Global businesses are pumping funds into generative AI (GenAI) use cases for customer service.<\/p>\n
Indeed, only software development and marketing teams have experienced greater GenAI investment than customer service \u2013 according to Gartner research<\/a>.<\/p>\n Why? Well, many tangible use cases were already in the space before the advent of the tech. Instead, natural language processing (NLP) engines powered them, not GenAI.<\/p>\n However, the ability of a large language model (LLM) \u2013 like ChatGPT – to extract context and entities from customer conversations on the fly has removed the requirement to spend hundreds of hours engineering those NLP solutions.<\/p>\n As such, GenAI has made capabilities such as case summarization, sentiment tracking, and customer intent modeling much more accessible and cost-effective.<\/p>\n Yet, it has also brought many new-fangled use cases to life. These aim to enhance many facets of customer service, from workforce engagement management (WEM) to conversational AI.<\/p>\n The following 25 GenAI use cases exemplify the tech\u2019s impact on the customer service space.<\/p>\n Example: Salesforce Einstein AI<\/strong><\/p>\n Generative AI understands customer intent. That capability sits at the core of many new customer service use cases for the technology \u2013 such as auto-generating customer replies.<\/p>\n Indeed, GenAI applications \u2013 like Service GPT by Salesforce \u2013 can do this by first understanding the customer query and sieving through various knowledge sources looking for the answer.<\/p>\n Such knowledge sources likely include web links, the knowledge base, CRM, and various other customer databases \u2013 which may also allow for personalization.<\/p>\n In trawling these, GenAI automates a relevant customer response, which the agent can evaluate, edit, and forward to customers.<\/p>\n That final part is crucial, keeping a human in the loop to lower the risk of responding with incorrect information and protecting service teams from GenAI hallucinations.<\/p>\n Example:<\/strong> Expanding Agent Replies by Zendesk <\/strong><\/p>\n Before LLMs burst onto the scene, many people played with generative AI when using tools like Gmail. Indeed, the email tool predicts how a sentence will likely end, and \u2013 if it guesses right – the user can hit the “tab” button, and it\u2019ll complete their message.<\/p>\n Embracing the advent of large language models (LLMs), Zendesk built a customer service version of this \u2013 on steroids.<\/p>\n Its “expanding agent replies” solution allows agents to type the bare bones of their response and then fleshes it out for them, saving them time in responding to customers across digital channels.<\/p>\n Again, the contact center must plug the solution into various knowledge sources for this to happen \u2013 as is the case across many other use cases \u2013 and an agent stays in the loop.<\/p>\n Example:<\/strong> Call Note Automation by Sprinklr AI+<\/strong><\/p>\n Agents often receive a barrage of information, which they must remember. Yet, keeping track of all the critical details is tricky.<\/p>\n As such, many supervisors encourage note-taking. However, even that can impede an agent\u2019s ability to engage in active listening<\/a> as they multi-task, resulting in increased resolution times.<\/p>\n Sprinklr\u2019s “call note automation” solution aims to overcome this issue by jotting down crucial information as the customer talks.<\/p>\n Agents can refer to these golden nuggets of information when forming their replies \u2013 instead of relying on the unstructured, bulky transcript.<\/p>\n Knowing this, they can stay focused on what the customer is saying, not trying to remember what they said previously, which should improve their call handling.<\/p>\n Example: Generative FAQ for CCAI Insights by Google Cloud<\/strong><\/p>\n As generative AI monitors customer intent, many vendors have built dashboards that track the primary reasons customers contact the business and categorize them.<\/p>\n With this information, contact centers can understand their primary demand drivers.<\/p>\n This enables the service team to prioritize actions to improve contact center journeys. Such actions may include improving agent support content, solving upstream issues, or adding conversational AI.<\/p>\n Google Cloud\u2019s Generative FAQ for CCAI Insights allows contact centers to upload redacted transcripts to unlock this capability. The tool may also generate conversation highlights, summaries, and a customer satisfaction score to store in the CRM.<\/p>\n Example:<\/strong> Auto-summarization for Genesys Copilot<\/strong><\/p>\n When a service agent ends a customer interaction, they must complete post-call processing. That typically involves uploading a contact summary and disposition code to the CRM system.<\/p>\n Generative AI solutions can now automate this process, shaving seconds from every contact center conversation and \u2013 therefore \u2013 saving the service operation significant resources.