What’s Next for Conversational AI?

Next generation chatbots are now writing poetry and giving math lessons, but these smart applications have a bigger job to do. Advanced chatbots simulate human interaction via complex artificial intelligence (AI) processes, or conversational AI. As business-ready systems, conversational AI is joining mainstream tech to deliver strategic benefits to customers and employees. For companies looking to adopt or expand their use of conversational AI, there’s quite a bit to understand and consider. 

Now that humans and machines are talking to each other, decision-makers will need clarity around the capabilities—especially as they vet various products and platforms. It helps to start by defining some key terms.

• Artificial intelligence (AI): A wide-ranging category of technology that allows computers to “strive to mimic human intelligence through experience and learning.”^[1] Common AI applications involve analysis of language, imagery, video, and data. 
• Machine learning (ML): In its definition of AI, Gartner cites ML as one of AI’s notable “advanced analysis and logic-based techniques,”^[2] whereby computer systems can learn from their experiences without explicit programming. 
• Natural language processing (NLP) focuses on machine reading comprehension through grammar and context, enabling it to determine the intended meaning of a sentence.  Known for applications such as voice-to-text and language translation, NLP uses AI and often ML to enable a computer to understand spoken or written human language. Natural language generation (NLG) focuses on text generation, or the construction of text in English or other languages, by a machine and based on a given dataset.
• Conversational AI: This advanced application of NLP is what allows people to have a spoken or written conversation with a computer system. At their best, conversational AI systems closely match human conversation—passing a measure called the Turing test.^[3] Here’s how it works from a technical perspective: During the automatic speech recognition (ASR) stage, a person may ask a question and the application converts that audio waveform to text. During the NLP phase, the question is interpreted, and the device generates a smart response. Finally, the text is converted back into audio for the user during the text-to-speech (TTS) stage. 

A Quick Rundown of How Conversational AI Works

Asking a smart phone whether it’s going to rain, telling a virtual assistant to play ’90s hip hop, requesting a navigation system give directions to a new sushi restaurant—each are examples of interacting with conversational AI. By speaking in a normal voice, a person can communicate with a device that understands, finds answers, and replies with natural-sounding speech.

Conversational AI may seem simple to the end user. But the technology behind it is intricate, involving multiple steps, a massive amount of computing power, and computations that occur in less than 300 milliseconds. When an application is presented with a question, the audio waveform is converted to text in what’s known as the automatic speech recognition stage. Using NLP, the question is interpreted and a response is generated. At the next step, called text-to-speech, the text response is converted into speech signals to generate audio. 

Why Customers and Employees Prefer Conversational AI
Most people have experienced the frustration of talking to a legacy chatbot, and perhaps even resorted to anger or shouting “Representatitive!”. But once chatbots are enhanced with conversational AI capabilities, research shows customer satisfaction rates to be three times higher, attributed to shorter wait times and more accurate, consistent customer support.^[4]

For employees, conversational AI can reduce stress and boost productivity by handling most low-level tasks and easing their day-to-day human-machine interactions. This frees up staff for other valuable and higher-level functions, benefiting customers and increasing morale.

Overall, for companies, the benefits may seem obvious: more productive staff and better customer service leading to increased productivity as well as higher customer satisfaction and retention rates. An additional benefit comes from the learning and training of models that continually improve and enhance employee and customer experiences.

Conversational AI in Action, From Retail to Healthcare to Real Estate

In constant search of competitive advantage, companies are increasing their investments in AI to the tune of a projected $204 billion by 2025.^[5] Across industries, the technology promises to deepen customer insights, drive employee efficiency, and accelerate innovation. 

In retail, conversational AI is giving shoppers a streamlined experience with call centers and customer service interactions. As the clunky chatbots of yore are replaced with savvy AI chatbots, customers can quickly get their questions answered, receive product recommendations, find the proper digital channel for their inquiry, or connect with a human service agent. 

In healthcare, applications for conversational AI can support telehealth patient triage to identify potential medical conditions. Systems can also be trained to securely manage patient data—making it easier to access information such as test results or immunization records. And the technology can support patients who are scheduling an appointment, checking on insurance eligibility, or looking for a provider.

In real estate, conversational AI tools are being applied to the time-sensitive lead generation process, automating functions for accuracy and efficiency. Chatbots are also handling initial conversations to assess what a customer is looking to buy or sell. Given AI’s ability to handle thousands of calls per day, a program can be integrated with the customer relationship management system, or CRM, to create more positive experiences.

Five Questions to Ask Before Deploying a Conversational AI System

Once a company is ready to explore a conversational AI project, there will be groundwork. Here are five essential questions—and clues to finding the answers.

1. What kind of hardware do you need? The answer depends on the application scope and throughput needs. Some implementations rely on ML tools and run best on high-performance computing. Others may be more limited in scope. In any case, Dell Technologies Validated Designs offer tested and proven configurations to fit needs based on specific use cases.
2. Which user interface options will your project support? Whether it’s a text-only chatbot or the more user-friendly voice interface, the decision must be based on what’s best for the customer and the budget.
3. What platforms will be supported? Determine how customers might access the chatbot—via mobile app, web, social media—and think about whether to integrate with popular voice assistants. 
4. Will you build your own or rely on a vendor? Doing it in-house requires expertise and time but offers more control. If selecting a vendor, consider whether one vendor or multiple vendors will be needed for the end-to-end system. 
5. What kind of infrastructure will you need? This depends on whether the implementation will be hosted in a private or public cloud service. For those hosting in their own data centers, likely for compliance or security reasons, be sure the vendor’s systems are designed specifically to meet the speed and performance for conversational AI. 

As consumers become more familiar with AI, using it to create art and pay bills and plan their workouts, the technology holds greater professional promise. Conversational AI is already supporting a number of essential business functions—a boon for customers, staff, and the bottom line. Executives can set the foundation for their own advanced chatbots and other applications by ensuring their IT systems are ready for innovation. 

Read the Guide to Conversational AI for Financial Services, and explore AI solutions from Dell Technologies and Intel.