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Ciscos new AI agents and assistants aim to ease customer service headaches<\/h1>\n
But AI agents are growing ever more sophisticated, showing promise in nearly every area of the enterprise, including customer service. Prior to launch, Cisco is performing an internal pilot of Webex AI Agent in its human resources department. In a demo video shared by Dhingra, a worker interacted on her phone with an AI agent and asked the agent to book time off for her in Workday. The agent asked how many days off she was planning to take, and when, then booked it. \u201cA lot of this is rooted in fragmented and siloed investments,\u201d he said (indeed, 88% of respondents to the Cisco survey reported technology siloes).<\/p>\n

From chatbots to robots: The rise of \u2018physical AI\u2019<\/h2>\n
This could involve automating warnings, messages or prompts to install updates based on alerts from other AI agents working elsewhere in the business. For example, if a number of users are having difficulty accessing a service, then other users who are likely to want to use the service could be warned beforehand, enabling them to make alternative arrangements. Ultimately, this will reduce the chance of losing customers due to poor support experiences. After all, chatbots are a flagship use case for generative AI, and the process of transitioning from human agents to automated systems began long before the emergence of language models (LLMs). A report by Harvard Business Review found that of 13 essential tasks involved in customer support and customer service, just four of them could be fully automated, while five could be augmented by AI to help humans work more effectively.<\/p>\n
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This could involve automating warnings, messages or prompts to install updates based on alerts from other AI agents working elsewhere in the business.<\/li>\n
In a support context, this means it can quickly analyze large volumes of tickets or inquiries, categorizing them according to the sentiment of the customer.<\/li>\n
After all, chatbots are a flagship use case for generative AI, and the process of transitioning from human agents to automated systems began long before the emergence of language models (LLMs).<\/li>\n
Rather than attempting to compete with it in order to stay relevant, learn how and when it can be used to boost your own efficiency and productivity.<\/li>\n
This could even take place in real-time, for example, by guiding human agents on how to respond during person-to-person interactions.<\/li>\n<\/ul>\n
Cisco\u2019s new AI agents and assistants aim to ease customer service headaches<\/h2>\n
Overall, I believe that the secret to success is to learn to treat AI as both a tool and as a partner. Rather than attempting to compete with it in order to stay relevant, learn how and when it can be used to boost your own efficiency and productivity. And focus on developing human skills that AI can\u2019t replicate when it comes to solving customer problems and improving customer experience. Generative AI can also be used to draft automated but personalized responses to email inquiries, making sure that messages carry a consistent tone while providing customers with advice relevant to their specific issues.<\/p>\n
New Nvidia technology provides instant answers to encyclopedic-length questions<\/h2>\n
The AI Agent platform will include a new design tool, AI Agent Studio, which allows contact centers to build, customize, and deploy voice or digital agents in minutes, choosing the model of their choice, Cisco said. This could help improve customer interactions and resolve issues more quickly and easily. I don\u2019t believe that we will immediately see mass human redundancy across customer support roles. After all, people will always be required to cope with unexpected and unique challenges that always occur.<\/p>\n
Over the last 18 to 24 months, Cisco has been working to improve the support experience for both customers and employees, said Dhingra. He pointed to a survey the company did with 1,000 customer experience leaders in 12 industries and 10 global markets. More than half of respondents (60%) said self-service isn\u2019t working, and 60% also reported that human agents are overworked. Further, customer experience leaders reported that 1 out of 3 of those agents lack the customer context needed to deliver the best possible customer experiences. Even in today\u2019s modern age, call center customer service continues to be a nightmare.<\/p>\n
I do, however, believe that professionals in the field who prepare themselves for the AI revolution will increase their chances of remaining useful and valued. They can be continuously kept up-to-date with the latest developments in best practices so that human agents will always have access to the most current information and insights. In addition, to support overworked humans, Cisco plans to release an agent wellness platform that will schedule automatic breaks and shift which channels agents support to increase or decrease capacity based on need. Cisco is angling to be a leader in AI-powered call center support, and Wednesday at its WebexOne 2024 event, it announced new Webex AI agents and assistants that will work alongside humans to streamline processes and address common headaches and snag points.<\/p>\n
Human interaction still critical<\/h2>\n
In a support context, this means it can quickly analyze large volumes of tickets or inquiries, categorizing them according to the sentiment of the customer. This could even take place in real-time, for example, by guiding human agents on how to respond during person-to-person interactions. Perhaps one of the most obvious applications \u2013 and certainly one we\u2019re seeing enthusiastic adoption of \u2013 is chatbots. In the past, most of us will probably have experienced the frustration of dealing with slow, clumsy and far-from-intelligent voice recognition and automated customer support technology. Today, thanks to the application of chatbots built on LLMs, bots can have conversations that are close to being as dynamic and flexible as those of humans.<\/p>\n
However, they will also become capable of providing personalized and instant responses across many more in-depth and edge-case customer support situations. This might be those needing case-specific knowledge not found in data the AI can access, multi-faceted problems or those that require input and collaboration from different departments. Predictive customer support will focus on solving customer issues before they are even raised.<\/p>\n