- January 19, 2022
- Priyanka Shah
With a new decade of technological advancement approaching, conversational agents still remain one of the most popular applications of artificial intelligence. Chatbots are revolutionizing the B2E and B2B organizational scenarios. Chatbots expected to cut business costs by $8 Bn by 2022 — Juniper Research.
Chatbot can be divided into two categories. One is Flow-based chatbot and another is AI-NLP based chatbot. You can consider a flow-based chatbot as a single-task bot, which lacks customer management capabilities. However as popular as flow-based single task chatbots and bot frameworks are, these virtual agents have evolved to more than just rule-based chat agents.
Especially as an industry, it is imperative to build conversational agents that are powered by artificial intelligence and capable of performing automation workflows coupled with the integration of internal and external ecosystems and application of natural language processing. But it is difficult to create a data-intensive chatbot that works in tandem with multiple applications to serve the purpose.
Some of the key factors that are necessary to keep in mind while building effective AI based chatbots are:
- Identify your Target Audience and Goals
One of the primary analysis required to build the right AI chatbot is the analysis of your target audience and the processes that you are looking to automate with this agent. Enterprises should gather a clear understanding through expert consultation of how their customers will communicate with their agent and what automation is possible for their specific use case. Consider the user’s “point of view” before determining your bot’s flow. Set clear goals for what you require out of these AI agents.
- Designing the Right Conversation
Engagement and ease of access remain the primary purpose of deploying these automated chat agents, this is only possible through a well-designed conversation journey. Understand your user journey and design conversations that are natural and seamless, guiding the user to their destination. Keep in mind that the goal is to engage your users and ease their task with automation. Natural language processing and bot personality play an important role in this spectrum.
- Develop the right personality
Another important aspect that is often overlooked by many industries resulting in so many chatbots failing is the consideration of bot personality. The users of this virtual agent need to feel a natural, humane response rather than face a robotic or extreme personality. Give your chat agent a personality that makes conversations comfortable and fun.
- Incorporate multi-channel support
An enterprise level chatbot must have the capability to be deployed across various utility channels so as to provide maximum productivity. Enterprises should look for services that develop AI agents with user interfaces designed to deploy across various platforms, especially those that are used within the organization itself like Microsoft Teams and Slack.
- Natural Language Processing and Voice Enabled Agents
Natural language processing has become a crucial element within the realm of ai chatbots and conversational agents. It enables these robots to understand natural language and conduct accurate conversations while remembering user context. It is especially important with conversational agents that interpret voice. The future of these agents is voice and this is the right time to begin incorporating text and speech enabled bots for automation.
- Optimum Training with Machine Learning
To build a truly intelligent chatbot, it is essential to incorporate learning within its system. An agent that constantly recollects and learns from experience, on the basis of machine learning based training that makes the bot smarter with time. It also enables the conversational agent to remember the user context and intuitively conduct conversations.
- Ensure Ease of Integration across Enterprise Data and Systems
Any automation tool built for an organization has a very large scope, to tap into its full potential it is necessary that it securely incorporates already existing systems within its environment. In the industry realm, virtual agents are only as useful as the quality of their integration with the on-premises and other required systems to make truly adaptable and intelligent conversational agents. Access to varied data is pivotal to the impact of these ai powered chatbots.
- Build for Scalability from the Start
Although bots still largely remain conversational agents serving as customer service agents with robotic process automation they can now also handle business processing using text and voice. This functionality is essential for enterprises as it grants them ability to track and streamline multiple tasks at once. Ideally, the enterprise should deploy a chatbot that works on a single task along with creating and deploying a multi-purpose chatbot that communicates with multiple systems and completes a variety of tasks within each of them.