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AI Virtual Assistants: 4 Things You Need to Know

This blog is an abbreviated version of our video and Ebook – made in cooperation with outriderUX – on the topic of AI virtual assistants.

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 in-car navigation apps to remote control devices for media streaming to voice recognition for security in mobile and desktop apps, we’re ready to talk and be understood – and the market is growing. deal. The global NLP market is expected to grow from USD 20.98 billion in 2021 to USD 127.26 billion in 2028 at a CAGR of 29.4% during this forecast period. At the forefront of the NLP movement is the enthusiastic AI virtual assistant, his hand outstretched to help businesses and their customers. The question is, are these assistants really up to the task?

Why AI virtual assistants now?

There are several reasons why now is the time for AI virtual assistants – alternatively known as conversational assistants, digital assistants, conversational interfaces and chatbots:

  • Improved development of voice technology algorithms and machine learning

  • Greater computing power

  • Access to increasingly diverse sample data

  • New proprietary and open source initiatives

Armed with these advancements, AI virtual assistants are eager to help out and show us what they can do to improve user experience as they interact more naturally with people at different touchpoints in the customer journey. Personal assistant technology has enabled these assistants to become a more integrated member of the customer service team during this historic time when the adoption of AI virtual assistants has accelerated during the COVID pandemic. In fact, during the pandemic, call center traffic has jumped 600% in some cases.

Without the use of bots, businesses are forced to choose between hiring customer service representatives (CSRs) or making customers wait longer for service. Moreover, voice robots offer their services 24/7/365, which saves companies money. These AI personal assistants never get tired and free up CSRs to handle more complex and higher value queries.

Four best practices for creating AI virtual assistants

Development organizations need to plan, design, model, and test. They should understand that NLP products require more iterations than non-voice products because designing for conversations is more complex than, say, web design. Developing for voice requires different mindsets, skills and tools. A concentration in the following areas is necessary for a successful deployment of AI virtual assistants.

  1. Plan – What do users want and what do they really need? How useful is the conversational product you plan to create in relation to these wants and needs? Perhaps more importantly, how is the task better solved with a conversational approach compared to other channels? You don’t want to try to solve something that could have been done more easily by using a different channel. There are like questions on the business side. How do AI virtual assistants fit into the larger business ecosystem? Which technology platforms should you evaluate for your conversational technology stack?

  2. Design – The intricacies of human verbal interaction make designing conversations with AI virtual assistants very different from designing UX for web or mobile. Designers consider user context, user needs, and bot capabilities, and create sample dialogs based on this information. Once they design a happy path based on these dialogues, they refine, add variety, and repair the techniques to account for the diversity and errors that naturally occur in human dialogue. Designers work closely with editors. They shape a character with a specific tone, style, gender, voice pitch, and speaking speed so that it resonates with audiences and accurately represents your brand.

  3. Training data for modeling – Building models requires specialized tools and knowledge. To begin with, a language model is only as good as the data it contains. It is essential to collect data that represents real user interactions in real environments. Be sure to actively identify all possible biases – as the vast majority of AI projects produce erroneous results due to bias – and then collect data to counter this.

  4. Test and get continuous feedback – The tests examine many elements of the performance of the AI ​​conversational assistant. For example, is the model you built accurate? Does your system correctly recognize what the user is saying? From a functional testing perspective, is the correct next step taken based on what the user said in the interaction? Successful conversational assistants require continuous feedback. Organizations should monitor the application, perform regular testing, review analytics, and improve the assistant based on 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.

Read our ebook, co-authored with outriderUX, an Applause partner, to learn more about AI virtual assistants. We also have a Q&A video on this topic.

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