Hospitality and human instructiveness are essential in connecting with consumers. Few would rather talk to a robot caller to handle their issue than a genuine human being. Additionally, customers typically dislike waiting for a representative for extended periods. And personnel sorting through enormous volumes of calls might produce a massive workload. Conversational AI can be used in a variety of organizations to attend to the needs of specific customers to lessen this issue and increase efficiency.

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! These days, conversational artificial intelligence (AI) chatbots are everywhere on websites, SMS and social channels. Conversational AI-supported chatbots that use natural language processing (NLP) help customers deal with everything from product recommendations to order questions. Enterprises love conversational AI chatbots, too: According to a recent Gartner report, by 2027 chatbots will become the primary customer service channel for roughly a quarter of organizations.

No-code AI solutions are helping more businesses to get started on their AI journeys than ever. AI News caught up with Stefano Somenzi, CTO at Athics, to get his thoughts on no-code AI and the development of virtual agents. AI News: Do you think "no-code" will help more businesses to begin their AI journeys? Stefano Somenzi: The real advantage of "no code" is not just the reduced effort required for businesses to get things done, it is also centered around changing the role of the user who will build the AI solution. "No code" means that the AI solution is built not by a data scientist but by the process owner.

In the last decade, advances in data science and engineering have made possible the development of various data products across industry. Problems that not so long ago were treated as very difficult for machines to tackle are now solved (to some extent) and available at large scale capacities. These include many perceptual-like tasks in computer vision, speech recognition, and natural language processing (NLP). Nowadays, we can contract large-scale deep learning-based vision systems that can recognize and verify faces on images and videos. In the same way, we can take advantage of large-scaled language models to build conversational bots, analyze large bodies of text to find common patterns, or use translation systems that can work on nearly any modern language.

Conversational Artificial Intelligence (AI) empowers enterprises to employ chatbots, messaging applications or virtual assistants to build highly engaging and valuable relationships with customers. This cutting-edge technology is spreading rapidly across every industry and more excitingly, providing enterprises with a huge potential to accelerate their growth and innovation. From whatever little it has seen of AI assistants so far, the enterprise world has built great expectations. They are visualizing a future full of bots, so smart and powerful, that they help humans with almost any kind of support required very substantially. No wonder conversational AI has become the much-awaited technology in today's enterprise world and attracting the attention of business leaders across the globe. But how much of it is hype, and how much is really closer to reality?

It's hard to imagine now, but a decade ago, open-source software--programs that allow users to modify the source code--was still on the fringes. Startups were starting to build on open source and open core, but few, if any, enterprises were. Looking back, we can now say that open-source models undoubtedly accelerated both the pace of innovation and the quality of traditional software development. Nowadays, most anyone who is trying to build a successful SaaS product typically leverages as much open-source code as possible. Given their success building open-source SaaS solutions, it makes sense that many enterprises would strongly consider building out their AI capabilities in house.

Artificial intelligence has gone beyond being the flavor of the week at tech conferences. AI offers the best way for companies to automate repetitive processes and gain insight into their customer bases. Some business leaders may want to take advantage of AI to boost productivity or revenue, but not being tech experts, may not know how best to do so. To help, 16 members of Forbes Technology Council offer smart ways business leaders can incorporate AI into their processes. Smart leaders can leverage AI as part of a customer journey orchestration strategy to improve customer lifetime value.

According to IBM, businesses globally spend over $1.3 trillion each year in order to handle approximately 265 billion customer service calls. Self-service solutions, such as chatbots and knowledge management, can help businesses save on customer service costs by automating the handling of those customer queries, especially the most redundant ones. As usual when adopting and implementing a new technology, you'll want to assess the return on investment your business will benefit from. When monitoring the performance of a self-service initiative, there are a variety of different KPIs to keep track of in order to allow ROI to be measured. One of the main indicators that can be taken into account is contact economy, which is based on the number of contacts avoided by phone or email.

Customers love voice and chat assistants, the conversational interfaces that turn on the lights, help home chefs cook an egg to perfection, and make it easy for consumers to research and buy goods online. However, while customers are already building strong relationships with these conversational assistants, retailers are still learning how to best use conversational bots to drive engagement and strengthen their customer relationships. Nonetheless, these conversational assistants represent a fantastic opportunity for retailers to humanize their interactions with customers at scale, as long as it's done with proper understanding of what it takes to engage with customers and how to deploy voice and chat to drive growth and return in 2020. Conversational interfaces fall into two categories: voice and chat. Voice assistants are mediums that can be accessed through voice commands on a smart speaker or smartphone application. Examples include Google Home and Google Assistant, Amazon Alexa, Apple Siri, and Microsoft Cortana.

Conversational AI tools or chatbots have come a long way from error-prone level-1 support to becoming a widely used tool in banks' automation and AI strategies. Today, chatbots in banking are being used by Wells Fargo, Capital One Bank of America, HDFC Bank, HSBC, CBA, and many other leading global banks. The contribution of these chatbots is noteworthy. Consider this: Bank of America's chatbot Erica has served over 35 million customers, and HDFC's EVA engages in over 20,000 conversations every day. The overall impact and growing importance of intelligent chatbots can be understood if we look at the report from Gartner, which suggests that by 2020, customers will prefer to resolve 85% of their problems with an enterprise without interacting with support staff.