{"id":507256,"date":"2024-02-20T20:18:10","date_gmt":"2024-02-21T04:18:10","guid":{"rendered":"https:\/\/www.genesys.com\/blog\/post\/cx-is-still-at-the-tip-of-the-conversational-ai-iceberg"},"modified":"2024-02-21T14:57:11","modified_gmt":"2024-02-21T22:57:11","slug":"cx-is-still-at-the-tip-of-the-conversational-ai-iceberg","status":"publish","type":"blog","link":"https:\/\/www.genesys.com\/en-sg\/blog\/post\/cx-is-still-at-the-tip-of-the-conversational-ai-iceberg","title":{"rendered":"CX Is Still at the Tip of the Conversational AI Iceberg"},"content":{"rendered":"

[vc_section full_width=”stretch_row”][vc_row][vc_column][vc_column_text]In 2022<\/a>, it was estimated that conversational AI would reduce labor costs by $80 billion. When ChatGPT became available, the focus shifted to generative AI \u2014 and that technology continues to dominate the conversations among customer experience (CX) professionals. The promise of easy access to information for customers, delivered naturally and with personality, is an attractive proposition. It\u2019s especially attractive to those who have been working with conversational AI. Some might question whether continuing conversational AI investments make sense in this generative AI future.<\/p>\n

Ringing the death knell for conversational AI seems premature. Gartner reported in 2023 that conversational AI is the fastest growing segment for the contact centre<\/a>. In the same study, the firm found that interactions that involve AI are augmented, not fully offloaded. This means there\u2019s a need for the human touch to complete the experience.<\/p>\n

Clearly, we\u2019re not yet ready to replace what we\u2019ve been working with \u2014 and there\u2019s room for generative and conversational AI<\/a> to coexist. While generative AI has the promise to improve how customer experience is delivered, there is still untapped potential in how conversational AI is applied.<\/p>\n

Conversational AI vs. Generative AI for Consumers<\/h2>\n

The difference between the application of generative AI and conversational AI for customer experience lies in the nature of the conversation that happens between the consumer and their product\/service provider. With generative AI, the conversation is informational, fluid and might cover a broad set of topics. It will feel like you\u2019re talking to a friend who can tell you about your destination with interesting details and can give you various options on how to reach the destination. However, this friend can\u2019t just drive you there or call you a cab.<\/p>\n

With conversational AI, the conversation will be more confined, potentially more robotic, and it could be digital or voice-based. IVRs that used to require the \u201cPress 1 for Yes, 2 for No\u201d now incorporate conversational AI to enable natural language conversations, sometimes augmented with DTMF (dual tone multi-frequency), which enables the use of the dial pad for sending information to a system.<\/p>\n

The conversation will be focused. These conversations are built to approximate human service interactions and typically end with an action \u2014 a purchase, a return, an account reset or an escalation.<\/p>\n

Conversational AI vs. Generative AI\u00a0for Conversational Designers<\/h2>\n

There are also differences for those who are building conversational experiences into interaction journeys. With generative AI, prompt engineering coupled with a pre-trained large language model can enable an automatic conversation. Prompt engineering means finding a way of extracting the right data from a model that has been trained on a very wide set of data gathered from virtually anywhere and anything. It\u2019s asking AI the right question with the right parameters to give it the right frame of reference to retrieve the right information. It\u2019s both art and science.<\/p>\n

Conversational AI bots are designed as conversational flows with defined paths based on intents. Conversational AI bots<\/a> are programmed to respond to certain questions within a specific domain. Asking conversational AI a question that doesn\u2019t match what it knows will result in a response such as, \u201cPlease repeat your question.\u201d<\/p>\n

With generative AI, that same situation is unlikely. Generative AI models are typically trained to provide some type of answer, because the data set that was used for the training is much bigger. Generative AI algorithms calculate what is the next best thing to say based on learned language patterns. Some generative AI models will provide wrong answers that sound right \u2013 a hallucination<\/a>.<\/p>\n

Conversational AI is used to take the customer request and extract an intent \u2013 the essence of the ask. The conversational design will determine what will happen once an intent is identified. Conversational designers can build in a data look-up (i.e., show me my account balance), or craft an answer based on a knowledge base.<\/p>\n

A conversational AI model has a very narrow focus (typically) \u2014 so it can respond to questions within that one domain. Generative AI models are bigger; they have billions of parameters well above typical conversational AI models \u2014 so they can understand a larger scope of language. It\u2019s like responding to an essay question about a book after reading a single page (or chapter) versus responding to an essay question after reading the entire book, the author\u2019s biography, the history behind the book and other reviews of the book.<\/p>\n

Conversational Bots Need to Do More Than Talk<\/h2>\n

The problem with shifting attention fully to generative AI is that the ability to deploy a bot that can have a fun conversation about a multitude of topics won\u2019t solve the problems that real customers bring to the contact centre. A more fluid conversation won\u2019t overcome issues that customers have experienced through use of chatbots.<\/p>\n

Data<\/a> shows that of those who have used chatbots:<\/p>\n