A report by Deloitte Digital called Conversational AI as ‘the next wave of customer and employee experiences.’However, it is not just a wave, but the entire future of customer and employee experiences in the digital age. Today, we have made enough progress in the field of automation, AI and Natural Language Processing (NLP) to create affordable, scalable and efficient digital interactions. Conversational AI platforms are helping businesses all over the world derive actionable insights through almost human-like conversations across diverse communication channels.
The potential benefits of deploying conversational AI far outweigh its operational challenges. In order to understand how conversational AI will develop in the future, it is worth looking at the status quo. The challenges that conversational AI is currently facing provide interesting insights into future developments.
Depending on the usage, the security related requirements of voice assistants change drastically. When a user makes a query to a voice assistant, it must be ensured that the data shared is processed and stored in a fool-proof manner. This is a must for data related to banking, healthcare and other financial transactions etc. Even among organizations that deploy voice AI, there is a tendency to look at it as an experiment or a pilot project. The need of the hour is to take conversational AI with greater seriousness and long-term vision.
Understanding sentiments and emotions
With the evolution of machine learning, voice AI is getting better at analyzing the semantic searches and delivering the right responses. However, there is still a fair distance to cover when it comes to a human-like understanding of emotions. As we know, it is not what a person says, but how they say it that makes the real difference. You might laugh at someone’s joke and say, “I am going to kill you for this”, but that doesn’t make you homicidal at all. We need to train conversational AI on various human voices to identify such emotional context, and respond accordingly.
Regional languages and dialects
Most of the globally used technologies and tools have English as the primary interface language. This is despite the fact that even when we combine native English speakers and others who speak it as a foreign language, there would still be only one out of five people capable of conversing in the language. That leaves 80% of the market uncovered by English language conversational AI. Since these people won’t be capable of communicating in English, there would be apprehensions and distrust which can only be overcome through deployment of platforms capable of communicating in regional languages and dialects. Through integration of regional language, it is also possible to overcome cultural and social differences effectively.
Some of the most promising Indian conversational AI startups have successfully devised solutions for languages like Hindi and Bengali as well as their mixture with English. With passage of time, we are witnessing rapid addition of such languages and their dialects among the features of voice AI solutions on offer. Considering that the majority of Indians converse in native languages only, this growth augurs well for any business looking to establish a pan-India footprint.
Identifying the right voice
Let’s say you are in the living room with your family on a weekend. Many people are talking simultaneously and that’s when you give a command to your smart speaker like Alexa or Google Home. In such a scenario, it becomes a major challenge for the voice assistant to identify the right command out of all the words it might simultaneously be listening to. In another scenario, it must be capable of identifying different users and their personal preferences as well as information. You, your wife, parents and kids are all likely to use the smart home system, but it could be a chaotic scene if the voice AI mistakes your kids for you or others. Other than these teething problems that will surely be overcome through constant improvement in Natural Language Processing (NLP) and Machine Learning, conversational AI offers some wonderful benefits as well. We need to take a look at those as well.
Superior customer experience
Through voice AI and chatbots, companies across verticals such as retail, technology, FMCG, banking and finance, etc., are providing real-time support to their customers. The speed and accuracy of responses provided by the technology is usually much higher thereby leading to significant enhancement in user experience. The AI doesn’t get tired and can deliver 24/7 support with consistent efficiency and ease. The technology is also faster in delivering automated responses to basic queries and transferring the call to human agents in case of an escalation.
One of the biggest impacts voice AI makes is in the area of analytics and generation of insights. A robust conversational AI platform can be easily deployed across channels and has the capability of covering 100% conversations. Thus, it can gather precise insights about user habits, preferences, common complaints and delight factors. Such analysis can also help in the identification of triggers for sales conversions, cross-selling and soft-selling. E-commerce portals are already using such technology to boost their sales volumes.
Affordability and scalability
Deploying conversational AI or chatbots is cheaper than building a cross-channel tech interface or setting up a fully functional contact center with human agents to handle the queries. Thus, opting for automation through voice AI can help a business save money on infrastructure building, hiring, training and paying a lot of employees. With NLP advancements, we are already seeing conversational AI platforms upping their expertise in areas like sentiment analysis, intent identification and regional language conversations.
Conversational AI is the future of business-customer interactions. Once we get past the early-stage challenges, the technology is going to provide incredible potential to personalize user experience and build customer loyalty across industries.
(Tapan Barman, Co-founder and CEO, Mihup and the views expressed in the article are his own)