The Road Towards Successful Conversational AI Capabilities

Conversational AI technologies are transforming into hyper-personalized, multimodal assistants that are inclusive and immersive. Businesses should gradually approach conversational AI, increasingly moving toward complex features with continuous development.

Rapid Development of AI Assistants 

Source: Infosys

According to Infosys’ Digital Radar 2022, roughly 56 percent of organizations worldwide have already embraced scalable AI. On the other hand, another 32 percent are experimenting with it in specific business units. Gartner management consulting company estimates global AI software market revenue to reach $62.5 billion by the end of 2022.

Conversational AI has been present in the enterprise landscape since 2016 and suddenly became popular during the pandemic. It has supported customer service and other aspects to help businesses cope with the present challenges. Statista reported that as of 2021, over 300 million households globally use intelligent speakers. Currently, conversational AI is one of the top five expenditures in AI software with virtual assistants accounting for almost $7 billion virtual assistants this year.

Seven Major Trends That Shape the Conversational AI Landscape

1. Conversational experiences to become hyper-personalized

Standardized conversations based on predetermined rules show behaviors seen in robots. During early developments, such conversations seemed amusing. But, with smart technology, it isn’t as surprising now. But investments in chatbots can make or break the customer experience. 

Today, organizations are building bots that can mimic human responses with empathy and contextualization. These bots can analyze historical data to create context and understand the users’ likes, dislikes, personalities, and moods to respond accordingly. 

Infosys has launched a hyper-personalized learning assistant called “Zoiee” for its workers. Zoiee is an avatar-based assistant that utilizes users’ inputs, previous conversations, and other relevant data to suggest certifications and courses and help them achieve career growth and meet learning targets.

2. Voice-enabled conversations to penetrate deeper into enterprises and consumers’ reach

Every smartphone now has voice AI, and smart speakers are available in more than 300 million households worldwide. Voice search and assistance features increasingly integrate with interactive voice response (IVR). IVR is used for customer support and other digital devices and services such as smart TVs, online streaming, and drive-through food ordering. 

3. Immersive conversational assistants proliferate.

With the introduction of the metaverse ecosystem, immersive technologies will become the norm for people, businesses, and machines to communicate. AR and VR elements will enhance all significant spaces, from workplaces and factories to gaming, entertainment, and social media. 

Digital assistants are one of the early applications of immersive technologies as businesses work toward improving customer experience simultaneously with automation initiatives. These assistants will soon become avatar-based audio-visual chatbots with gesturing capabilities to make the experience as humanly possible. But, AI has a long way to go to reach that track. 

4. Context persistence to gain attention for seamless conversations across devices

Context Users expect a seamless experience while switching between multiple devices (phone, laptop, tablet, smart speakers, etc.) throughout the day. Companies try to add context persistence capabilities that seamlessly enable customers to switch from social media to web or phone conversations. Also, they use the technology for smoother payment transactions. Such AI solutions will transform call centers in the future. 

For example, Infosys Cortex, an AI-based contact center solution, enables context persistence across platforms. It is aimed at enhancing agents’ experiences by helping them with relevant information and suggestions in real-time to support customers effectively.

5. Multimodal conversations for seamless interactions

Multimodal AI solutions can collect information from multiple sources, including text, visual, and audio, to allow more precise “conversations.” Big players such as Google, Meta, and OpenAI are quickly adopting these multimodal AI developments. 

6. Increased adoption of LCNC platforms to boost productivity

AI solutions require data selection and preparation, feature extraction, model selection, fine-tuning, and training. These technologies need testing and debugging and, most importantly, time. Meanwhile, Low-Code/No-Code Development (LCNC) tools can enable non-technical people to develop solutions through intuitive interfaces that use everyday interactions such as clicking, dragging, or dropping without the skill to write code. 

7. Local language and domain-specific AI models have become popular

Streaming platforms like Netflix, Amazon Prime Video, and Hotstar were integrated into regional language content. In India, restricted content is expected to claim a 54 percent share on streaming platforms by 2024. However, most other companies are yet to incorporate customer experiences in local languages. Perhaps, we’ll be seeing more conversational AI applications soon.

Conclusion

Many businesses cannot maximize their AI initiatives because they are immersed in too much change at once. Around 80 percent of business transformation programs can’t deliver their true potential for the same reason. They should design a gradual and appropriate approach to implementing conversational AI. That said, Infosys recommends the maturity model approach for maximum adoption among target segments and higher accuracy of AI responses.