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Conversational AI chatbots: 3 myths, busted

Concept for an AI Chatbot smart digital customer care application. Artificial intelligence chatbot program on a computer or mobile automatic responds to online messages to assist clients immediately.

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Nowadays, conversational artificial intelligence (AI) chatbots are everywhere online, SMS and social channels. Conversational AI-supported chatbots that use natural language processing (NLP) help customers cope with from product recommendations to order questions.

Enterprises love conversational AI chatbots, too: In accordance with a recent Gartner report, by 2027 chatbots can be the primary customer support channel for roughly 25 % of organizations. Over half (54%) of survey respondents said they’re already with a couple type of chatbot, virtual customer assistant (VCA) or other conversational AI platform for customer-facing applications.

In accordance with Susan Hura, chief design officer at, chatbots arent all-knowing virtual assistants living on an internet site that are prepared to answer every question at a moments notice. While integrating a conversational AI-supported chatbot might seem fast and simple, you can find complex intricacies beneath the hood. A chatbots design, she explained, plays a far more strategic role than one might think and requires an immense quantity of human input to generate.

Designing the conversational AI experience

Orlando, Florida-based was cited in Gartners 2022 Magic Quadrant for Enterprise Conversational AI Platforms as supplying a no-code platform for conversational AI in an easy sense, crossing over into adjacent product categories with interface and process building capabilities. Essentially, the business develops conversational bots for enterprises across different channels, from traditional web chatbots and SMS bots to bots in Facebook Messenger and WhatsApp and voice-enabled bots.


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Hura joined the business in March to create out a specialist design practice for the business.

Although it is really a do-it-yourself platform, for most of our enterprise-level customers a specialist team will come in to greatly help define the framework for the bot or this suite of bots they develop, she said.

You can find five conversation designers on her behalf team who define what the bot says to an individual and develop the structure of the conversation. Additionally, she explained there are seven natural language analysts define the way the bot listens and interprets what an individual says.

Both of these together really form the conversational experience that someone could have interacting with one of these brilliant bots, she said.

Hura, who includes a Ph.D. in linguistics and began employed in speech technology while working at Bell Labs, which she noted, was literally because I was sitting close to visual designers who have been focusing on a speech technology project. Hura said there are numerous misconceptions about conversational AI chatbots. From this backdrop are three myths that she says have to be busted.

Myth 1: Conversational AI chatbots are magic

Truth: It requires effort and time to create successful chatbots.

Hura said she still sees enterprise customers surprised with what conversational AI chatbots cannot do.

I believe its partly because theres still a lot of salespeople and folks in the media who portray conversational AI as though its magic, she said. As though simply by designing a conversational bot, all of your dreams should come true.

However, exactly like any technology, organizations need to invest enough time to be able to teach the bots to accomplish the things they need it to accomplish.

You’ll never expect a human who was simply likely to be filling the role of a va to just automatically know everything and also have all the details they want, she explained.

That’s where its vital that you recognize that understanding is often a human word, she added. I believe when people hear what natural language understanding they believe the technology is founded on meaning when, actually, its not.

Actually, she explained, conversational AI technology is founded on language. The bot is merely producing output predicated on its analysis of all input you placed into it, she said. The higher structured that data is, the more intelligent a bot will sound.

Myth 2: Conversational AI chatbots understand users

Truth: Chatbots need context.

Imagine a user is on a webpage getting together with a conversational AI chatbot. An individual says, it looks like there exists a duplicate charge online three. The simple truth is, line three means nothing to a bot, Hura emphasized.

The bot is sitting there on the site, however the bot does not have any knowledge of whats happening in the context where the user is seeing it, she said. So people frequently have misaligned expectations round the context useful.

So, for example, in case a customer is searching for something and wants something comparison, a bot would need to be trained not only with something comparison chart but with all the current data that has been used to create that chart.

The bot won’t be any smarter than your site, Hura explained. The conversational AI-supported bot cant answer a nuanced question if it needs more data than can be acquired. It could only response to the extent youve provided the info.

Chatbots additionally require the context of the conversation itself.

Sometimes those perceptions drop to the bots capability to speak in a manner that knows the context of the conversation itself, she said.

For instance, if the bot asked an individual for a bit of information like, What’s your account number? then your following question may be What’s your password? If the bot asked As well as your password? instead, it could feel more natural, said Hura.

Thats just how a human would say it, she explained. The term and in addition does a huge amount of work in the conversation this implies Ive heard your answer and am following up with another question, it feels as though the bot knows whats happening.

Myth 3: Chatbots don’t need design

Truth: Conversational AI chatbot design is really as important as UX product design.

Hura said chatbot design is focused on user experience (UX) design. On my team, we practice something called user-centric design having an iterative process, said Hura. As were taking into consideration the framework for conversations between a bot and a user, the more we realize concerning the user who they’re, what their expectations are, what their relationship has been the company the higher.

The vital thing Huras team does is create a conversational style guide, like the style guides created when creating a mobile app, website or software application. We define the sound and believe that we wish this bot to possess, she explained. Its a great and unique thing that defines the personality of the bot.

A script defines what the bot says, while flowchart-type diagrams map out all of the possible paths that the bot could decrease.

For example, for a credit card applicatoin where in fact the user calls to produce a service appointment for his or her car. The business must collect the automobile year, make and model.

If an individual says early in the conversation I have to bring my Corolla set for an oil change, I dont need to ask for the entire year, make and model because I know a Corolla is really a Toyota, she said. But we build flowcharts to make certain that the bot gets the right words to state in virtually any possible situation we may encounter.

Conversational AI builds customer relationships

Overall, Hura explained that conversations are techniques people build and reinforce relationships including with chatbots.

We make judgments about whoever were speaking with, more than merely they gave a precise answer, she said. And we assign bots personality, even though were 100% clear its a bot.

Thats why making certain conversational AI chatbots have the proper design is indeed important, she added.

Organizations should take time to control that and be sure that the bots speak in a manner that reflects your brand value, she said.

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