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From a smart assistant that helps you increase your credit card limit, to an airline chatbot that tells you if you can change your flight, to Alexa who operates your household appliances on command, conversational AI is everywhere in daily life. And now it is making its way into the enterprise.
Best understood as a combination of AI technologies — Natural Language Processing (NLP), Speech Recognition, and Deep Learning — conversation AI allows people and computers to have spoken or written conversations in everyday language in real-time. And, it is seeing good demand, with one source projecting that the market will grow 20% year on year to $32 billion by 2030.
Broader AI scope
Organizations have been quick to adopt conversational AI in front-end applications — for example, to answer routine service queries, support live call center agents with alerts and actionable insights, and personalize customer experiences. Now, they are also discovering its potential for deployment within internal enterprise systems and processes.
Popular enterprise use cases for conversational AI include the IT helpdesk where a bot can help employees resolve common problems with their laptops or business applications; human resource solutions for travel and expense reporting; and recruitment processes where a chatbot guides candidates through the company’s website or social media channel. It informs them on what documents they must submit and even makes preliminary selection of resumes.
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While there is no denying that conversational AI offers attractive opportunities to innovate and differentiate, it presents some challenges, as well. Managing an enterprise conversational AI landscape with disparate technologies and solutions that do not communicate with each other is only one problem. Inadequate automation of repetitive processes across the conversational AI lifecycle and the lack of an integrated development approach can extend the implementation timeline. Last but by no means least, AI talent is in short supply.
By adopting some thoughtful practices, enterprises can improve their conversational AI outcomes.
Five best pr …