Today, you can’t open your newsfeed without seeing a headline about AI. Many people are excited about AI, comparing the technology to exciting historical advances like the printing press. Others are scared, comparing AI to horrifying disasters, like the atomic bomb. Some think it’s here to take our jobs, while other think it’ll make our jobs better. Whatever your opinion, it’s clear that AI is something many business leaders will need to get educated on quickly. We’ve created this guide as a starting point.
The truth is that AI, in and of itself, isn’t good or bad. It’s simply a tool, and it’s actually something that’s been around since the 1960s. It just wasn’t getting much press until ChatGPT came on the scene. With its easily accessible open-source AI, ChatGPT brought a newer type of AI – generative AI – to the masses. And that’s really the AI that everyone is talking about. Conversational AI, on the other hand, has been a quiet mainstay in contact center technology like conversational chatbots for quite a while.
Other forms of AI or machine learning (ML) have long been used in contact centers and customer experience strategies to automate manual tasks and increase productivity. As AI advances by leaps and bounds, so too do the opportunities to improve customer experience, reduce costs, and increase productivity by leveraging that AI. In this guide, you’ll learn:
- Common AI terms
- Contact center AI use cases
- AI risks
- AI benefits
- What’s next for AI in CX
What is Contact Center AI?
Contact center artificial intelligence or CCAI is actually a very broad category that contains multiple subcategories like machine learning (ML), natural language processing (NLP), natural language understanding (NLU), conversational AI, and even generative AI. These technologies may be used separately or together to provide a more personalized and streamlined customer experience by automating routine tasks, providing self-service options, and enabling agents to handle more complex issues.
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Different Types of Contact Center AI: Terms Defined
Artificial intelligence encompasses many different technologies. Since this is an introductory guide, we’ll define some of the terms you’re most likely to hear in conversations about contact center AI.
Conversational AI: Conversational AI is designed to engage in back-and-forth interactions, like a conversation, between humans or other machines in a natural language. Conversational AI is often used to collect information, accelerate responses, and augment an agent’s capabilities.
Generative AI: This type of AI involves programming a computer to replicate a human mind in order to create new content. Generative AI takes data from a training set and then generates new data based on the patterns and characteristics of the training set.
Large Language Model (LLM): A large language model is an advanced artificial intelligence system that uses deep neural networks and massive amounts of training data and millions (if not billions) of parameters tuning its predictions to generate human-like text based on the input it receives. It can perform various natural language processing tasks, such as translation, summarization, and question-answering, and is trained to understand and generate text across a wide range of topics.
Natural Language Processing (NLP): Everything from speech recognition and language translation to sentiment analysis and text classification falls under the NLP umbrella, which programs computers to process and analyze human language.
Natural Language Understanding (NLU): A subset of NLP, NLU focuses on the comprehension and interpretation of human language by machines. NLU involves the deeper understanding of the meaning, context, and intent behind a given piece of text or speech.
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What Are the Use Cases for Contact Center AI?
Contact center AI can be difficult to explain because it isn’t just one thing. It’s an accelerator that amplifies valuable CX outcomes, like accessibility, speed, efficiency, consistency, personalization, and learning.
That said, we’re just scratching the surface for AI’s potential in the contact center. As AI – especially generative AI – becomes more customized and readily accessible at scale, we’ll no doubt see further innovations.
The following is a list of capabilities that’s sure to grow, yet still represents some of the big places where we already see AI having a real impact in the CX space today:
Automate simple/repetitive tasks: Automatic transcription, call summarization, and other AI-enhanced automations can free up your contact center agents to focus on harder-to-handle customer inquiries and give them time for other higher-value tasks.
Chatbots and virtual agents: Chatbots and virtual agents powered by conversational AI are designed to interact with customers conversationally by using natural language processing (NLP). These bots can handle more complex inquiries than their predecessors, which were rules-based chatbots and only able to respond to specific inputs. They can also operate across channels and improve their responses over time.
