Why AI is here to help—not replace—customer service agents

While many prominent minds such as Stephen Hawking believe artificial intelligence is poised to replace humans, I believe today’s AI technology is more suited towards enhancing our capabilities as human beings, not taking us out of the equation altogether.

Specifically, for smart companies—those focused on delivering exceptional customer experiences—utilizing AI technology is all about adopting applications that can operate and draw inferences faster than people can. It’s also about making sure their enterprise agents always have the most useful and valuable information “at their fingertips” to best serve the customer, whether they wish to do so in a fully automated, hybrid, or human-only interaction model.

As technology continues to evolve at an impressive pace, here are four ways that smart businesses are using AI not to replace their customer service agents, but to improve their overall performance:

“<em>Through using chatbots, even small businesses can easily extend and automate a range of customer service inquiries and a
Through using chatbots, even small businesses can easily extend and automate a range of customer service inquiries and actions that would otherwise need to wait until the next business day.

Effortless self-service

Through using chatbots, even small businesses can easily extend and automate a range of customer service inquiries and actions that would otherwise need to wait until the next business day. However, an AI solution is only as smart as the data upon which it draws and, in some cases, the sophistication of the models that drive its behavior. This is hard for most businesses to plan, let alone put in place, without providing customers with a fallback option to speak with a human agent.

Companies focused on implementing digital channels are also now looking heavily towards expert systems-based chatbots and virtual assistants, which rely upon semantic analysis, natural language speech recognition, and rule-based pattern matching capabilities. These technologies are designed to provide increasingly friendly and flexible levels of automation, resulting in more natural information exchange between consumers and the business, without specific human intervention.

Smart routing and improved interactions

Mid and large enterprises are now using Big Data to provide pinpoint customer routing strategies, matching agents and callers based on past history and behaviors, and then providing those agents with real-time feedback on customer sentiment. Increasingly, businesses are also giving customer service reps instant access to proactive guidance and next-best-action suggestions consistently across voice, video, chat, email and messaging channels. Often, this includes digital experiences like mobile-delivered video clips or Augmented & Virtual Reality (AR/VR) capabilities.

By incorporating these AI technologies, there’s no more waiting on hold for customers while an agent goes to ‘look something up’. Rather than manually searching knowledgebases and other internal sources for reference materials, agents now have all the information and resources they need to bring the customer interaction to a positive conclusion, leading to increased upsell, better retention, and improved customer satisfaction metrics.

Trend spotting and sentimental analysis

Today, many companies are focused on reducing fraud and/or increasing security through the application of voice biometrics, whether through use of specific passphrases to establish identity, or more indirect methods that provide human agents with a scoring system that indicates whether the caller may be deceptive or of questionable identity based on a variety of factors.

However, in addition to ensuring the security of their customers, companies can use biometric technology to elevate offerings and enhance business processes. Using trend spotting and sentiment analysis, businesses can get real-time feedback to an agent as to how receptive a caller may be to the overall tone and tenor of the conversation.

This leads to deeper, more personal and positive interactions for those companies whose brand represents a high touch or personal relationship model. Sentiment analysis can also be used in the agent/supervisor relationship, allowing contact center managers to recognize when agents need more coaching or where energy levels may be lagging, and therefore, impacting customer relationship metrics.

Enhanced workforce optimization

AI is not only making agents better at their jobs, it’s improving the productivity and effectiveness of the call center itself. In fact, many of today’s leading omni-channel contact centers are already busy collecting an enormous amount of data on every customer interaction. Avaya has spent significant effort in developing customer journey models and capturing this history in an omni-channel datastore, Avaya Context Store, which can be mined for insight at both a macro and specific-individual level.

These insights can provide both “next step” journey guidance for a specific individual to maximize engagement, retention and upsell opportunities, as well as discovery and identification of best practices from the most proficient agents that can be leveraged as part of proactive agent guidance activities.

Everywhere you look, AI technology is helping address many of the complexities and issues faced by companies in delivering an optimal customer experience, helping businesses deliver transformational levels of revenue, cost reduction, and customer satisfaction. Instead of worrying about how AI and machine-learning technologies might one day replace humans, it’s time for all companies to now recognize that AI can only help their people deliver the exceptional levels of service their customers now expect.

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