March 10, 2021
There are many NLP and ML technology companies that offer their framework for enterprises to build chatbots. Some of them even promote their framework as a chatbot in 10 minutes. In 2021, many of us believe that building and maintaining a chatbot is a piece of cake, something like creating a WordPress website.
It sounds like an easy task to build a chatbot for enterprise users and end customers using these raw technology frameworks to solve real-world problems, right? But let’s see the reality with some practical examples.
- Enterprise users are well settled with their employment-specific vocabulary for day-to-day business conversations. They don’t want to learn any new terminology to use for their chatbot
- Enterprise users especially end-customers expect customized answers rather than standard reply statements for their queries. This is why many enterprises are dealing with high volume of phone calls to their help desk, especially for business-critical requests
- Chatbot frameworks don’t offer out-of-the-box linguists to understand common enterprise user's language, especially during escalations
- Users are quickly hung out to dry with responses such as “contact your service desk” or “can you rephrase your question?”. Chatbots can work only with pre-trained statements, so users should ask questions only in the way it has been configured
- Even after a huge long-term investment in chatbot framework, enterprises are forced to keep advanced AI experts to constantly analyze data, fine-tune existing configurations, and identify false positive and false negative responses
- Enterprises need to keep their dedicated chatbot support teams for regular maintenance, handle user desertion problems, and track consumption ratios to identify new scope
- Most chatbots are good at searching knowledge articles. But many knowledge management softwares offer inbuilt knowledge search capabilities. Enterprises don’t need a separate chatbot for this monotonous purpose
- Chatbot framework doesn’t come with the pre-built user interface (UI). So, the enterprise need to do a separate investment for UI design and development
- Chatbot framework providers are core technology companies and they are not business domain experts. So enterprises will not get any support on best practices for their domain-specific functions
- Enabling chatbots in all user-preferred channels will help enterprises maximize their ROI. But they may or may not find their convenient channels from the standard offering. In that case, they need to hire a separate team to build chatbot channels
- Integrating these standard chatbots with customized enterprise application is not an easy task because every enterprise application works differently with its own custom data format
- Building performance analytics and reporting dashboard as a separate initiative to track enterprise-wide chatbot performance can be a burden
Like any other modern technology for business, user experience (UX) is a key factor to maximize user acceptance and quickly harvest ROI. But the above bottlenecks push chatbot users to think that their interactions are just a transaction, void of true care and support. So, what is the best option to own a chatbot for enterprise functions, and provide the best user experience
When an enterprise buys Lucy from DRYiCE software they don’t need to face any of the above obstacles. They don’t need to start from scratch, because most of the features, starting from basic to advanced CVA functionalities are either pre-built as a package or enabled as easy-to-configure predefined functions. Enterprises can deploy Lucy and run it quickly without any huge upfront investment in infrastructure setup, and expensive human resources to obtain quick ROI. This is a critical element for enterprises that are planning to build their chatbot. Additionally, Lucy can get smarter with every user interaction. So enterprises can deliver an exceptional user experience.
- Lucy supports enterprise-wide use cases, starting from raising a problem ticket, finding the right team to assign, to even triggering an automation to resolve user problems
- Lucy can speak the languages of users on day one and can be trained with any custom vocabulary
- Lucy comes with several out-of-the-box, pre-trained use cases for quick deployments
- Lucy can handle and support multiple queries from the same user at the same time
- During unknown user queries, Lucy can proactively connect the user with the right support team or even create a problem ticket
- Lucy is designed to do human-like conversation with personalized support. Most of the time, users may get confused about whom they are chatting with, a bot or a human
- Lucy offers all admin capabilities such as out-of-the-box features and a complete self-service model for enterprise to maintain with few admin resources
- Lucy admins require minimum coding knowledge to configure and test new use cases
- Lucy offers inbuilt performance analytics and tracking dashboards for enterprises to track and measure performance. Lucy even supports enterprises to find greenfield to enable more use cases
- Lucy maintains chat history for the respective user’s reference. The same user can continue incomplete conversations, instead of re-explaining the problems
- Lucy does context-based information search rather than standard keyword searches
- Lucy is powered to automatically create use cases for enterprise knowledge management software
- Lucy is enabled with most of the enterprise chat channels without any customization or coding
- Lucy comes with a pre-built and customizable user interface for enterprise consumption
- Lucy offers several industry-specific best practices and dialog snippets for effective deployments
- Lucy brings high scalability — it can dynamically scale up or down based on usage
- Lucy can understand user queries in the form of text, image, or voice and respond accordingly
- Lucy offers several pre-built integration adapters for the enterprise to quickly integrate with its existing IT and business systems
- Lucy protects enterprise data with all the latest technologies
This brings two-fold benefits for enterprises. First, they can quickly configure and enable Lucy with very minimal manpower. Second, they gain full control and ownership of their Lucy. With key features such as drag and drop, and minimum coding functionality, they can continuously manage, maintain and evolve Lucy’s functions.
Click here to request a quick demo and understand more about Lucy.
He is passionate and keen to remain knowledgeable about IT infrastructure, Artificial Intelligence (AI), Automation, Neural nets, Machine Learning (ML), Natural Language Processing (NLP), Image Processing, Client Computing, Blockchain, and Public/Private Clouds. His research interest circles around stack solutions to enterprise problems.
Kumar has a MS in Software Engineering (SE) along with several technical certifications like MIT’s Artificial Intelligence (AI), Machine Learning (ML- Information Classification), Service Oriented Architecture (SOA), Unified Modeling Language (UML), Enterprise Java Beans (EJB), Cloud and Blockchain, etc.