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Developing a Conversational AI Program

Towards data science – by Rachel Bimbi

Conversational AI technologies have evolved rapidly in the last decade, with chatbots, virtual agents, voice assistants and conversational user interfaces now part of our daily lives. This explosive transformation toward AI assistance hasn’t come from an individual technological innovation, but rather multiple innovations developed as an assistive layer between our lives and our digital services, whether we’re asking for directions, purchasing online or banking. In fact, IDC predicts global spend on AI will double from 2020 to 2024, growing to more than $110 billion, with retail banking expected to spend the most.

Surprisingly, for all the benefits conversational AI offers, many projects fail as a result of poor discovery done at the beginning, which is why spending time upfront to examine what’s being built and the value it will deliver to customers is critical. With lessons learned in the field instrumental in improving odds for success, the following seven steps can serve as a guide for enterprises in nearly every industry embarking on or advancing an existing conversational AI platform.

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