{"id":532236,"date":"2024-08-01T16:31:15","date_gmt":"2024-08-01T23:31:15","guid":{"rendered":"https:\/\/www.genesys.com\/blog\/post\/genesys-cloud-agent-copilot-deep-dive"},"modified":"2024-08-05T16:40:18","modified_gmt":"2024-08-05T23:40:18","slug":"genesys-cloud-agent-copilot-deep-dive","status":"publish","type":"blog","link":"https:\/\/www.genesys.com\/en-sg\/blog\/post\/genesys-cloud-agent-copilot-deep-dive","title":{"rendered":"Genesys Cloud Agent Copilot Deep Dive"},"content":{"rendered":"

[vc_section full_width=”stretch_row”][vc_row][vc_column][vc_column_text css=””]As advancements in self-service tools have prevented many \u201ceasy\u201d questions from ever reaching an agent, customer service teams are instead left to answer more complex, nuanced and personalised questions from customers who reach out directly for support via a phone call, text, email or other channel. For agents, the stakes have never been higher.\u00a0<\/span>\u00a0<\/span><\/p>\n

Novice agents often lean heavily on existing knowledge bases to support the answers they give to customers. And even veteran agents need direct support from knowledge articles. Manual search options typically yield good results and connect agents with information, but they take time.\u00a0<\/span>\u00a0<\/span><\/p>\n

That\u2019s a thing of the past. Genesys Cloud Agent Copilot, part of the Genesys Cloud<\/span>\u2122<\/span>\u00a0platform, delivers the most advanced agent-assistive technology ever deployed to contact centres to support <\/span>AI-Powered Experience Orchestration<\/span><\/a>.\u00a0<\/span>\u00a0<\/span><\/p>\n

Genesys Cloud Agent Copilot supports agents as they work through customer interactions from start to finish. In the contact centre, it surfaces knowledge for agents, without them needing to search for it, based on the content of the conversation. And because it takes notes on customer interactions and presents them to the agent to review, it also helps to shorten after-call work.<\/span>\u00a0<\/span><\/p>\n

Using generative, conversational and predictive AI, Genesys Cloud <\/span>Agent Copilot<\/span> supports teams during and after customer interactions on any channel by:\u00a0<\/span>\u00a0<\/span><\/p>\n

    \n
  1. Surfacing knowledge<\/span>\u00a0<\/span><\/li>\n
  2. Capturing agent suggestions on knowledge improvements\u00a0<\/span>\u00a0<\/span><\/li>\n
  3. Transcribing conversations<\/span>\u00a0<\/span><\/li>\n
  4. Providing custom scripting<\/span>\u00a0<\/span><\/li>\n
  5. Presenting the correct form or workflow process document<\/span>\u00a0<\/span><\/li>\n
  6. Suggesting wrap-up codes<\/span>\u00a0<\/span><\/li>\n
  7. Writing a summary of the interaction<\/span><\/li>\n<\/ol>\n

    Automatically Suggest Relevant Knowledge<\/h2>\n

    With Genesys Cloud Agent Copilot, no agent \u2014 no matter their experience \u2014 has to search for knowledge. It automatically appears in their workspace when they need it.\u00a0<\/span>\u00a0<\/span>[\/vc_column_text][vc_row_inner][vc_column_inner width=”1\/2″][vc_column_text css=””]<\/p>\n

    Without a C<\/span>opilot<\/span><\/span><\/h4>\n

    Agents must understand<\/span> customers<\/span>\u2019<\/span> intent and <\/span>know <\/span>what piece of knowledge <\/span>can help<\/span> support them. They <\/span>need to <\/span>manually type search terms<\/span> and<\/span> scan articles that come up for relevance<\/span>. They need to <\/span>find the correct information and <\/span>then relay it to the customer<\/span> \u2014<\/span> all while multitasking and chatting with customers<\/span> or while the customer waits on hold<\/span>.\u00a0<\/span><\/span>\u00a0<\/span>[\/vc_column_text][\/vc_column_inner][vc_column_inner width=”1\/2″][vc_column_text css=””]<\/p>\n

