What’s New in Tokyo - Platform AI Capabilities

[Christina P]

With the Tokyo family release, we have some exciting AI enhancements and capabilities coming onto the Now Platform®.

Task Intelligence

With Task Intelligence, we’re delivering AI-powered task creation to help agents spend less time triaging and investigating cases, so customers can get resolutions faster.  The new capabilities that we are releasing in Tokyo will be available initially with Customer Service Management. 

Agents need to triage, investigate, and resolve cases efficiently.  Unfortunately, they spend too much time reading emails or details in case forms to extract the most valuable information. Today’s support agents require an easier approach to streamline case resolution.

First, Sentiment Analysis helps ensure the sentiment of any communication is correctly understood to prioritize the case with the right urgency. 

Second, we deliver the capability to auto-populate fields so cases can be routed to the right team at the right time to optimize agent productivity and reduce delays.   

Plus, ‘Task Intelligence Admin Console’ makes AI accessible to all users. It empowers process owners to drive support efficiency with the power of AI, with an intuitive, data-driven, guided administration experience to define, train, assess, and deploy models with in-product resources for ongoing value realization.

AI Search Enhancements

Users want enhanced search experiences. They want highly relevant responses and effective search-based autocomplete suggestions for greater efficiency when looking for information. With the Tokyo release, AI Search brings auto-complete taking search users to the destination quickly and helps them receive highly relevant results. 

Also, Tokyo brings improved relevancy with new language support for traditional and simplified Chinese through the tokenization of characters

NLU Workbench Enhancements

With the Tokyo release, the NLU Workbench delivers enhanced usability to make the model-management process more intuitive for customers and easier to use. 

Back in San Diego, we introduced Expert Feedback Loop, which takes real-world or end-user utterances to improve model performance on a continual basis, also known as continual learning. With the Tokyo release, we improved the user experience of reviewing end-user utterances through the Expert Feedback Loop card-based view. 

And finally, we deliver greater visibility on NLU performance for Virtual Agent, with the ability to view performance by language or by model.

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