What AI in call centers means
Artificial intelligence is used to automate and enhance various aspects of customer interactions and support services in call centers. There are a variety of different options when it comes to AI usage in a call center.
- Virtual Assistance and Chatbots
- Natural Language Processing (interpreting what’s happening on calls)
- Sentiment Analysis (analyzing the customer’s tone during calls)
- Call Routing
- Predictive Analytics
- Self-Serve Options
And even more! AI is all around us and some may not even realize how much it is being used in everyday life.
Who can benefit from AI
Many people think AI is replacing jobs.
That’s not entirely false. What AI is allowing is for agents, managers, and leaders to level up their game and become more productive.
Researchers found that agents with the AI assistant achieved a significant boost of 13.8 percent in worker productivity, specifically in chat resolution rates per hour. Plus, positive interactions between customers and agents increased as reflected in the uptick of positive sentiments expressed in their messaging from AI.
AI for Managers/Supervisors
Time-consuming tasks like call reviews and agent performance monitoring can take hours. With AI, call center managers can now optimize their time and spend more of it focusing on their agents.
Being able to quickly see top performers, those agents who are struggling, and predictive outcomes in relation to their goals, gives managers the tools to improve their one-on-one coaching with agents.
AI for Agents
For agents who embrace AI, their performance will significantly improve. Receiving real-time coaching while on calls and understanding where they could have improved quickly between calls are two ways AI is helping agents.
Additionally, AI reduces the workload and call volume needed for agents to successfully hit their targets.
AI for Leaders/Owners
The biggest win for leaders is data-driven decision making. AI allows leadership to make informed decisions about processes and policies based on what is actually happening on calls with customers.
This leads to a better understanding of their customers ‘ needs and how their call center is impacting revenue and customer loyalty.
What problems AI is solving in the next five years
By leveraging AI technology in call centers, companies can deliver faster, more efficient, and even more personalized customer experiences. All while optimizing their operational efficiency.
Here is more about what you can expect to see in the next five years:
Customer Support and Automation
AI-powered chatbots and assistants will handle routine customer inquiries to provide real-time responses to customers. This will reduce the workload for current agents and improve first call resolution times.
The range of capabilities that this type of technology can provide is increasing every day.
Improving the Overall Experience
AI in call centers has improved the overall experience tenfold. More personalization and faster response times have led to higher customer satisfaction.
The vast amount of data that AI can gather and analyze has allowed agents to provide a tailored response to the customer or prospect.
This is a HUGE undertaking. Think about how different each person says a specific phrase. Sentiment analysis AI can analyze the tone of the interaction and help agents give the best response based on the tone the customer is using.
This enables prompt interventions if a conversation starts to go south and overall better management of the emotions behind agents in a call center.
AI in call centers is revamping the way QA is done. Being able to monitor and assess every phone call, chat, and email has removed the redundancy of the manual process QA teams have to go through.
QA managers can now be notified in real-time when a call falls out of compliance. This allows for quick remedies and training to be done with the agents.
By automating the routine tasks outlined in the above paragraphs, AI will help call centers reduce operational costs and better allocate their resources.
Think agent attrition and absenteeism, overhead for QA teams, etc.
Customer reaction to AI in call centers
While AI is transforming the way call centers do business and handle customer interactions, there are also some negative aspects that need to be addressed.
We’ll take a look at both positive and negative reactions to AI in the eyes of customers.
- Speediness: Customers appreciate being able to receive the RIGHT answer, and quickly. Nobody wants to call customer support back because of an incorrect response.
- 24/7 Availability: The most routine questions can be answered at any point during the day or night. AI enables this around-the-clock support.
- Consistency: When it comes to AI in call centers, you know the answers will be consistent. You don’t have to worry about a “new” agent or someone who doesn’t know the answer.
- Not A Real Person: Some people don’t want to speak to AI, plain and simple. It doesn’t matter their inquiry, they want to speak with a real person.
- Limited Problem-Solving Ability: When it comes down to problem-solving, as of right now AI can only do so much. Human intervention is still highly requested for complex issues. Plus humans can show empathy and understanding if the situation is not ideal for the customer.
- Language & Understanding: If someone has a thick accent or has trouble explaining their issue, it could result in frustration from a chatbot. This is where human interaction can make a big difference.
Takeaways of AI in Call Centers
As more and more companies are using AI, the benefits seem clear. However, it is such a new realm for most.
We don’t know the long-term impact of such a powerful technology and its effect on the call center workforce.
All we can look at is the immediate return that call centers are seeing in regards to using some type of AI in their operations.
As suggested, try to review both the positive and negative implications before integrating new technology into your business.
As generative AI and foundation models more broadly improve performance, more of these tools are finding their way into the workforce. But thus far, few studies have examined in any large scale their impact on productivity, organizational structure, or morale, the authors note.
Source: Stanford University