Conversational Gen AI as a Tool in Service Centers
- Sagit Salmon
- Apr 29
- 3 min read
The breakthrough of Conversational Gen AI tools like ChatGPT, which happened almost overnight, caused us to recalculate our path. Alongside great excitement and curiosity, there was also a lot of concern. As someone who has been working in the field of service centers for many years, who has worked with many people along the way and still supports organizations and knowledge management teams today, I ask myself if the day will come when chat will replace us? After so many years, will there no longer be a need for content editors in service centers?
In my previous article, " ChatGPT in Knowledge Management and Service Desks," I addressed general ideas for integrating AI tools in knowledge management for service centers, both for knowledge management teams and for service centers’ operators.
In this article I want to focus on concrete examples of how to use these tools, as they are already being implemented in service centers across various organizations.
First, when defining a topic tree, AI chat helps with recommendations for building the hierarchy—topics and subtopics—and suggestions for names. This is especially helpful since we often struggle to give names that accurately reflect the content and are short enough.
In the world of templates, AI chat can recommend the type of template and the names of the section headers, but beyond that, it can help organize them on the content page. It can also assist with more detailed specifications, such as the types of fields in the template, e.g., simple or rich text, mandatory or optional fields, etc.
In terms of content editing, AI chat helps break down complex content. It starts with a basic understanding of the content and summarizing the main messages of the text. Then, it suggests the type of template recommended to make the content accessible and reorganizes it in the template.
Furthermore, AI chat assists in content editing - it can highlight the main messages and unnecessary information (that which is less relevant to the item in question), help with linguistic simplification of the text, split long sentences, provide suggestions for rephrasing some of the information, and suggest ideas for the item's title.
Additionally, it can be used to identify duplications, such as repeated sentences or messages that often found in raw content. In addition, you can ask it to suggest ideas for missing information and issues that don't have answers, and to identify contradictions in the information (for example, two different ways to handle the same service issue).
The AI chat also assists with translating between languages in multilingual service centers environments, and writing for different target audiences. For example, content may be written in a service-oriented language intended for service representatives, as well as suggest marketing language suitable for customers. Or, for instance, when text is intended for telephone service representatives, who actually need to deliver the information by "speaking" it, the AI chat can formulate the information so it is appropriate for written communication with a customer.
Finally, assistance with implementation actions for better use of the knowledge management system is needed. Here are a few examples:
Writing knowledge tests - formulating questions, "inventing" incorrect answers for multiple-choice questions
Drafting communications about system updates
Analyzing usage reports
Suggesting questions for satisfaction surveys
The possibilities for utilizing AI chat are endless. If, at the beginning, there was great concern that the AI chat would replace employees in service centers , then the focus is currently on the efficient use of AI chat.
Only time will tell what will happen in the future. Until then, we will continue to enjoy technology's ability to improve and streamline our work.
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