AI Virtual Assistants: 4 Things You Need To Know
There are many modes of communication today, but conversation is the most natural for humans. It’s no wonder that technologies in all industries are evolving to incorporate some degree of natural language processing (NLP). From navigation apps built into cars to remote control devices for media streaming to voice recognition for security in mobile and desktop apps, we are primed to speak and be understood - and the market agrees. The global NLP market is projected to grow from $20.98 billion in 2021 to $127.26 billion in 2028 at a CAGR of 29.4% in that forecast period. On the frontline of the NLP movement stands the eager AI virtual assistant, its hand outstretched to help businesses and their customers. The question is, are these helpers really up to the task?
Why AI virtual assistants now?
There are several reasons why the time is right for AI virtual assistants — alternately called conversational assistants, digital assistants, conversational interfaces and chatbots:
Improved speech technology algorithm development and machine learning
Greater computing power
Access to more and increasingly diverse sample data
New proprietary and open-source initiatives
Armed with these advances, AI virtual assistants are anxious to help, and show us what they can do to improve the user experience, as they interact more naturally with people at different touchpoints in the customer journey. The personal assistant technology has enabled these assistants to become a more integrated member of the customer service team during this historic time where AI virtual assistants adoption has accelerated during the COVID pandemic. In fact, during the pandemic, call center traffic surged by 600% in certain instances.
Without the use of bots, firms are forced to choose between hiring customer service reps (CSRs), or have customers wait longer for service. In addition, voicebots offer their services 24/7/365, and save companies money while doing so. These AI personal assistants never tire, and free up CSRs to handle higher-value, more complex queries.
Four best practices for building AI virtual assistants
Development organizations must plan, design, model and test. They must understand that NLP products require more iteration than non-voice products because designing for conversations is more complex than, for example, a web design. Developing for voice requires different mindsets, skill sets and toolsets. A focus in the following areas is required for successful AI virtual assistants deployment.
Plan - What do users want and what do they really need? How useful is the conversational product you’re planning to build relative to those wants and needs? Perhaps more important, how is the task better solved with a conversational approach compared to other channels? You don’t want to attempt to solve for something that could have more easily been achieved using a different channel. There are like questions on the business side. How do AI virtual assistants fit into the larger business ecosystem? What technology platforms should you evaluate for your conversational tech stack?
Design - The intricacies of human verbal interaction make designing conversations with AI virtual assistants very different from UX design for web or mobile. Designers take the user context, the user needs and the bot’s capabilities into account, and create sample dialogues based on that information. Once they’ve designed a happy path based on these dialogues, they refine, add variety and repair techniques to account for the diversity and errors that naturally occur in human dialogue. Designers work very closely with copywriters. They fashion a persona with a certain tone, style, gender, voice pitch and speed of talking so that it resonates with the audience and accurately represents your brand.
Training data for modeling - Building models requires specialized tools and knowledge. To begin with, a language model is only as good as the data that goes into it. It’s key to collect data that represents real user interactions in real environments. Ensure that you actively identify all possible biases — as a vast majority of AI projects deliver erroneous outcomes due to bias — and then gather data to counteract this.
Test and get continuous feedback - Testing examines many elements of the AI conversational assistant’s performance. For example, is the model you’ve built accurate? Does your system correctly recognize what the user says? From a functional testing perspective, is the correct next step taken based on what the user said in the interaction? Successful conversational assistants require ongoing feedback. Organizations must monitor the app, do regular testing, review analytics and improve the assistant based on the captured data.
As unstructured data grows, AI virtual assistants and the NLP technology that supports them continue to evolve to better understand the nuances, context, and ambiguities of human language.