Gartner predicts that conversational AI will reduce contact center agent labor costs by $80 billion by 2026.
Market expectations for conversational AI are high. One market research report last year estimated that the global conversational AI market size would reach $32.62 Billion by 2030, at a Compound Annual Growth Rate (CAGR) of 20.0% through 2030. That report noted that there a variety of technologies (machine learning, deep learning, NLP, and automated speech recognition) are helping make conversational AI use more common in industries including financial services, retail, eCommerce, healthcare, and more.
That report also noted use of conversational AI would rise due to an increase in demand for AI-based chatbot solutions and artificial intelligence-powered customer support services. In particular, the report noted that market growth would likely be boosted by technological advancements in natural language processing (NLP) and due to the expanding ‘Chat first’ strategy adopted by the service industry.
Drivers: Increased user demand and the need for cost reduction
Supporting enterprise employees and customers is a costly proposition. And today, both groups want rapid responses and instant answers to their questions. Providing such service with humans means staffing centers round the clock. Increasingly, businesses are turning to chatbots and virtual assistants to help.
NLP technology is used by AI-powered chatbots to conduct human-like interactions and provide real-time assistance to clients. AI-enabled chatbots and virtual agents can also be used to automate repetitive and manual activities, including order placement, balance inquiries, general queries, technical assistance, and other customer services.
The cost savings using these technologies can be substantial. Last week, Gartner predicted that conversational AI will reduce contact center agent labor costs by $80 billion by 2026.
“Many organizations are challenged by agent staff shortages and the need to curtail labor expenses, which can represent up to 95% of contact center costs,” said Daniel O’Connell, VP analyst at Gartner. “Conversational AI makes agents more efficient and effective while also improving the customer experience.”
Gartner projects that one in 10 agent interactions will be automated by 2026, an increase from an estimated 1.6% of interactions today that are automated using AI. Conversational AI can automate all or part of a contact center customer interaction through both voice and digital channels, through voicebots or chatbots, and it is expected to have transformational benefits to customer service and support organizations within two years.
Synergies abound with conversational AI
Gartner and others have that the fundamental technologies used offer additional benefits beyond automating interactions. For example, NLP can help in multiple ways.
For example, many businesses are using NLP to support natural language understanding (NLU), which provides the semantic interpretation of text and natural language, and natural language generation (NLG).
Many of us have experienced these technologies in action. If you call a helpline, you are often greeted with a message asking you why you are calling. NLP is often used to extract the meaning of sentences and parts of sentences or phrases. For instance, saying “I want to track an order” or “I want to change my address,” can easily be interpreted, using the information to route your call to the right person. Similarly, a mobile app chatbot can use the same NLP techniques to interpret a verbal question and provide a response.
NLP can also be used to capture relevant information from a conversation. A caller might say, “my name is John Smith, my account number is 12345, and I wanted to check my balance.” NLP could be used to capture the caller’s name, account number, and the purpose of the call. That information can then be used to direct the call, query an account database, and more. More sophisticated solutions can use NLP to gauge user intent or sentiment.
Obstacles to implementing conversational AI
Gartner noted that while the benefits of conversational AI are compelling, the technology is still maturing. There is a fragmented vendor landscape, and deployments can be complex.
“Implementing conversational AI requires expensive professional resources in areas such as data analytics, knowledge graphs, and natural language understanding,” said O’Connell. “Once built, the conversational AI capabilities must be continuously supported, updated, and maintained, resulting in additional costs.”
He noted: “Complex, large-scale conversational AI deployments can take multiple years as more call flows are built out, and existing call flows are fine-tuned for improvement.” To that point, Gartner estimates integration pricing at $1,000 to $1,500 per conversational AI agent, though some organizations cite costs of up to $2,000 per agent. Therefore, early adoption of conversational AI will be primarily led by organizations with many agents with the budget for the requisite technical resources.