information as many times as you want. Moreover, the link-back to the data-source makes sure that the user is assured about the correctness of the information. Chatbots don’t demand their users to be trained Be it folder structure or the search method, you need to train your users about their limitations and best practices. The users should know how to store the information, where to store it, and how to retrieve it from a heap. Unfortunately, this comes out as a big task and it’s not possible to train everyone on it. Hence, it limits the number of users you can train and deploy for knowledge management. On the other hand, you don’t need the training to get assistance from bots. Rather, the bots are trained to make things easier for the users. They have models to answer common questions, learn new information, and relay queries to learn user intent if they don’t answers. It’s simple; you ask a question to a bot, if it knows the answer, it will respond instantly. If the answer is incorrect, you provide feedback, and the bot learns about it. To be precise, organizations need a standard knowledge base groundwork to curate the useful content for their chatbots and make them effective to handle customer services. Considering the complexity of content (PDFS, FAQs, Text, Web, Tables, etc.) where the intended information might reside in, only bots have the processing power to come up with error-free responses in no time. Chatbots are revolutionizing the business sphere, but for that to happen with your business too, you need chatbots to revolutionize your Knowledge management first. Toward the day’s end, you can have all the information contained on the planet; however, without effective knowledge base management, chatbots probably won’t have the option to discover it for you. One of the biggest reasons for organizations to invest in chatbots is the convenience of connecting with their customers. Moreover, the same implementation and management of information using chatbots also enable organizations to organize their in-house process simultaneously. Thus, organizations can optimize their overall operational cost and upscale business operations with ease and efficiency. Despite this, only 4% of enterprises have currently deployed communication systems with AI-chatbots as of now. The cherry on top is a study by Chatbots Magazine that claims that enterprises can save up to 30% in customer service after deploying AI chatbots. However, it is a good thing… only 4% of enterprises Chatbots Magazine 30% in customer service The fact that haunts are there in one of Gartner’s studies , which has found that 99% of the AI-based initiatives by the organizations will fail in 2020 due to lack of proper knowledge base foundation. Since chatbots are one of the most common AI-initiatives among the organizations, chatbot knowledgebase management is one such requirement that might prevent this poised failure. one of Gartner’s studies How knowledge management relates to chatbots? How knowledge management relates to chatbots? Take a customer-initiated chat or query for instance. The customer support chatbot aims to narrow down the customer’s actual intent by asking the least possible but enough questions from the user. The bot uses Natural Language Processing (NLP) to understand the user’s query, and some information from its chatbot knowledge base to find the right solution, content, answer, or a question to respond against the query. Hence, if the knowledge base lacks the right content or information, the chatbot fails to do any of this. Thus, knowledge management (KM) is the foundation of any AI initiative, and it defines the strength of your AI chatbot to manage your customer support. The bot uses Natural Language Processing (NLP) to understand the user’s query, and some information from its chatbot knowledge base chatbot knowledge base to find the right solution, content, answer, or a question to respond against the query. Not just chatbots but KM is a critical element of every other AI application, which might range from managing the customer support to fetching data-extensive responses from CRM or guiding a customer to find the right kind of product on an online store. Unfortunately, most organizations, in a hurry to implement AI in their current operations, overlook the fundamental requirement of knowledge management to improve their chatbots. Knowledgebase management and operation of chatbots are a co-dependent phenomenon. Both require each other to pull meaningful insights for your organization. Chatbots can unify your information neatly Chatbots can unify your information neatly Generally, on a website, we organize the files in a directory or metadata structure. Depending on the standards used for the hierarchy and neatness of the structure, it works well for website management. If you have enough time, and you know the location of a file, you can traverse through the hierarchy and find any information you need. However, this method proves ineffective when you are dealing with a huge database. It takes time to traverse a directory manually and find the intended information. Alternatively, you may adopt a similar method to that of search engines to fetch a piece of information. Based on the keywords, metadata, and popularity of the files, a search bar can return you a set of multiple files that might contain the intended information, and it will work decently for huge datasets, too. However, this information organization method is valuable only when the user has time to traverse through the search results to find the information. That’s where chatbots can organize your information better and provide direct responses; no directory to parse manually or no search result to traverse. With NLP in action, the bot can understand a query, and employ deep learning to analyze the entire dataset in a blink and return a direct response. As a user, it might seem to you that information is unorganized on the front, but it actually is organized in the back-end. As a user, you care about mere information that you want to retrieve, and there is no point pushing you to the back-end, file hierarchy, or database to get that. Direct responses from bots eliminate hassles in huge data sets Direct responses from bots eliminate hassles in huge data sets Both the site hierarchy and search method fail at a certain point when a huge amount of information overwhelms your database. Even if somehow you manage to retrieve some information manually from a well-organized but huge dataset, you can’t be sure about its precision and correctness. On the other hand, the search-based organization too can return an overwhelming number of results, which would not be easy to traverse. You still have to deal with unimportant results. Overall, it will degrade the data organization and retrieval experience that will eventually reflect on your support quality. In contrast to this, the chatbot will handle the back-end processing for you, while you can enjoy a direct response, that too in a conversational flow. Less work for you to organize and retrieve the same information as many times as you want. Moreover, the link-back to the data-source makes sure that the user is assured about the correctness of the information. Chatbots don’t demand their users to be trained Chatbots don’t demand their users to be trained Be it folder structure or the search method, you need to train your users about their limitations and best practices. The users should know how to store the information, where to store it, and how to retrieve it from a heap. Unfortunately, this comes out as a big task and it’s not possible to train everyone on it. Hence, it limits the number of users you can train and deploy for knowledge management. On the other hand, you don’t need the training to get assistance from bots. Rather, the bots are trained to make things easier for the users. They have models to answer common questions, learn new information, and relay queries to learn user intent if they don’t answers. It’s simple; you ask a question to a bot, if it knows the answer, it will respond instantly. If the answer is incorrect, you provide feedback, and the bot learns about it. To be precise, organizations need a standard knowledge base groundwork to curate the useful content for their chatbots and make them effective to handle customer services. Considering the complexity of content (PDFS, FAQs, Text, Web, Tables, etc.) where the intended information might reside in, only bots have the processing power to come up with error-free responses in no time. Chatbots are revolutionizing the business sphere, but for that to happen with your business too, you need chatbots to revolutionize your Knowledge management first. Toward the day’s end, you can have all the information contained on the planet; however, without effective knowledge base management, chatbots probably won’t have the option to discover it for you.
