This is achieved by using CX assistants to manage simple tasks, thereby freeing human agents to concentrate on more complex issues.
Over time, agentic AI-enabled CX assistants can assume increasing responsibilities and eventually manage customer interactions. Typical uses in contact centers can include:
• Setting appointments.
• Data entry.
• Handling basic customer inquiries.
• Transcribing and summarizing interactions.
• Providing powerful insights through real-time conversation and data analysis.
• Intelligent routing based on context or customer profiling.
• Conducting sentiment and trend analysis for automated quality control.
For instance, agentic AI can automatically generate summaries of customer conversations and upload them to CRM systems, ensuring accurate and up-todate records. It can also facilitate faster issue resolution. For example, it can suggest relevant knowledge base articles or troubleshooting steps.
Additionally, by leveraging customer data, agentic AI delivers personalized interactions by analyzing past conversations and preferences. And by addressing simple queries and supporting human agents in real time, issues are more likely to be resolved on first contact. Thus enhancing customer satisfaction scores( CSAT) and Net Promoter Scores( NPS).
Critically, agentic AI can help improve the bottom line. By automating tasks and resolving issues efficiently, it lowers the cost per call and total service costs, which is particularly beneficial for high-volume contact centers. It also scales to handle more interactions without extra costs, which is crucial for growing businesses or managing seasonal spikes.
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IMPROVING AGENT SATISFACTION
While agentic AI transforms customer interactions, it also reshapes the role of human agents. By providing real-time assistance and automating mundane tasks, it benefits human agents in several ways.
First, agentic AI provides real-time information and recommendations during customer interactions. This enables agents to handle requests faster and more competently while reducing cognitive load and stress. For example, it can suggest responses or actions based on conversation contexts, facilitating quicker resolutions.
Second, by automating repetitive tasks, agentic AI allows agents to concentrate on more enjoyable and complex activities, thereby improving job satisfaction and the quality of customer interactions as agents can spend more time addressing unique customer needs.
Third, agentic AI alleviates the stress associated with handling high volumes of customer interactions by assisting with information retrieval, compliance tracking, and other tasks. This leads to a healthier work environment and reduced agent turnover.
Finally, agentic AI supports agent training and development by analyzing performance and offering personalized feedback and coaching. This helps them to enhance their skills and stay current with the best practices.
With agentic AI handling routine tasks, agents gain autonomy to make decisions and address complex issues. This results in higher job satisfaction and a sense of accomplishment as they can focus on delivering excellent service and creating memorable CXs.
ENABLING BETTER BUSINESS INSIGHTS
Key to achieving the benefits of agentic AI is its powerful data analysis capabilities, enabled by machine learning and large language models( LLMs), compared with current methods such as manual data review and outdated, static analysis algorithms.
• It can provide a much deeper level of insight into the CX, enabling more effective personalization, and into the agent experience, improving their performance.
• It is much more effective for alerting managers to issues such as service delays, as well as helping them spot opportunities when it comes to customer wishes.
For CX managers, there are several ways that agentic AI can achieve these outcomes depending on the business requirements.
At its core, agentic AI engines need to access the content of the interactions. This can easily be done if the contact center already has an AI tool for transcribing or summarizing calls.
These transcriptions can be fed back into AI and used as the input for the analytical process. It can be performed in real time, with the AI keeping a dashboard of the information or executed on historical data at a set interval, like at the end of the day or week.
For example, if customers are repeatedly contacting agents about a particular faulty product, the analysis will pick this up and it can be fed back to the product team who can fix the issue. The analysis could also find links between different pieces of information, such as complaints about a rise in prices correlated with a drop in subscriptions to a service.
This information is vitally important to contact center managers. These trends can represent a huge boost in customer service quality if realized in time.
In the past, businesses relied on agents to identify these trends. But with an agentic AI solution, this can be done automatically. Issues that may have gone unnoticed previously can now be addressed much faster, improving the quality of customer service and experience.
NEW TECHNOLOGIES, NEW CHALLENGES
The benefits of contact centers adopting agentic AI into their workflows are clear. However, as with all new technology, there are some downsides. But when these are managed properly, agentic AI is nothing but a force multiplier for businesses.