Details of the latest Siebel CRM 24.9 Release Update under the Continuous Delivery Model. This Update was GA on 20th September, 2024.
This Update contains new features and delivers lots of key bug fixes.
Siebel AI Framework for Oracle AI
The Siebel AI framework has been extended to provide the ability to interoperate with prebuilt machine learning AI models including Oracle’s Generative AI services. The new enhancements to the Siebel AI Framework provide organizations a faster route for using the OCI Generative AI service for a variety of use cases.
Built-in AI capabilities for Siebel Applications
A collection of out-of-the box functional use cases are provided that use the Siebel AI Framework within both user-driven and automated task flows. These use-cases can help to improve productivity, accuracy, and customer experience. Acting as if it were a co-worker or assistant, the standard use case (which can be customized), provides the following functionality:
- Classification: Utilize the pre-trained OCI Language AI Service model to classify incoming service requests and auto-populate the Area and Subarea fields that are often used for more efficient routing.
- Sentiment Analysis: Use Generative AI to determine the sentiment of all inbound communication so that a Call Center Agent can better understand how a customer is feeling about their current service or product concern.
- Auto-Summarization: When closing a Service Request, the Call Center Agent has the option to auto-generate a summary of the Service Request using Generative AI that can be reviewed and revised before sharing with the customer.
- In-Flight Summarization: In situations when the Call Center Agent must transfer or collaborate with a subject matter expert (SME) to help resolve a Service Request, a summary can automatically be generated using Generative AI, which will help the SME come up to speed quickly on the issue.
The key benefits and ROI that the built-in AI capabilities provide for the Service use case include:
- Reduce resourcing cost at scale for everyday processes
- Reduce manual errors
- Improve productivity for agents and reduce fatigue
- Provide valuable sentiment insight that allows the agent to improve customer experience
- Save time writing activity summaries for closing or transferring service requests
Oracle Generative AI – Service Scenario
In this out-of-the-box functional use-case a new Service Request is handled via automation using the Oracle Generative AI Service in the following manner:
- Automatic SR classification
- Sentiment Analysis
- Summarization
Enhancements to Existing Integrated AI Services
- Detection of Private Health Information (PHI). This is in addition to Personally Identifiable Information (PII) which is already available out-of-the-box.
- Speech-to-text transcription available for new languages including French, German, Italian, US English, Spanish, and Portuguese; more input formats, including WAV, MP3, OGG, OGA, WEBM, MKV, AC3, AAC, M4A, MP4, FLAC, and AMR
Open UI – Configuring List and Form Applets to display a color based on the value selected
Using the Applet Presentation Model (PM) User Property, you can configure list applet columns or form applet controls to highlight a field with colors based on the value selected. You can add a CSS definition to show the columns or controls in the specified color.
For example, if the Customer Service Center wants to easily highlight customer “sentiment” for Service Requests to assist Service Agents, then this is easily done. Where sentiment is pre-defined – potentially via Oracle AI Services – developers can make a column in a list applet appear in a specific color if the sentiment column has a value positive, negative, or neutral.
This simple feature reduces development and configuration effort, whilst making the UX more intuitive for users.
A review of the Release Update is also available here on the Siebel Hub YouTube channel.
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