Conversational AI with Human-Like Qualities: Past, Present, and Future
Conversational Artificial Intelligence (AI) is now a part of our daily lives, both at work and at home.
Conversational AI is everywhere, from personal virtual assistants that help us play music and monitor our homes to skilled virtual assistants that help us do our work.
Let’s take a closer look at the history of Conversational AI, where it is now, and where it is headed in the future.
A Quick Overview of Conversational AI
MIT created ELIZA, a precursor to natural language processing, in the 1960s, which sowed the seeds of Conversational AI (NLP). ELIZA was prompted by a user statement and ran on a variety of scripts. One of the most well-known applications of ELIZA was in psychotherapy, where a client would say something like “My mother hates me,” and the technology would answer with something like “Why do you think your mother hates you?” It was convincing enough to pass the Turing exam at the time.
With Shoebox, a voice-activated calculator, IBM, a tech giant and long-time innovator, helped NLP take a move forward. In the 1980s, IBM continued to innovate, creating a voice-activated typewriter with a built-in vocabulary of 20,000 words. With the Simon smartphone, IBM developed the first-ever Virtual Assistant, complete with automated speech recognition technology, nearly a decade later.
Colloquies created the world’s first chatbot, SmarterChild. It used AOL instant messenger to connect and had 30 million “buddies” on the network.
SimpSocial entered the market in 2007 with an AI Assistant designed to assist car dealerships in motivating online leads to visit the store. SimpSocial has since extended the use cases for its Conversational AI technology to assist all revenue-generating teams across sectors, based on how effective it was in speeding up transactions.
The number of households and companies using personal AI assistants including Google Home and Amazon Alexa increased dramatically in the 2010s. Voice-activated assistants are used for a wide range of tasks. These tasks include using home computers, conducting web searches, and shopping online.
Today’s Most Popular Conversational AI Use Cases
Today, customer-facing business teams use Conversational AI in the form of AI Assistants to engage contacts in customized conversations at scale.
AI Assistants are virtual team members who assist business professionals in completing tasks. As a result, whereas AI Assistants automate routine activities, business professionals may concentrate on higher-value tasks that involve creative thinking and problem solving, which only humans are capable of.
Different teams have different use cases for AI Assistants. Marketing, for example, uses AI Assistants to give each incoming lead a personalized touch to determine the leads are “sales-ready” and which need more time. Customer Success teams, on the other hand, use AI Assistants to communicate with their clients on a daily basis. This makes it easier for Customer Success Managers to plan crucial meetings, collect feedback, track customer health, and provide growth opportunities.
Another common form of Conversational AI is chatbots. Website chat facilitates the exchange of information between website users and the information they need. This involves answering commonly asked questions, directing visitors to specific web sites, and assisting with problem resolution.
Website chat is very common on ecommerce sites and for customer service. Marketers have recently adopted website chat to gather lead information, identify sales-ready leads, and direct visitors to useful resources such as customer case studies.
There is, admittedly, a distinction between simple chatbots and Conversational AI-powered website chat, both in terms of construction and function. Chatbots are simple rule-based systems. Certain words are recognized, and phrases are matched to a pre-programmed answer. They are unable to simulate two-way conversations, and their applications are fairly limited—which is perfect if that is what you need.
However, with a more advanced AI approach, many businesses and consumers enjoy two-way conversations. Although these AI are more advanced than bots, they are often referred to as personality chatbots.
Website visitors can have real-time conversations with AI-driven website chat. AI Assistants, on the other hand, can communicate through a variety of networks, including email and SMS text messaging. Although these exchanges can take place in real time, AI Assistants can carry on the conversation for days or even weeks. This level of commitment is required to promote contact with leads or clients in order to help inspire them to take the next best step, such as arranging a meeting with your Sales or Customer Success teams.
Conversational AI is popular with customers because it is timely, persistent, and courteous. They don’t know they’re talking to an AI in certain situations, thinking they’re talking to a real human.
The Future of Conversational AI
Although no one can predict the future with absolute certainty, the trend toward more sophisticated Conversational AI and more use cases is likely to continue.
Conversational AI’s number of available channels is expected to grow in the near future. If the popularity of social selling grows, so will the popularity of social-selling AI Assistants. Similarly, Conversational AI that is triggered by voice can improve its ability to understand and respond to users in normal, two-way conversations.
It’s worth noting that a lot of Conversational AI solutions use Machine Learning, which helps to improve accuracy and capabilities over time. As a result, newcomers to Conversational AI will, on average, have less history to draw on than a legacy provider. Conversational AI can make its way into new business teams as a result of increased learning and creativity.
It’s likely that Conversational AI could evolve from AI Assistants, which assist employees in automating routine tasks to increase productivity, to AI Advisors, which assist employees in making strategic decisions based on the AI’s ability to process vast volumes of data rapidly.
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