conversational AI

5 Must-Have Features of Your Conversational AI Solutions

It can be challenging to find the right solutions, so we’ve laid out the five most valuable features to look for in your conversational AI solutions. 

Conversational AI technologies are rapidly growing in popularity, and there’s little wonder why. By leveraging elements of artificial intelligence (AI), Natural Language Processing (NLP), and Machine Learning (ML), conversational AI enables automated, human-like interactions between individuals. Whether interacting in a B2B or B2C environment, conversational AI facilitates highly efficient and accurate communications to optimize your business operations. 

But, while many companies recognize the value of a conversational AI solution and are interested in adopting one, they aren’t sure where to start. With so many features available, it can be challenging to determine which ones are necessary for your business. For this reason, we’ve laid out the five most valuable features to look for in your conversational AI solution. 

Nearly every company today is looking to implement conversational AI. Virtual AI agents enable expedited engagement, extended support for remote working, self-service and autonomy in request resolution, and drastically increased productivity while saving money. Conversational AI provides unprecedented customer data insights for educated sales leads, upselling and cross-selling, and fast response to developing trends. As a result, a virtual AI assistant may save you millions of dollars, increase customer happiness, and handle more complicated use cases. In other words, everyone benefits.

With the growing popularity of conversational AI comes a plethora of options for businesses. There are plenty of options for virtual AI assistants on the market right now, and choosing the right one may greatly influence the advantages and value you get out of your investment. At the same time, you want to invest in conversational AI capabilities that support your markets, use cases, and target audiences without introducing unnecessary features. So, if you've decided to use an AI-powered virtual assistant, let's have a look at the five "must have" qualities of your conversational AI solution:


1. Advanced AI Learning 

Conversational AI assistants are built on advanced AI self-learning capabilities. Consumer-oriented, data-driven, well trained virtual AI assistants will engage in conversations as if they were humans, being able to easily identify your customer’s intent and to answer accurately. An advanced virtual AI assistant must  be contextually aware, which can only be accomplished through Natural Language Processing (NLP). Machine learning (ML) is also critical to your virtual AI assistant’s capacity to learn new things while operating. Your conversational AI solution should also use predictive intelligence and analytics to tailor responses based on user profiles; it recalls a user's preferences and gives solutions and suggestions, or it makes educated estimates about a customer's future requirements.

For example, DRUID provides a cutting-edge proprietary NLP/NLU engine with over 95% accuracy that interprets user intent and provides real-time contextual information based on behavior and preferences for the ultimate conversational experience.


2. Omnichannel Messaging 

Conversational AI solutions ensure that every user's previous interactions with the virtual AI assistant are remembered across all channels–whether online, through SMS, website, or phone. The virtual AI assistant can use information from a customer’s profile, order history, prior transactions, and other data to conduct accurate, relevant, and enjoyable discussions. 

As such, your conversational AI can communicate with users in a way that is relevant and accurate to them, enabling quick-fixes for customer problems. 

People nowadays have access to an almost infinite number of channels for carrying out their professional and personal demands. Because of this, they often switch between channels if their questions aren’t answered promptly. Your virtual AI assistant should be able to connect with the user via whichever channel they desire and do so quickly. As a result, an omnichannel messaging platform is critical to delivering a great user experience and enabling speedy self-service resolution of customer, agent, and staff support concerns.

As omnichannel is expanding, the virtual AI assistant must be able to function throughout the entire range of channels. This covers workplace messengers such as Skype and Microsoft Teams, social messengers such as Twitter and Facebook Messenger, voice help such as Amazon Alexa and Google Assistant, email, and web/mobile apps. At the core of the omnichannel are NLU and NLP, which ensure that your conversational AI correctly understands and interprets messages and reacts to discussions, regardless of the topic.


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3. No-Code Visual Flow Builder 

Training your conversational AI agent should be simple, intuitive, and painless. That also means no code. To execute this, look for a visual flow builder that adapts to your needs and supports zero-code bot creation, allowing you to design and tweak your bot directly on the platform without any technical knowledge.

Without any coding knowledge, you can now train a virtual AI assistant that begins producing benefits right away. A visual workflow designer enables you to easily view, configure, and update conversational AI flows, add notes, provide context and troubleshoot existing issues. This helps you improve relevance and provide a better end-user experience. 

Machine Learning can also be used by your conversational AI platform to continually enhance its performance and improve your virtual AI assistant’s processes. The assistant may be tweaked and customized to respond to new business trends, initiatives, and consumer feedback.


4. Live Chat

In some situations, customers ask to be transferred to a live agent. As such, your conversational AI solution should enable smooth human transfer at the appropriate time. That would be 1) when the virtual AI assistant can't identify the customer’s intent and 2) when the user wants to communicate with a live representative.

When participating in a discussion, virtual AI assistants require a specific kind of intelligence to assess a developing feeling of urgency or intricacy. This functionality protects the assistant’s value by notifying it when to end the interaction and give it over to a human. When a virtual AI assistant processing an online order, for example, is unable to understand or carry out the request, it may quickly transfer the interaction to a person, preventing aggravation and preserving a pleasant conclusion.

NLP technology enables conversational AI to assess whatever attitude a user is speaking and detect displeasure–in other words, perform sentiment analysis. The call or other channel may then seamlessly link to a live agent for tailored, hands-on assistance and interaction. Even after the agent has engaged, certain virtual AI assistants can continue to assist the process by transmitting background information on the caller's location (even down to the street or ZIP code!). The virtual AI assistant then can also inform the agent of previous transactions and other relevant user data. As a result, conversational AI may continue to function even when not explicitly requested, assisting both the agent and the caller in building a happy, successful experience.


5. Sentiment Analysis

Sentiment analysis is one of the most recent and fascinating conversational AI functions. When consumers are typically short on time (and temper), a virtual AI assistant’s ability to discern the purpose behind a user's inquiry, recognize sentiment from the tone of voice, and answer accordingly is an immensely important talent. Even if the phrase structure, spelling, or grammar are inconsistent, confusing, or informal, such as jargon or slang, the virtual AI assistant can intuit the meaning and improve the user experience.

Conversational AI solutions, like humans, learn rapidly and preserve that information for later use. With each engagement, the virtual AI assistant grows more intelligent, perceptive, and useful. Previously, a standard chatbot could just regurgitate its replies; the capacity to detect client sentiment was speculative at best. However, using today's story mapping technology, conversational AI can identify essential words and assign them a relative value: positive, neutral, or negative. This influences the virtual AI assistant’s "understanding" of an interaction's mood and tone. The solution can then find out how to reply to the user and have a meaningful conversation.


Interested in learning more about conversational AI? Download DRUID’S guide to learn how conversational AI can help improve your customer service–and more. 


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