Now been involved in a number of such enterprise efforts, nicely put, can use more examples and details. See the use cases linked to for more.
Developing Successful AI Apps for the Enterprise
The IBM team encourages developers to ask tough questions, be patient, and be ready to fail gracefully. By Nicole Tache November 6, 2017, Network exchange in O'Reilly
Read about use cases and practical tips for bringing AI to the enterprise in a chapter excerpt from “Getting Started with Artificial Intelligence.” Download the free chapter “Natural Language Processing.”
In this episode of the O’Reilly Media Podcast, I sat down with Josh Zheng and Tom Markiewicz, developer advocates for IBM Watson. We discussed how natural language processing (NLP) APIs, and chatbots in particular, represent just one of the ways AI is augmenting humans and boosting productivity in enterprises today.
In order to apply AI to the enterprise, Zheng and Markiewicz explain, developers first need to understand the importance of sourcing and cleaning the organization’s data, much of which is coming in unstructured formats like email, customer support chats, and PDF documents. This can be “unglamorous” work, but it’s also critical to building a successful NLP app, or chatbot.
From there, Zheng and Markiewicz offer some practical tips for developers looking to build chatbots: to have context awareness, to fail gracefully, and to have patience—building a successful chatbot can take time. .... "
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