People expect on-demand access to information in an always-on, always-connected world. Whether they’re reaching out to a customer service team for help, looking for product information, or seeking guidance on their next task at work, they don’t want to wait.
Humans aren’t always available to provide information or resolve issues, but conversational artificial intelligence (AI) is. These solutions can work 24/7/365 to provide answers and point people in the right direction. The best conversational AI solutions can make those interactions feel just like a discussion with another person.
Why is Conversational AI Important?
Most consumers now use multiple platforms to engage with businesses. Phone and email support are no longer the only options –– and most people look for digital options. Statista reports that 88% of consumers around the world expect a business to have an online self-service portal.
Conversational AI can give a company the option for employees or customers to connect through an e-commerce platform, social media chat, Software as a Service (SaaS) application, mobile app, or by clicking a text link. This technology meets users’ demands for speed, autonomy, and instant gratification without the expense of hiring additional staff.
However, there are other reasons that the conversational AI market is growing at a phenomenal 23.6% CAGR from 2022 to 2030. Conversational AI can play a role in helping people work more effectively and efficiently. Conversational AI platforms can field simple requests, allowing agents to focus on complex problems that need their attention rather than spend time checking orders or account status or directing a warehouse picker to the next job. Moreover, conversational AI not only handles straightforward requests but also can address multiple interactions at once, increasing throughput, efficiency, and user satisfaction.
What Exactly Is Conversational AI?
Conversational AI platforms are designed to simulate human conversation. These platforms understand the meanings of words and phrases and can discern a person’s intent and sentiment so their responses are relevant and seem natural to users.
Many conversational AI solutions use a voice user interface (VUI), which is the most natural means of communication. People can use conversational AI faster than typing queries or selecting buttons on a graphical user interface. They just speak to use a conversational interface.
How Does it Differ From a Chatbot?
A chatbot is a text-based conversational interface. Some chatbots use conversational AI. However, many of the chatbots that companies have used for years are rules-based, working on an if-this-then-that principle. Those chatbots deliver outputs based on inputs, but they aren’t able to create a give-and-take conversation.
Conversational AI, on the other hand, allows users to ask questions in natural language, receive answers, and ask follow-up questions for more details –– in a conversational style.
How Are Conversational AI Systems Created?
Conversational AI engineers break down human language so a machine can understand it. They create tools that use representations of language, such as speech to text ,natural language processing, mark-up language, or other means. They also work with conversation designers to create conversational interfaces and other elements of user experiences, such as branding a voice assistant with accents or tone of speech and how dialogs flow.
Training the model is also critical to how it performs. Skilled data annotators must create datasets that include different variations of speech and language. For example, a conversational AI platform used for restaurant ordering needs to understand that a customer may place an order by saying, “I want a pizza with ham and extra pineapple,” and another may say, “Pineapple ham pizza, please,” and in both customers are asking for the same order. Training data for conversational AI typically uses hundreds of thousands of data points to prepare a model for real-world use.
How Does Natural Language Processing (NLP) Tie In?
AI project teams must allow the platform to convert unstructured audio data into a workable form. Natural language processing (NLP) combines rules-based models with machine learning (ML)to convert spoken language into usable data. One of the challenges is the variability of human speech. NLP identifies synonyms, compensates for grammar errors and interprets sentiment, so the machine can understand what the user is trying to communicate.
Conversational AI also requires natural language understanding (NLU), which allows a solution to discern the intent and sentiment behind a query. Then, the platform uses natural language generation (NLG) to create a response.
The Steps of Conversational AI
Generally, a conversational AI platform’s workflow follows these steps:
1. The user asks a question, such as, “Can I go right to a lab for my blood test?”
2. Natural language processing formats the unstructured text or audio data so the model can process it. This step may include language translation.
3. Natural language understanding (NLU) helps the model interpret the meaning of the words, for example, whether a user means “right,” the direction, or “right,” as in straight away. This step may include disambiguation in which the model will ask the user a question, such as, “Did you mean go directly to the lab?”
4. Next, the conversational AI platform will use data from the query to create an output much like the answer a human would give. In this case, the response may be, “The lab is open until 4:30 p.m. today.”
5 Ways Businesses Can Use Conversational AI in Their Operations
When you think of conversational AI, you may immediately associate it with voice assistants like Amazon’s Alexa. But businesses don’t have to be e-commerce giants or large enterprises to benefit from this technology. Some examples of how conversational AI are providing value to businesses of all sizes today include:
Retail customer service
Conversational AI platforms can work around the clock every day of the year to provide fast, efficient service. They can also overcome language barriers more easily than human customer service agents can. Additionally, they can collect information from customers and display it on a customer service agent’s screen along with related information from a knowledge base to make person-to-person interactions faster and more efficient.
Conversational AI can also enhance interactive voice recognition (IVR) systems to route calls and collect information more intelligently. This technology can even conduct surveys to collect customer feedback.
Supply chain workflows
Conversational AI can enhance voice picking in warehouses. Employees can use a voice interface to ask for their next task, and the platform can respond by directing them through the order down to bin numbers. This system improves accuracy, efficiency, and safety as pickers receive orders over a headset instead of on a tablet.
Healthcare scheduling
This technology allows patients to quickly and easily schedule healthcare appointments. Conversational AI can also capture information from the patient that can help doctors prepare for the appointment without requiring intervention from a nurse or medical assistant.
Banking virtual assistants
Conversational AI platforms trained for financial institutions allow users to check balances and find information. It can also detect phrases or keywords that could indicate fraud, helping to decrease losses. Additionally, when data is communicated to an AI model versus humans, fewer people within the organization access sensitive information, helping to protect users’ privacy.
Sales and marketing
Sales teams can add conversational AI to their tool chests, allowing the platform to make cold calls or follow-ups and collect information for a hand-off to a rep. Conversational AI can also be trained to sell, guiding potential customers on a buying journey and completing a transaction, including payment.
Fielding real estate inquiries.
Real estate agents can benefit from conversational AI that fields inquiries and provides people in the market with information on available properties. When the platform detects real interest, it can connect the prospective buyer with an agent.
What’s the Future of Conversational AI?
Servion Global Solutions research found that by 2025, 95% of customer service and support interactions will be automated. Moreover, more of those interactions will involve voice interfaces in the coming years, perhaps one day making the keyboard and mouse obsolete.
In addition, conversational AI will advance to become more like actual conversation. And as AI platforms grow more intelligent, they can surpass human capabilities. In the future, robots leveraging conversational AI may assist employees in their jobs, provide “in-person” service, or engage patients in personal care homes.
Conversational AI Is Reshaping Our Interactions with Businesses and Each Other
As people rely on automation to increase productivity at work and in their day-to-day lives, advances in conversational AI will enable automating interactions that don’t necessarily require a human touch. Conversational AI allows people to speak their queries rather than type them into a search bar, saving time and enabling the most natural interaction with a computer. Furthermore, as conversational AI advances, machines can be more active participants in the exchange, asking questions that help refine intent and even guide the user as they seek information or a resolution.
Conversational AI will become more prevalent –– and more of a consumer expectation. Businesses focused on growth should explore how conversational AI can help their operations today and how it can help them stay competitive in the future. Contact us to learn more.