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<\/p>\nAI enabled tools and platforms offer the potential to leverage unstructured digital information to improve customer experiences and drive business outcomes.<\/p>\n
AI is not a silver bullet, but it is a useful tool when applied to the right use cases. This article introduces an approach for selecting AI use cases and balancing risks and rewards.<\/p>\n
ServiceXRG defines and presents 20 possible AI for Support use cases. The dimensions for choosing which use cases to consider are described in detail following the figure.<\/p>\n
<\/p>\n
AI-enabled tools can be provided to end customers or limited to internal team access. Opportunities and risk factors for internal versus external uses cases are considered below.<\/p>\n
+<\/strong><\/span> Ability to expose organizational knowledge to internal staff resulting in faster time to expertise.<\/p>\n +<\/span><\/strong> Generative capabilities quickly create answers to share with customers.<\/p>\n +<\/span><\/strong> Ability to maintain data privacy and security.<\/p>\n +<\/span><\/strong> Low exposure of risk due to hallucinations and unexpected performance.<\/p>\n +<\/span> <\/strong>Ability to control use of tools.<\/p>\n +<\/span><\/strong> Easier to capture feedback.<\/p>\n – <\/span><\/strong>Customers continue to rely on assisted channels.<\/p>\n –<\/strong> <\/span>Support team may be reluctant to use AI-assisted tools.<\/p>\n +<\/strong><\/span> Ability to share organizational knowledge with customers, lowering reliance on assisted resources.<\/p>\n +<\/span> <\/strong>Generative capabilities dynamically create audience appropriate responses.<\/p>\n +<\/strong><\/span> Potential to lower cost of knowledge curation and management.<\/p>\n –<\/span> <\/strong>Increased exposure to hallucinations and unexpected performance.<\/p>\n –<\/span><\/strong> Sensitive data may be inadvertently used and shared.<\/p>\n –<\/span> <\/strong>Difficult to track effectiveness.<\/p>\n AI-enabled tools are relatively new to the market. Many vendors report offering turnkey solutions, yet some may not be fully vetted.\u00a0 Considerations for the level of effort and cost to implement AI use cases are listed below.<\/p>\n AGENT ANSWER ASSIST<\/strong> \u2013 Creation of a suggested technical response to customer questions to be used by agent (internal).<\/p>\n AUTO RESPONDER<\/strong> \u2013 AI generated response to digital customer communications (customer facing).<\/p>\n BRAND CONSISTENCY<\/strong> \u2013 Ability to generate suggested responses to customers that are consistent with core company messaging and branding (internal).<\/p>\n BUG TRACKING<\/strong> \u2013 Means to analyze case records and customer generated content to identify possible root causes of top bugs (internal).<\/p>\n CASE COMPLETION<\/strong> \u2013 Ability to suggest required data to add to a case as an electronic case is being created by the customer (customer facing).<\/p>\n CHAT<\/strong> \u2013 Intelligent chat bot to provide answers to customer questions or route to best qualified resources (customer facing).<\/p>\n CLASSIFICATION<\/strong> \u2013 Classification of unstructured digital customer information into defined categories (internal).<\/p>\n ENHANCED SEARCH | RETRIEVAL-AUGMENTED GENERATION (RAG)<\/strong> \u2013 Use of Large Language Model (LLM) to generate the best answer to a customer question using knowledge from existing, trusted knowledge sources. (customer facing | internal).<\/p>\n ESCALATION PREDICATION<\/strong> \u2013 Ability to identify cases that will likely require escalation (internal).<\/p>\n GEN AI FORMULATED RESULTS<\/strong> \u2013 On demand creation of an article to answer specific customer questions using sources beyond the knowledge base (customer facing).<\/p>\n GRAMMER GUIDANCE<\/strong> \u2013 Means to improve the level of digital communication with customers by providing recommendations to improve the syntax and diction of external communication (internal).<\/p>\n HEALTH INDEX<\/strong> \u2013 Analysis of cumulative customer data to determine the status of a customer relationship (internal).<\/p>\n KNOWLEDGE ARTICLE CREATION<\/strong> \u2013 Generative capabilities used to create new knowledge base articles from exiting digital data including community posts, case records, or chat sessions (internal).<\/p>\n KNOWLEDGE GAP ANALYSIS<\/strong> \u2013 Determination of the information gaps that exist between the knowledge base and submitted case records (internal).<\/p>\n PERSONALIZED RESPONSE<\/strong> \u2013 AI generated response to customer inquiry leveraging all known information about the existing customer relationship (customer facing).<\/p>\n PROFESSIONALISM MONITORING<\/strong> \u2013 Examine the level of professionalism expressed within customer communications (internal).<\/p>\n QUEUE MANAGEMENT<\/strong> \u2013 Intelligent routing of digitally submitted cases to the best qualified resources based on case contents, known customer information, and available resources (internal).<\/p>\n SENTIMENT ANALYSIS<\/strong> \u2013 Analysis of unstructured customer data to identify levels of customer satisfaction (internal).<\/p>\n SEO AND MARKETING EFFECTIVENESS<\/strong> \u2013 Ability to optimize external customer communications posted on the support portal to increase use and effectiveness (internal).<\/p>\n TRANSLATION<\/strong> \u2013 Language translation of support cases and responses (customer facing | internal).<\/p>\n","protected":false},"excerpt":{"rendered":" This article introduces an approach for selecting AI use cases and presents 20 possible support use cases.<\/p>\n","protected":false},"author":4,"featured_media":253301,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","footnotes":"","_wp_rev_ctl_limit":""},"categories":[30,50,14],"tags":[82,303,90,294,316,304,347],"class_list":["post-254046","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-transform","category-scale","category-technology","tag-ai","tag-chatgpt","tag-deflection","tag-generative-ai","tag-gpt","tag-llm","tag-use-cases","post_outcome-deliver-support-effectively","post_outcome-scale-delivery-capability","post_outcome-transform-and-innovate","post_activity-self-help-and-automation"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.servicexrg.com\/wp-json\/wp\/v2\/posts\/254046","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.servicexrg.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.servicexrg.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.servicexrg.com\/wp-json\/wp\/v2\/users\/4"}],"replies":[{"embeddable":true,"href":"https:\/\/www.servicexrg.com\/wp-json\/wp\/v2\/comments?post=254046"}],"version-history":[{"count":15,"href":"https:\/\/www.servicexrg.com\/wp-json\/wp\/v2\/posts\/254046\/revisions"}],"predecessor-version":[{"id":254722,"href":"https:\/\/www.servicexrg.com\/wp-json\/wp\/v2\/posts\/254046\/revisions\/254722"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.servicexrg.com\/wp-json\/wp\/v2\/media\/253301"}],"wp:attachment":[{"href":"https:\/\/www.servicexrg.com\/wp-json\/wp\/v2\/media?parent=254046"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.servicexrg.com\/wp-json\/wp\/v2\/categories?post=254046"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.servicexrg.com\/wp-json\/wp\/v2\/tags?post=254046"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}External<\/h3>\n
Use Case Dimensions: Level of Effort<\/h2>\n
Low Effort | Low Cost<\/h3>\n
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High Effort | High Cost<\/h3>\n
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20 AI Use Cases for Support<\/h1>\n