The new wave of digital experience with Conversational AI

Over the past decade, consumers who became comfortable using smartphone-based communication platforms and voice assistants such as Whatsapp and Alexa, for personal communication and tasks, now expect similar conveniences when interacting with businesses. 

On the other hand, a wide range of potential applications for AI has fired the imagination of business leaders who are trying to identify use cases that can beef up their customer experience. In a recent Gartner survey, the largest proportion of respondents considered customer experience/retention to be their top area of use for Generative AI. Organizations always prioritize customer experience because it drives customer satisfaction, retention, and royalty and thereby improves revenue.

Messaging platforms are the new engines of digital commerce

Many organizations have already invested in evolving these messaging platforms into engines of digital commerce. Organizations use these to interact with customers across all three major stages of the customer acquisition lifecycle: discovery of the product/service; continued engagement between seller and buyer; and finally, purchase and delivery. 

Some have also integrated these platforms with their after-sales service functions and feedback processes to create a hyper-personal 360^o interaction channel with every customer to drive engagement, sales, and loyalty. The emergence of chatbots accelerated the adoption of this paradigm as a distinct revenue-generating channel. Across industries, enterprises have integrated chatbots into their websites and apps to provide greater self-service and 24x7x365 access at scale; this also reduced the need for staff. 

The evolution of conversational AI

The emergence of advanced APIs and maturing of new technologies including AI now offer enterprises the opportunity to take customer service to higher levels. Advanced APIs transcend the constraints of basic APIs, which limit voice interfaces to specific services from one company. Now, API-based interfacing is possible with members of entire business ecosystems. This broadens the scope and scale of interactions between humans and machines.

The new-age voice bots and chatbots can be trained to better “understand” user contexts and personas using demographic data, previous conversations, and the ability to understand what users need. This means that conversational AI can go much beyond providing standard, pre-defined answers. Developments in natural language processing (NLP) make it possible for virtual agents/bots to mirror human conversations more accurately. They can ask open-ended questions and shape the conversation based on how humans respond. Machines can now accurately capture the context of every interaction and respond with higher levels of intelligence.  Such bots can do a better job of speech recognition (including accents and multiple languages) and manage customer dialogues more efficiently. The underlying AI tool can learn from every interaction so that future conversations are less prone to errors. 

Conversational AI will also help drive employee productivity in organizations, redefine recruitment and training, and other facets of operations. These will help simplify and enhance user experience and expand the range of potential touchpoints available to enterprises and customers in an increasingly phygital world, thus creating new sales engines. 

Depending on the business context, such touchpoints can include smart home appliances or smart public kiosks. Processes such as scheduling medical appointments or answering questions about treatment can be handled through conversational AI. Travelers can use conversational AI to help plan holidays or itineraries based on specific needs- tasks that can be very time-consuming if done the traditional way. As 5G (and 6G) become mainstream and various sensors are integrated into devices, customer experience will continue to transform in terms of interaction speeds and quality of outcomes. 

Conversational AI involves training computers to:

• Capture and understand input generated or supplied by humans.
• Analyze such input using NLP and/or speech recognition to make sense of human needs.
• Formulate responses using dialogue management.
• Continuously learn from user inputs and dialogue patterns via a feedback loop to refine future responses.

Conversational AI can deliver multi-dimensional business value, including:

• Cost efficiencies
• Easy scalability (including integration via advanced APIs to multiple processes/entities and with legacy systems)
• Superior customer engagement through hyper-personalization, multiple language support, and smoother omnichannel integration. 

But conversational AI has its challenges. The ability to understand human input depends on how accurately bots can decode variations in accents, emotions, and other nuances. Adequate safeguards around privacy must be in place because customers may disclose personally identifiable or other confidential/privileged information. Also, if the bot is unable to satisfactorily address the customer query, the system must be able to connect to a trained human employee who can take over the interaction. 

With the potential of generative AI firing conversational AI, this technology will evolve further. It will generate better actionable insights, suggest prescriptive actions, and be more “humane”. Lastly, it promises high short-term ROI to organizations willing to invest in it.

The article has been written by Bindya S Raj, Associate Vice President, Head - Strategy, Planning & Operations, Digital Experience, Infosys