Top Conversational AI Trends to Follow in 2050 and Beyond

With more and more evolution, Conversational AI is advancing at a great pace. Following the addition of more disruptive technologies in its cognitive capabilities and rising market demand of AI skillset, the technology is trending in 2020. Conversational AI is going to become only greater in the coming times with new abilities and attributes adding to it.

Here are some of the Conversational AI trends to follow in 2020 and beyond.

Low code/No-code Platforms

According to Kore.ai, with the use cases booming from hundreds to thousands, and conversation types increasing from thousands to millions, organizations are seeking platform-based models for meeting their evolving conversational AI needs at ease.

Coding for every little change in the requirements can be a tedious exercise resulting in a very long time to market. LowCode/NoCode platforms are the perfect choice to address these issues. Having all possible features for a variety of use-case in one place, these platforms can customize their bots with just a few clicks. Low Code and No-Code platforms are designed to increase the agility and effectiveness of organizations and will continue to grow traction in 2020.

Personalization

Conversation AI bots of 2020 will deliver highly personalized and contextual conversations that give them a human touch. For example, they can understand and remember conversation context, past dialogs, and user preferences, etc., and 'wow' the users with their suaveness. They can also carry the context across multiple conversations to understand the past and future requirements. With their humanesque conversations, they can understand your sentiments and moods, and respond accordingly. These bots can be widely leveraged to cross-sell and up-sell products/services to users.

Augmented Reality in Conversational AI

AR in chatbots is a unique technology that can take the engagement level and usage to the next heights. AR is a relatively new technology for mobile/web apps and the users are not accustomed to the usage of AR. A chatbot within an app can facilitate the usage of this technology. Depending upon the behavior of the user and his stage in the buying cycle, they can be prompted by the use of AR by the bots.

For example, if you want to feel how a coffee table would look in your living room, how apparel would fit you, you can use the AR technology. Enterprises such as IKEA, Zara, Loreal, Amazon, and many more are testing the potential of AR, but despite its amazing technology, the adaptability is less. The cognitive capabilities of a Conversational AI bot can be used as an online concierge to assist the users in their buying journey.

An Upsurge in New Skill Sets

Building conversational AI systems is no longer the exclusive domain of developers and computational linguists. Enterprises are increasing including business users on the implementation teams, allowing them to collaborate in developing and maintaining conversational AI applications — proving that new technology will bring with it changes in the skill sets needed within the business and potentially new employment opportunities.

Scriptwriters will ensure the flow of the conversation includes everything from brand values to open-ended questions that better understand the customer's needs. Team leaders will need to know not just the business processes, but the quirks of those processes that could trip a chatbot up. Designers will improve visual understanding, such as engaging avatars capable of reacting to emotions within the conversation. And the list goes on.

Key here will be their ability to collaborate as a team in building and enhancing the conversational AI application, which means the development platform must-have features like workflows, automated testing, version control, and roll-back, as well as a graphical interface that's easy to use.

A Hybrid Approach to Development

Andy Peart, Chief Marketing & Strategy Officer at Artificial Solutions, has long been an advocate of a hybrid natural language model that combines the advantages of both linguistic and machine learning. According to him, in 2020, we will see this hybrid approach taken to the next level. The development platforms themselves will offer the intelligence to decide where each model is used to better optimize the performance of the conversational system.

In a Forbes article, he noted that this will make it easier for the developer to build a robust application by automatically mixing and matching the underlying technology to achieve the best results. This increase in hybridization will make conversational AI technology significantly more productive for the developer where the development platform includes a deep linguistic knowledge.