Emergency or uncertain situations cause spikes in call volumes and reduce the customers’ appetite for ‘your call is extremely important for us. Please wait while we connect you to a representative’, voice messages. This is where AI is going to be a gamechanger for businesses globally. According to Gartner, about 70% of customer interactions will see involvement of technologies such as machine learning, chatbots, and mobile messaging etc.
In the post-pandemic normal, the need to humanize and expedite customer engagement is increasing exponentially. Today, customers expect brands to be accessible anytime, from any place, and have a support system in place that is well equipped to quickly resolve their problems or answer their questions. This is a need that conversational AI is set to fulfil. It sounds like a human and understands customer speech with near human accuracy. Thus, customer conversations become more personalized and engaging even if the callers are actually talking to a computer. Conversational AI assistants respond with equal efficiency and accuracy to text and voice commands and give users the freedom of choice on whether they want to talk or chat. Business leaders are taking note of the development and adopting the technology rapidly. Growing at a CAGR of 21.9%, the conversational AI market is expected to touch 13.9 billion dollars by 2025.
AI is now going beyond merely the power of verbally responding to commands, and gaining the ability to hold its own in deeper, multi-layered conversations. The emergence of natural language processing has enabled AI customer care systems to faster and better understand what is being said. It is not uncommon to see the AI independently manage the whole conversation without the need to redirect the call to a human agent.
The best part is that the higher the frequency of AI system’s conversations with humans, the better it gets at understanding and engaging them. Machine learning algorithms developed by leading voice AI brands ensure that each conversation is used as data that enables the AI system to comprehend the nuances, and predict the right response. Whether it be monitoring a customer’s words to understand the intent or using past data to deliver personalized content and suggestions, AI keeps getting better at it. By continuously processing more data, the AI algorithms self-learn and improvise to proactively make suggestions and display contextual awareness. When it gets the assessment right and receives consumer delight as a response, it tries to repeat the same approach with other callers, and if a response doesn’t enthuse the customer, then it is avoided with the next caller.
One of the biggest challenges in universal adoption of voice AI to drive personalized conversations was the dependence on English as the language of communication. Further, even the usage of spoken English proved complicated for the AI because of the diversity of accents, mannerisms and styles. An American would speak English differently compared to a Brit. Similarly, an Indian from the southern part of the country would have a different accent compared to a north Indian. All such accents are valid and should be equally responded to. However, that never really happened as the AI assistants were trained in specific accents. Today, the Natural Language Processing ability is doing away with these accent or language barriers and factoring in spoken language features such as intonation, voice energy, silence, pauses and usage of words etc.
Equipped with this power of catering to diversity of languages and dialects, the AI voice assistant is all set to be the best friend, an impartial advisor, and even a personal shopper depending upon the use case. That’s the future we are heading towards.
The article has been written by Tapan Barman, Co-founder and CEO, Mihup