    With Genesys Cloud Agent Copilot<\/span><\/span><\/h4>\n

    Agents <\/span>chat<\/span> back and forth with customers, and refer to their<\/span> Genesys Cloud Agent <\/span>C<\/span>opilot panel, which is constantly refreshed <\/span>with relevant knowledge <\/span>based on the context of the customer-agent conversation<\/span>.<\/span>\u00a0<\/span><\/span>[\/vc_column_text][\/vc_column_inner][\/vc_row_inner][\/vc_column][\/vc_row][vc_row][vc_column][vc_column_text css=””]Sometimes, a knowledge article presented to an agent is lengthy. Genesys Cloud Agent Copilot knows which passage is relevant to the agent and highlights the section. This saves time and, most importantly, keeps the original article intact, which ensures accuracy.[\/vc_column_text][vc_single_image image=”530947″ css=”.vc_custom_1721934719171{margin-top: 1.5em !important;border-bottom-width: 1.5em !important;}”][vc_row_inner][vc_column_inner][vc_column_text css=””]<\/p>\n

    Provide Next-Best Action Guidance for Any Interaction<\/h2>\n

    Customer interactions are unique, yet the playbooks and scripts that agents work from are designed to be one-size-fits-all. By recognising the customer\u2019s intent based on what they\u2019ve said, Genesys Cloud Agent Copilot automatically presents agents with whatever they need next \u2014 a form, a script, a disclaimer to read, a tool that checks an order status \u2014 to progress the customer toward their desired outcome.[\/vc_column_text][\/vc_column_inner][\/vc_row_inner][vc_row_inner][vc_column_inner width=”1\/2″][vc_column_text css=””]<\/p>\n

    Without a Copilot<\/h4>\n

    Agents use their own expertise and consult with one another to find the right form or ask the right question. This method can work, but also results in low first-contact resolution rates, high wait and hold times, and inconsistent experiences, among other frustrations. It also requires agents to spend time hunting down the right material to complete the customer\u2019s request.[\/vc_column_text][\/vc_column_inner][vc_column_inner width=”1\/2″][vc_column_text css=””]<\/p>\n

    With Genesys Cloud Agent Copilot<\/h4>\n

    Agents chat back and forth with customers; as they do this, Genesys Cloud Agent Copilot offers script prompts and forms directly in their workspace.[\/vc_column_text][\/vc_column_inner][\/vc_row_inner][\/vc_column][\/vc_row][vc_row][vc_column][vc_single_image image=”530829″ css=”.vc_custom_1721913550605{margin-top: 1.5em !important;border-bottom-width: 1.5em !important;}”][vc_row_inner][vc_column_inner][vc_column_text css=””]Agents who work with Genesys Cloud Agent Copilot spend less time doing mundane, repetitive tasks than agents who don\u2019t have the power of AI at their fingertips. As a result, they can provide more empathetic and personalised service.<\/p>\n

    Save Time During and After Customer Interactions with Auto-Summarisation<\/h2>\n

    Keeping high-quality notes about what goes on in every customer interaction is tedious. Most agents work on their \u201cafter-call\u201d notes throughout a customer interaction by jotting down things they want to remember to capture in the summary. So, despite being called an \u201cafter\u201d interaction task, this note-taking takes time and agent attention as they interact with customers.[\/vc_column_text][vc_single_image image=”530830″ css=”.vc_custom_1721913177621{margin-top: 1.5em !important;margin-bottom: 1.5em !important;border-bottom-width: 1.5em !important;}”][\/vc_column_inner][\/vc_row_inner][\/vc_column][\/vc_row][vc_row][vc_column width=”1\/2″][vc_column_text css=””]<\/p>\n

    Without a Copilot<\/strong><\/h4>\n

    Agents take notes as they speak to customers, multitasking so they don\u2019t miss something in the summary. Customers can tell when agents are distracted and busy, and using filler words like \u201cM<\/span>mmhmm<\/span> okay\u2026\u201d.<\/span> After the interaction ends, <\/span>agents often write <\/span>notes as fast as they can so they can move onto the next customer<\/span>, calling upon the quick notes they made during the interaction. With this, they’re likely to miss key details, especially during long calls. <\/span><\/span>\u00a0<\/span>[\/vc_column_text][\/vc_column][vc_column width=”1\/2″][vc_column_text css=””]<\/p>\n

