to assist customers with basic customer service needs or concerns

systems that can analyze a customer’s question and provide the appropriate next steps or answers without need for a human agent

that uses AI-technology to match callers with the right live agent based on experience, personality, topic or more

to give agents faster insight into customer concerns and needs

to analyze more customer calls faster and provide a deeper level of analysis based on NLP, sentiment analysis and more

Implementing AI can increase agent productivity by 31%, saving about 12 hours a week in one study. But what kinds of AI tools really improve productivity? We’ve seen things like:

All of these automations also serve to increase agent engagement. When agents spend less time on tedious, repetitive tasks, they can spend more time interacting with customers, providing quality service and building relationships. This leads to higher levels of engagement with their work and less burnout and boredom.

And since more engaged agents deliver better customer engagement, this is also a win for your customers—correlating with increased customer satisfaction, brand affinity and loyalty.

According to recent research, implementing AI tools for your team can lower operational costs by as much as 59%. And no, this doesn’t mean you should immediately go fire your human agents.

Instead, AI can lower operational costs—even with the same team on board—by making your team much more effective and delivering critical business insights sooner. For example: teams who implemented real-time voice transcription found costs reduced by 13.6%. Not only were agents more efficient, as after-call work was reduced due to AI transcriptions and note-taking, but there was also clearer follow-up after each call, leading to increased customer satisfaction, better data analysis and stronger QA processes.

This is just one example of how AI tools can help lower operational costs. Aside from improving agent performance, AI tools can also:

Human agents are great at empathy, emotional intelligence, connecting with customers, problem-solving (especially complex problems) and creativity—all things that AI isn’t that great at (at least, not yet).

But what about what humans aren’t so great at? Things like:

• Tedious, repetitive tasks
• High levels of detail, especially over large quantities of work or data
• Quickly managing large data sets for processing or analysis

These are all areas where AI shines, and bringing AI into your workflow for these types of tasks can not only improve efficiency and lower operational costs (see above points), but they can also help your team greatly reduce human error and create more repeatable, consistent processes and data handling.

For example, take the process of data entry and processing—even with the best human agents on the case, the work is likely to be slow and tedious, which means that human agents will become more liable to errors and mistakes along the way. On the other hand, a machine can process data endlessly, with no tiredness and much lower potential for errors.

It makes sense that as agent efficiency and engagement improves, you can also improve service metrics such as speed of answer and average handle time.

But these aren’t only indirectly impacted by AI tools—there are also ways to implement AI tools intentionally to improve service levels. Consider:

• AI-powered interactive voice response (IVR) can help get common questions answered resulting in faster response times
• AI customer analysis can provide detailed insight into customer behavior and preferences, assisting agents with providing more personalized service
• AI-powered knowledge bases can assist agents with providing accurate answers more quickly, allowing for faster handle times and less call transfers
• AI chatbots and self-service tools can provide faster service for simple questions and route calls, providing 24/7, on-demand service for higher customer satisfaction and lower wait times

In addition, increasing self-service tools not only lowers wait times for the customers who make use of them, but, since it frees up agents to focus on more complex tasks and questions, it also lowers wait times for customers who really need to speak to a human agent.

Sentiment analysis has the potential to be one of the biggest AI supports for human agents. When employed properly, sentiment analysis can:

• quickly give agents an overview of tone and emotion of incoming callers, allowing them to respond appropriately in less time
• analyze large volumes of call transcripts or customer communications to understand high-level sentiment among your customer base
• alert managers or supervisors when an agent might need support dealing with an angry customer
• provide real-time assistance for responses and solutions based on customer emotions and previously-successful responses and mor

In short, sentiment analysis is like an emotional coach for your agents—monitoring every call and interaction in real time for word choice, tone, complexity, caller history and more, so that your agents can provide the best support every time.

While human agents may still be the best at actually providing that emotional, empathetic response, AI tools can make quick analysis of what the emotion or sentiment is, on both a micro and macro level. Having these insights not only provides support for your agents, but it also provides your team with better analysis of your customer’s feelings, thoughts and perceptions toward your brand.

Another major area where AI can shine in your call center is through improved QA and agent training.

In the past, QA has been a largely manual operation, with QA supervisors or managers reviewing calls or call transcripts one-by-one, grading them against a scorecard, and then compiling data for further analysis, agent training or review. Due to the amount of time and effort this requires, most call centers only reviewed a random sample of calls or conversations, and agent reviews typically took place days or weeks after a given conversation.

AI is changing the game for QA processes, in terms of both speed and scale. For example, augmented quality monitoring (AQM) software allows call centers to automatically record every call and uses AI tools to transcribe calls, review them, score them against a given scorecard and provide feedback and customer data, all using NLP, sentiment analysis and more. Not only does this provide a much deeper look at quality, but it also improves efficiency, reduces manual work for agents and supervisors, and drives cost-savings as a result.