Thanks to recent technological advances, we can talk to AI on computers, phones, and sites. “Talk to a robot” is no longer a line reserved for sci-fi movies. Market analysts value the global conversational AI market at $4.8 billion, and expect it to grow to $13,9 billion by 2025.
All over the globe, software agents based on conversational artificial intelligence are replacing human customer service reps. They have become an increasingly popular means of communicating with customers and providing real-time customer service. The adaptation of conversational AI across different industries has massive implications for human-computer interactions online.
What is Conversational AI?
Conversational AI is a set of technologies that enable human-like communication between humans and computers. It makes use of language technologies, such as speech recognition, machine learning (ML), and natural language processing, to process and contextualize written or spoken words and determine what is the best way to interact with users.
Conversational AI is behind the most advanced chatbots and virtual assistants. For instance, Google Now, Apple’s Siri, and Microsoft’s Cortana are just a few of many competing platforms for conversational AI.
Thanks to conversational AI, chatbots and virtual assistants can understand and respond to text or voice inputs in a way that seems natural to humans. Conversational AI allows us to interact with complex systems in an easier and faster way.
Because of this, businesses use conversational AI platforms to deliver customer support and personalized engagements. Conversational AI helps them improve the overall customer experience.
When it comes to human-computer interaction (HCI), conversational AI is a tectonic move forward. The ability to converse with machines is a huge step forward from the on-screen command-based interactions we have been using since personal computers became a thing.
However, now that we can conversationally interact with AI-powered assistants on our devices, user expectations are growing exponentially. As conversational AI platforms are becoming more popular, user frustrations levels are rising.
Conversational AI is seeing great success in applications like smart devices and customer support, and this success is precisely the cause of overly high expectations.
Since conversational AI platforms can respond with human-like quickness via text or voice, many people believe chatbots and virtual assistants can participate in unbound, back-and-forth exchanges.
But, even though you can ask Cortana about the weather forecast, if you follow it with, “should I wear rain boots” you won’t get the answer you want. Moreover, people identify with and personify virtual assistants, so it’s easy to see why these interactions can often lead to disappointment.
When it comes to sensing and responding with emotion, virtual assistants and chatbots have a long way to go in meeting user expectations, even though there has been a lot of progress in this area. Users aren’t surprised when a virtual assistant shows empathy—they expect it.
But, conversational AI hasn’t yet made great strides in natural language understanding (NLU) as it has in natural language processing. Engineers mostly still use “if-then” rules to script VA and chatbot responses. Anticipating every way a conversation can go is difficult, and if the engineer that has designed the conversational AI leaves something out, there is a higher chance that the system will respond with “I don’t understand.”
Because many virtual assistants and chatbots cannot follow unpredicted paths, they can still come off as too mechanical. To combat this issue, engineers are working on highly context aware conversational AI that can connect the dots of what was said before in a conversation. For instance, the startup Posh has designed an AI-powered system that uses “conversational memory” and the most relevant data, making their bots even more human-like.
However, conversation AI will need to learn a lot more about each user in order to improve in this area, so it will require a lot more data. We are yet to see whether users will be willing to give conversational AI unfettered access to their private information.
Managing User Expectations
As mentioned, the use of conversational AI in customer service is thriving. It is fundamentally changing the way users interact with organizations online, and it is generally changing it for the better. Thanks to chatbots, many customers have become used to getting instant customer service 24/7.
Chatbots thrive when it comes to specific use cases, such as helping clients apply for a loan, book a flight, or schedule an appointment, but they have limitations in other areas. Businesses themselves feed the hype around chatbots by marketing almost every solution as “AI-enabled.” They create the impression that the most recent advances in conversational AI are applicable to most chatbot solutions, when they are in fact not.
They are doing a huge disservice to their customers and themselves. When companies introduce chatbots to their customers, they often lean on wishful thinking instead of focusing on managing expectations.
Customer-facing departments that utilize chatbots are at the forefront of the HCI revolution, and it’s their responsibility to teach customers how to use them. If you have a business that is considering implementing or has implemented a conversational AI platform, here is what you can do:
- Set expectations during onboarding. Naturally, you know why you have started using a conversational AI solution for your customer service, but it may not be that obvious to your customers. To ensure customer satisfaction, you need proper user onboarding.
- Promote clarity during the process and help customers understand how to use your new chatbot. Explain to them how the interaction should work. For instance, can they use a menu with different options or are they supposed to type text only? Make sure they have a clear understanding of how they can interact with the chatbot from the beginning.
- Guide them to discover features. Your customers will be more inclined to continue using your chatbot if they can quickly understand what your new conversational AI solution does and how it can help them complete a task or solve an issue.
- Demonstrate value right away. Virtual assistants and chatbots must be useful, they are not supposed to be just entertaining.
- Don’t lead your customers to think your chatbot is human. There are still many companies that present their chatbots as human, but this is definitely a poor practice. By doing so, they set customer expectations too high. As a result, customers may feel like they’ve been fooled and end up feeling frustrated.
- Take responsibility. When your chatbot gets stuck, don’t let the users feel like it’s their fault. When the chatbot cannot help the user with something, it should let the user know it realizes its inability to help. For instance, it can send a message saying, “Sorry, I am still learning how to answer that. Would you like to hear a joke instead?”
In order for your chatbot to be well-received among customers, you will continually need to collect, process, analyze, and act on user preferences. Analytical processes allow you to grow your business, and they should encompass your new conversational AI solution just as they do other marketing metrics.
As more people are interacting with conversational AI, user expectations are growing. The way users interact with chatbots and virtual assistants usually depends on how they perceive conversational AI and its capabilities.
Organizations that utilize conversational AI to improve the customer experience should also make an effort to teach users how to interact with their chatbots and what to expect.
About the Author
I’m Rebecca, a translator and avid traveler, book worm, and horror flick enthusiast. My job has given me the amazing opportunity to travel to dozens of countries around the world, and writing on Rough Draft gives me a chance to try to showcase some of them.