Ask anyone to consider what comes to mind when they think about “AI”, and “chatbot” is likely to be high on the list.
Emerging as one of the most hotly debated forms of CX automation, chatbots are changing customer service and support. Indeed, 67% of decision makers now say their companies use chatbots, compared to only 23% in 2018.
Chatbots are also often the first concept that springs to mind when discussing “conversational AI” – the ability of machines to interact with human beings. However, the first bot models to emerge on the market failed to demonstrate the full potential of conversational AI.
Fortunately, the technology has come a long way, and the evolution of machine learning (ML), natural language understanding (NLU), and natural language processing (NLP), has begun to pave the way for a new generation of powerful customer support tools.
The Birth of Conversational AI: First Gen Chatbots
When chatbots first entered the CX space, many were advertised as a powerful, AI-driven solution for customer service. However, the reality was many of these basic tools only contained small amounts of AI. They relied on simplistic NLP models to uncover customer intent, then churn out scripted answers in response to recognisable keywords.
Unfortunately, the result was little more than a fancy FAQ engine which failed to capture the hearts of users and consumers alike. In fact, 54% of US online consumers surveyed by Forrester said they believed interacting with a chatbot “negatively impacted their quality of life.”
Yet, even when upgraded chatbot solutions began to emerge, many businesses still steered clear. Why? Because their sophisticated models required teams of designers and developers, computational linguistic specialists, and experts in knowledge management.
The expense of creating a custom chatbot, combined with the negative perception among consumers of these tools prompted many companies to explore alternative routes.
The Rise of a New Age of Chatbots
As the marketplace continued to evolve, and consumers began to demand more convenient, personalised, and meaningful experiences from companies, investment in new strategies for strengthening the potential of chatbots increased. Advancements in NLP, NLU, ML, and robotic process automation (RPA) brought new capabilities to the chatbot landscape. Basic FAQ-style bots transformed into emotionally driven, intuitive tools.
Today’s chatbots have grown more intelligent, and more capable of achieving a wide range of tasks on the behalf of consumers. These tools – paired with a health flow of data - can essentially “think” for themselves, to autonomously resolve requests, sustain employee productivity, and enhance the experiences of customers with creative solutions to problems.
Even better, the rapid acceleration of the digital and technology landscapes has made intelligent chatbots easier to access. No-code and low-code tools now allow businesses to build their own conversational intelligence systems without the help of programming specialists. Virtually every business can welcome its own smart bot into the workplace.
In addition, one of the biggest developments has been in the democratisation of conversational AI – ie in addition to the low-code/no-code tools, the cost of the technology is also now much more affordable. What was once available to large enterprises in terms of cost profile and the skillset needed is now becoming more mainstream and mass-market.
Stuart Dorman, Chief Innovation Officer at Sabio, said: “Substantial advances in the use of machine learning have led to developments in conversational AI which underpin intelligent chatbots. Looking forward, we expect this to develop further through Foundational AI models touted for broader commercial use. Although those models are in the early stages of development, we expect them to drive the next wave of innovation in the conversational AI landscape.”
What Can Chatbots Do Today?
The highly scripted and restricted robotic chatbots introduced at the beginning of the CX revolution often proved unable to effectively predict user intent or engage in meaningful dialogue. This meant most conversations between machines and humans were frustrating, impersonal, and exhausting affairs.
However, bringing more advanced AI concepts into the chatbot landscape has solved a number of these problems. Today’s bots can do a lot more than simply regurgitate FAQ responses to customers on a website browser. They can respond to natural human voice, detect emotion, and sentiment in a client’s tone, and kickstart automated workflows, without human input.