    With Genesys Cloud Agent Copilot<\/h4>\n

    Agents spend the entirety of every customer interaction speaking to the customer, knowing that the copilot is keeping track of what has happened. When the interaction ends, agents are presented with notes to read, edit if necessary, and save.[\/vc_column_text][\/vc_column][\/vc_row][vc_row][vc_column][vc_column_text css=””]Genesys Cloud Agent Copilot uses generative AI to write a summary of interactions on voice and digital channels. Agents who use the technology don\u2019t have to write notes at all, they simply review what the copilot drafted, make edits if they chose, and save the notes to the system of record.[\/vc_column_text][vc_single_image image=”530830″ css=”.vc_custom_1721934954907{margin-top: 1.5em !important;border-bottom-width: 1.5em !important;}”][\/vc_column][\/vc_row][vc_row][vc_column][vc_column_text css=””]Time saved is just one benefit to auto-summarisation. Another is improved note quality. Agents who feel the pressure of keeping their performance metrics aligned with goals can make spelling mistakes or leave out key details. Additionally, agents sometimes create well-intended notes that are difficult to understand. When a customer makes a second or third contact with the company, or if an interaction gets transferred or escalated, the agent handling that interaction relies on quality notes to loop them into what\u2019s going on with the customer and their case. But notes that are full of errors aren\u2019t helpful at all. AI-generated notes are written consistently \u2014 and agents have more time to review and edit notes as necessary \u2014 improving overall quality.<\/p>\n

    Auto-summarisation and AI-generated notes also ensure data quality for analytics. AI doesn\u2019t make spelling errors; it spells out or uses consistent acronyms and numbers, according to how you train it. The data that comes from AI summaries is much cleaner and easier to use for analytics versus summaries humans have written.<\/p>\n

    AI Speeds Wrap-Up Code Work<\/h2>\n

    An important data point for every contact centre administrator is wrap-up codes. But the practical reality of selecting a wrap-up code is a hassle for every agent.<\/p>\n

    Genesys Cloud Agent Copilot creates a prioritised short list of wrap-up codes that it recommends for the agent to select from at the conclusion of each customer interaction. This reduces the time agents spend searching for and selecting wrap-up codes \u2014 and it improves the consistency and data quality of the codes.[\/vc_column_text][vc_single_image image=”530834″ css=”.vc_custom_1721913645672{margin-top: 1.5em !important;border-bottom-width: 1.5em !important;}”][\/vc_column][\/vc_row][vc_row][vc_column width=”1\/2″][vc_column_text css=””]<\/p>\n

    Without a Copilot: For the Agent<\/h4>\n

    Agents finish an interaction, write notes and then scroll-scroll-scroll to select the correct wrap-up code. There\u2019s often more than one code that could apply, so the agent just picks what makes the most sense to them. This is all done in isolation.[\/vc_column_text][\/vc_column][vc_column width=”1\/2″][vc_column_text css=””]<\/p>\n

    With Genesys Cloud Agent Copilot: For the Agent<\/h4>\n

    Agents finish an interaction and select a suggested wrap-up code with one click.[\/vc_column_text][\/vc_column][\/vc_row][vc_row][vc_column width=”1\/2″][vc_column_text css=””]<\/p>\n

    Without a Copilot: For the Administrator<\/h4>\n

    Rely on training of all agents to make wrap-up code selections consistent enough that the data rely on for business decisions is accurate. Find that in the data, many agents just select the default wrap up code for lack of a better <\/span>option<\/span>.\u00a0<\/span>[\/vc_column_text][\/vc_column][vc_column width=”1\/2″][vc_column_text css=””]<\/p>\n

    With Genesys Cloud Agent Copilot: For the Administrator<\/h4>\n

    Wrap up codes are applied consistently, so data is more trustworthy and better suited for analytics and planning.[\/vc_column_text][\/vc_column][\/vc_row][vc_row][vc_column][vc_column_text css=””]Wrap-up code suggestions are a perfect example of AI working alongside agents for better results. When AI does the first pass at selecting the wrap-up code \u2014 culling list of options from dozens to two or three \u2014 it allows people to work on higher-value work for which they\u2019re better suited.<\/p>\n

    Veteran agents prefer to use suggested wrap-up codes to shorten their after-call workload. And novice agents require less training on wrap-up codes in general, since they have the support of copilot assisting their work.<\/p>\n

    Improved Agent Interactions for Better Customer Experiences<\/h2>\n

    Genesys Cloud Agent Copilot makes agents more efficient and, ultimately, that improves the overall customer experience. \u00a0By serving agents the right information at the right time, suggesting what action they should take next, and doing the heavy-lifting for after-interaction work, it reduces wait times for customers, improves the quality of service that agents provide and enriches the day-to-day experience for agents.<\/p>\n

    Learn more<\/a> about Genesys Cloud Agent Copilot today.[\/vc_column_text][\/vc_column][\/vc_row][\/vc_section]<\/p>\n<\/div>","protected":false},"excerpt":{"rendered":"

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