CIOInsider India Magazine

Providing outstanding client experiences is crucial in today's hectic corporate environment, and conversational AI is a key player in this shift. Conversational AI & Customer Experience Summit 2024, held at The DoubleTree by Hilton Bengaluru Whitefield, shed light on staying ahead of the constantly changing field of customer-centric technologies. The event explored business practices for companies throughout India, building on a wealth of global ideas that are now specifically suited for the Indian market. The thought leaders explored real-world case studies that demonstrate the significant influence of Conversational AI on customer happiness and business growth, brainstormed about the newest trends, and conversed with prominent figures in the field.

Conversational AI in the Banking Sector
The panel, including Priyanka Sarkar, Director of Global Product Management, Mastercard, Tanmay Shah, Assistant Vice President, Axis Bank, Deepak Gupta, Vice President of Engineering, CARS24 and Priya Chakravarthy, Vice President – Experience, BluSmart, discussed the future of conversational AI from the perspective of customers.

As one of the panelists says, the history of design and the user-centric approach is relatively young. We simply travel back in time. We began with your desktop computers and mobile user interfaces at first. We then gradually transitioned to a mobile strategy to purchase that, with the goal of delivering a user experience consistent with the experience text. After that, everything was basically alright.

Initially, the website regarding clothing purchases—which one may choose not to make—turned out to have the appropriate buttons, giving a could pull it off. As we gradually approach the realm of conversational AI, the concept seems to be that greater attention is being paid to conversational user interface design—that is, how to create a whole interface with the user at the center. Customization becomes extremely challenging since no customer can be provided if the client is not authorized. Because many people have highly precise information, businesses are attempting to develop something that will allow them to identify the individual and have they used a component that is based on the customer approach.

Furthermore, being a bank makes customization extremely challenging since no customer information may be provided if the consumer is not authorized. Because many people have highly particular information, the sector aims to develop something that will allow the businesses to identify a person based on their information and they will use an element that is based on the customer approach that one can use on a daily basis.


Conversational AI has to be Integrated into Your Core
When an organization participates, it frequently reaches its ideal lifecycle stage, which is also highly common. Many growth factors are taken into account in between the strategies or the quantities one likes to forecast, the target market, the client base, or the product emphasis, all of which affect the status of PR Returning to the four methods pertinent to the sector.

One of the panelists shared her experience of use cases in which many are aware that API generation occurs throughout the whole product lifetime, notably during the execution of any integrated project for a particular functionality-based event.

For this reason, API has a very secure platform. As a result, many companies adopt API as the common format for all strategic integrations. The second one is most frequently used in technologies used by large organizations. An example of this would be event-based architecture. For example, when attempting to complete an application and selecting a payment method or code, you select the appropriate code. You may also be required to complete an application process or provide a PIN code or passport that will be sent to your mobile device. Architecture is even built on that.

This implies that one must initiate an event at every layer of the structure to receive a response from the client. Examples include large-scale streets, important sectors such as DTH or banking, our insurance industry, which is also extremely high in nature, and any other industry that exhibits great volume. Many organizations are becoming obsolete as more and more people use many cloud platforms. Many people are currently quite skeptical of it and don't necessarily want to be present in the room. They have large database systems, substantial financial resources, and—most importantly—own servers and physical space. Thus, there is something that isn't related to anything that has to do with your on-site apps, connecting to your sleep zone, cloud properties, or application servers. In actuality, it depends on the strategy of your company. Depending on the product line and training I'm using, a tech solution will either push itself into the system or provide you with the most output possible. And once more, there are three ways that a natural leader might achieve maximum output: they can be very postural, very experienced, very driven, or exceedingly detached. However, it only focuses on these three elements.

Data Diversity
One of the panel members says that data diversity is not a nice thing. Suppose we take any compositional system, which is the foundation, especially when looking for AI models to work for everyone, for every type of user. The challenge that now arises is how to obtain the DNC because, while discussing data diversity, it is not about being sophisticated; rather, when one looks at that particular section, the mind is immediately drawn to the numbers. However, that's only the tip of the iceberg in my view. One must examine the data to the state and the most extensive, widely used models. For instance, an artificial intelligence model must comprehend not just the languages we are concentrating on but also the several dialects that exist within each language, as well as their distinct temporal variations. Additionally, in this conversation, multiple dialects and accents are involved. Therefore, we need to take care of the generally diverse in order for us to become globally integrated and recognized as global citizens. And as he said, alongside this, they also address the biases and fairness of the datasets. This is because, with more data, you must ensure that it is not skewed towards any particular demographic or that it is fair enough to take into account all demographics. Living with this kind of emphasis allows you to decide whether to introduce a designer for five minutes. In order to produce this sort of data collection and annotate this kind of data, I also need to include a highly broad team.

In addition, the only data set you can train on is very generic and cannot be used by the general public to determine whether someone is in custody or not. Your models must be trained on very specific data provided to your client; an example would be a chatbot that uses this data to find a bank and a restaurant. They would also explain that you will always find a disability and that you are only playing on average if you question them about the check, which has a very different meaning.

Panelists continue explaining that the users need to figure it out. This means that if my user isn't going to be precise, I'll need to introduce a wide range of user queries, bring that into the context of equal pay, and comprehend the user's goal and intent in order to reach the ultimate goal of comprehending what the user asked us in whatever way. It requires ongoing work with some data. Additionally, it takes constant work to adapt the model to the new set of data and to remember everything. That brings up another point: AI models should be able to identify any policies, data gaps, biases, and lack of diversity that should be brought to light so that the Department of Human Intervention can supply a diverse sample of the diverse data in a way that allows AI to be retrained.