<\/p>\n CCaaS Magic Quadrant<\/a> leader Genesys is one vendor to offer such a solution \u2013 automating these post-call processes for agents to review, tweak, and publish in the CRM after each conversation.<\/p>\n Moreover, the vendor standardizes the format of each conversation summary. That makes it easier for future agents \u2013 handling follow-ups – to get to grips with what happened on the previous call.<\/p>\n Example: The Verint Interaction Transfer Bot<\/strong><\/p>\n When a contact escalates, the customer must often repeat their problem and the information they shared with the first agent \u2013 which is a common source of customer frustration<\/a>.<\/p>\n Yet, generative AI can help by summarizing the contact so far and sending that to the second support agent or supervisor.<\/p>\n As a result, they can continue the conversation from where it broke down, saving time and preventing the customer from repeating themselves.<\/p>\n The Verint Interaction Transfer Bot does precisely that. No matter if the first agent is a human or a bot, it sends a quick, informative summary rather than an unwieldy transcript.<\/p>\n Example:<\/strong> Five9 <\/strong>AI Insights<\/strong><\/p>\n By uncovering customer FAQs, generative AI helps contact centers spot opportunities for conversation automation. Yet, AI Insights by Five9 takes this further.<\/p>\n The solution takes customer conversations and groups them by various traits, like intent.<\/p>\n From there, it applies GenAI and NLP to search for patterns within these groups of contacts, suggesting process and automation improvement opportunities.<\/p>\n In doing so, the tool indicates how often these opportunities present themselves and the possible cost-savings the contact center can make by acting on them.<\/p>\n Example: The Verint Quality Template Bot<\/strong><\/p>\n After years of call and contact monitoring and CSAT\/sentiment analysis, experienced team leaders and quality analysts understand what an excellent customer conversation looks like.<\/p>\n Nevertheless, transferring that knowledge into specific, measurable, and fair quality assurance (QA) scorecard criteria is easier said than done, not to mention time-consuming.<\/p>\n GenAI can help here via solutions like the Verint Quality Template Bot. With this, a QA leader can input simple prompts as to what a top-notch customer-agent interaction looks like on a specific channel.<\/p>\n Then, the GenAI-infused solution will scour historical contacts to spot phrases and behaviors that reflect those prompts on the channel to auto-build a QA scorecard.<\/p>\n Finally, the QA team can review, edit, and finalize that scorecard before repeating the process across other channels (and perhaps specific customer intents).<\/p>\n Example:<\/strong> Manager Assist for Amazon Connect<\/strong><\/p>\n Many CCaaS providers now offer the capability to automate quality scoring, giving insight into all contact center conversations. This provides a more holistic view of agent performance.<\/p>\n Generative AI takes this further by not only automating “what happened” questions \u2013 i.e., did the agent say this or do that? \u2013 but additional criteria too.<\/p>\n The Manager Assist for Amazon Connect solution does this by harnessing GenAI to auto-fill more subjective scorecard criteria such as: Did the customer leave the call satisfied? Or: Did the agent offer any concessions?<\/p>\n Alongside this, the solution provides a rationale for the automated answer in case quality analysts, supervisors, or coaches wish to delve deeper or an agent wants to challenge it.<\/p>\n Example:<\/strong> Manager Assist for Amazon Connect<\/strong><\/p>\n Alongside auto-filling more of the quality scorecard (as above), Manager Assist for Amazon Connect provides an automated agent performance summary for every customer conversation. That summary includes coaching and positive recognition opportunities.<\/p>\n Indeed, in an example that AWS shared,<\/a> the solution generated feedback such as:<\/p>\n The innovation also inspires cooperation between quality assurance and coaching teams, who can create a connected learning strategy<\/a> to bolster agent performance.<\/p>\n Example: NICE Enlighten Actions<\/strong><\/p>\n Generative AI unlocks several chances to turn insight into action \u2013 including insights that conversational intelligence tools uncover.<\/p>\n For instance, NICE uses such tools to detect customer sentiment in real time.<\/p>\n1. Auto-Generating Customer Replies<\/h2>\n
2. Assisting Agents as They Type<\/h2>\n
3. Automating Note Taking<\/h2>\n
4. Unearthing Customer FAQs<\/h2>\n
5. Automating Post-Call Processing<\/h2>\n
6. Simplifying Call Transfers and Escalations<\/h2>\n
7. Detecting Customer Service Automation Opportunities<\/h2>\n
8. Developing Quality Assurance Scorecards<\/h2>\n
9. Adding Context to Automated Quality Scoring<\/h2>\n
10. Pinpointing Agent Coaching Opportunities<\/h2>\n
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11. Alerting<\/strong> Supervisors to Agent Issues<\/strong><\/h2>\n