These stages are not fixed because every business is different , but they do help illustrate the varying levels to which AI can be used within a contact center and how the value only grows as your adoption matures . Even an AI tool that automates your most basic , foundational tasks can generate great ROI .
Of course , it ’ s easy to tell you that AI is great in contact centers and capable of driving significant results at every stage . To better understand its efficacy , it ’ s time to explore some real-world use cases so that we can show you .
THE MOST EFFECTIVE AI CONTACT CENTER USE CASES
If you ’ re interested in unlocking the power of AI Agents within your contact center , you need to identify the specific use cases that AI can help with . At this early stage , you may struggle to do this because you haven ’ t actually seen what an AI Agent is capable of .
That ’ s why it ’ s a good idea to explore some generic use cases built around the common challenges most contact centers encounter . In all of these examples , the fundamental ‘ system ’ behind the AI is the same – the role that the AI plays changes depending on which processes you apply it to .
With that in mind , here are some of the most compelling use cases for AI within a contact center environment …
CALL MONITORING
By combining speech-to-text ( STT ) with AI , you can create a solid call monitoring solution . This is a passive task because the AI isn ’ t changing anything or interacting directly with anyone . However , it is still one of the most valuable in terms of time savings and cost-efficiency , which mostly come from reducing the labor burden on your human team .
To say AI is simply transcribing a call is a gross understatement . AI takes a far more active role in even this passive task , using Generative AI and Conversational AI to understand the caller ’ s intent , analyze their sentiment , and create a summary afterward .
Though even this basic AI role can deliver big performance improvements to your contact center , the benefits don ’ t stop there . Your AI will retrieve text from an STT system then analyze it to recognize intent and begin making suggestions . It can also generate insights such as sentiment analysis to be used in reporting .
For example , if the AI detects a query in a customer ’ s call , it can generate an answer or appropriate resource which is sent to the agent in real-time , dramatically speeding up favorable resolutions .
THOUGH IT PAYS TO BE SKEPTICAL ABOUT NEW TECHNOLOGY , THERE COMES A POINT WHEN VALUE AND UTILITY BECOME TOO COMPELLING TO IGNORE .
WHY USE AN AI AGENT FOR CALL MONITORING ?
• Automatically transcribes calls in real time .
• Can create summaries or logs in any format or template you require .
• Understands context , intent , and user sentiment .
• Can take follow-up actions such as routing a query to an agent .
In Action :
In a UK-based insurance provider ’ s contact center , human agents were tasked with creating call summaries . Even using a pre-defined format and minimizing labor requirements , the task still took between 5-10 minutes after each call .
The agents were under constant pressure to resolve calls and move on to the next customer , so this logging task was viewed as a chore and was often either done poorly or ignored entirely .
By incorporating an AI Agent into their team , the insurance provider was able to remove this manual burden from the human agents . The AI monitored every call and summarized them in the required format in a matter of seconds . The agent would then perform a quick final check before storing the log .
Not only did this improve agent satisfaction , but it also improved overall productivity and utilization because agents were saving valuable minutes per call . The AI Agent ’ s summaries , which included sentiment analysis , then acted as a valuable resource for the management team to review overall performance .
IDENTIFICATION & VERIFICATION ( IDV )
Identifying the customer , determining their problem , and verifying personal information is essential in every customer interaction . However , this process wastes valuable time and frustrates everyone involved .
An AI Agent tasked with performing IDV can eliminate initial waiting times and immediately begin verifying a user . 33 % of customers report that their biggest frustration is waiting on hold , which shows why having an AI Agent that can reduce waiting times to zero is appealing .
How you use AI to change your IDV process will depend on your organization . A contact center can often utilize an AI-powered chatbot to take a user through basic IDV without them even making a call . This empowers the user AND reduces total call volume and pressure on agents .
Chatbots are far from the only application . You can also deploy an AI Agent that uses Conversational IVR to field live customer calls . It can take a user through the initial verification process and then recognize context and intent to either resolve a simple task automatically ( such as a password change or address update ) or elevate the call to an agent .
OCTOBER 2024 15