Conversational artificial intelligence refers to chatbots and virtual agents that can handle requests from customers. The technology includes automated messaging (texts, in-app, chat, etc.) and speech-enabled applications (such as Siri and Alexa).
These experiences don’t always meet consumer expectations. That’s a serious issue considering that companies say they plan to double AI spending by 2024 and consumers say that they will not accept poor customer service.
When done extraordinarily well, conversational AI can mimic natural language so effectively that customers cannot tell the difference between interacting with a real human agent and artificial intelligence.
Most people have already encountered some type of AI-assisted experience, such as chatbots and virtual assistants, including Siri and Alexa.
Artificial intelligence has already accomplished a lot for companies, including healthcare organizations, e-commerce stores, and omnichannel marketers.
Honestly, though, we’ve just scratched the service. The future of AI technology will use deep learning to improve natural language processing (NLP), natural language understanding (NLU), automation, AI chatbots, and automatic speech recognition (ASR).
Ideally, these improvements in AI technology will improve customer satisfaction while helping companies save money.
Instead of waiting on hold for 30 minutes to talk to a human agent, you could get human-like interactions from virtual agents, real-time text messages, and guided self-service bots.
Once this happens, consumers can have conversational experiences 24/7 without forcing companies to pay for 24/7 contact centers.
You have certainly noticed that some AI tools offer better user experiences than others.
That’s because some AI platforms do much better jobs learning from their interactions with humans.
Conversational AI can only succeed when AI solutions have machine learning and algorithms that adjust quickly to new information.
Conversational AI relies on a multi-stage process and several pieces of technology that work together to provide a seamless customer experience. Imagine someone using a conversational AI chatbot to retrieve a forgotten password.
During the first stage, the person tells the chatbot that they forgot their password. The conversational AI system uses automated speech recognition and natural language understanding simultaneously to hear and comprehend what the person said.
In the second stage, the system’s dialog management tool will form a response.
Third stage, dialog management sends information to the natural language general software to create a response that sounds natural to the customer.
It then posts a response that says something like, “No problem. I just need you to answer a couple of questions. Are you ready?”
Human language evolves, so conversational AI must adjust to emerging trends in speech. Customer interactions a decade from now will probably look and sound quite different from today’s interactions.
Excellent customer support might need machine learning and algorithms that can understand the meanings of new words and anticipate what consumers want when they use them.
Even changing accents could make it difficult for artificial intelligence to understand human language. Machine learning will need to notice those differences and update algorithms to improve customer engagement.
Much like the human brain, these technologies learn from new information in their environments.
They adjust, make changes, and keep up with the humans interacting with them. Software developers might need to step in from time to time to adjust the software. Overall, though, a terrific AI system should learn and grow without much help.
It might sound easy enough to give AI solutions well-researched dictionaries and let them go to work. Unfortunately, it doesn’t work that way.
A simple text-to-speech app, for example, cannot recognize tones of voice. Partially functional AI might assume that someone who says, “Yeah, that’s just what I needed,” is a happy customer.
More robust conversational AI, however, might identify a sarcastic tone in that statement. The tone of voice will tell it that the customer’s words conflict with their feelings.
Other challenges to Conversational AI include:
A genuinely successful AI assistant will need to understand these challenges and find ways to overcome them.
We imagine a near future where conversational AI applications improve social media interactions, give people the services they need without interacting with human agents, streamline workflows, and gently nudge consumers along the customer journey.
Anyone who works with emerging technologies, though, will worry about conversational AI’s barriers to success.
Potential barriers go far beyond the challenges listed above. For example, what happens when a frustrated customer keeps asking an AI tool the same question without getting a satisfactory answer?
What happens when a non-native speaker gets stuck using an AI solution that can’t explain its questions well?
Strategies that could make these challenges less detrimental to your business:
It could take decades before voice assistants and other AI interfaces can consistently replicate the experience of interacting with another human.
Until that day comes, companies should keep a few human agents on call to handle extraordinary circumstances.
For example, when an AI chatbot fails to answer a customer’s question two times in a row, it can escalate the call and pass it to a human operator.
Most of the time, the chatbots will work fine, so you won’t need nearly as many call agents working outside of business hours.
Still, it makes sense to have some people available for those rare occasions.
Some AI interactions might fail for unknown reasons. With enough background noise, even a human agent can’t understand what someone is saying.
Other instances will make it obvious that your AI solution needs additional training.
If you see that a high percentage of calls get escalated because the AI assistant did not understand the meaning of a word, you can add that word to its knowledge base.
If your company expands into a new area and your AI assistants don’t understand the local dialect, you can use new inputs to teach the tool to adjust.
Always, keep working with partners that understand the technology and your end goals to keep conversational AI working for you.
Conversational AI has many obvious benefits. The technology still has plenty of room to grow as developers hone their machine learning algorithms and explore ways for programs to coordinate their efforts.
Whether you need an expert to choose an AI platform that fits your company’s needs, or you need someone to build unique software solution based on Conversational AI for your system, reach out to our professionals.
Start leveraging the latest advances in Conversational AI.