Interactive Voice Response (IVR) Systems: When built using conversational AI and NLP, today’s IVR systems allow callers to simply state the reason they’re calling rather than listening to a laundry list of reasons. The IVR can then use intelligent routing capabilities to direct the caller to the right agent or simply resolve the request.
Sentiment Analysis: How can a computer tell how a customer is feeling? Sentiment analysis. Using natural language processing (NLP), sentiment analysis detects positive or negative cues in customer conversations in speech or text. Understanding how customers feel allows automation systems or agents to respond more appropriately.
Intelligent Routing: Also known as skills-based routing, intelligent routing technology powered by AI collects customer inquiries through voice, digital, or social channels, and then applies rules to route the inquiry to the agent best equipped to answer the question or resolve the issue.
Personalization: Showing your customers that you know them and understand them is one of the bedrocks of great customer service. With AI, businesses can do this at scale. AI systems can gather data from many sources, including customer profiles, purchase history, browsing behavior, social media activity, and more to identify patterns and create a comprehensive understanding of each customer. This data can then be used to proactively predict customers’ needs.
Fraud Detection: One of the main ways contact centers can use AI for fraud detection is through voice biometric authentication. By using a person’s voice to authenticate them, businesses not only have another secure way to determine a person’s identity, they can also improve the customer experience by reducing the friction that comes from answering endless security questions. AI algorithms can also aid in fraud detection by analyzing historical customer interactions and comparing new interactions with known fraud cases to flag suspicious activities.
Translation: By using neural machine translation (NMT), AI translation goes beyond word-for-word translation to better understand full phrasing, context, and complex sentence structures. When applied to the contact center, AI translation has the ability to allow agents to communicate effectively with customers even when they don’t speak the same language.
Predictive Analytics: While predictive analytics have long been a staple of contact center operations to help anticipate future outcomes and proactively prepare, this discipline gets a big boost from AI. With AI-enhanced predictive analytics, contact center operations can analyze more data more quickly, recognize patterns in data, and enable real-time decision making.
Learning and Development: AI-powered knowledge centers allow contact centers to greatly reduce the amount of time it takes to onboard new agents. Using existing knowledge bases, manuals, FAQs, case notes or other guides, generative AI can consume all of that content and use it to generate answers to just about any question an agent might receive.
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What Are the Benefits of Using AI to Improve Contact Center Operations and Customer Experience?
Many customer experience leaders are already well aware of the benefits AI can reap in the contact center space, as evidenced by a recent Gartner poll that showed customer experience would be the primary focus of AI investments in 2023. What’s even more interesting about the Gartner poll was that customer experience outranked both cost optimization and revenue growth as a focus for AI investments.
Clearly, businesses leaders – not just CX leaders – are realizing that they’re competing on much more than price or product. They’re competing on customer experience. And AI has a big role to play in optimizing the customer experience. Here are just a few of the benefits we see when contact centers and CX strategies leverage AI appropriately:
Reduced costs: Gartner predicts that AI will reduce contact center labor costs by $80 billion by 2026. AI-powered systems can automate routine tasks, reducing the need for human intervention and minimizing labor costs. By handling repetitive inquiries, AI helps contact centers operate more efficiently and cost-effectively.
Lower average handle time (AHT): A study by MIT Technology Review found that AI-powered customer service reduces resolution times by 25% on average. Conversational chatbots, smarter IVR systems, AI-powered knowledge bases, intelligent routing and more all contribute to a more streamlined and frictionless customer experience, which in turn reduces the amount of time contact center agents spend per call.
Improved customer experience: According to a survey by Aspect Software, 73% of customers want the ability to solve product or service issues on their own. In addition, IBM reports that using AI for customer service can result in a 75% reduction in call volume. Given the fact that customers want to self-serve, coupled with the power of AI to help them do so, the case for how AI can improve customer experience is strong.
Enhanced contact center agent experience: Our internal data shows high-performing contact center agents have 12% lower attrition than low-performing agents. So how do you turn more of your agents into high performers? With AI. Knowledge bases, real-time coaching, automation, and call deflection all combine to improve working conditions for your agents.
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What Are the Risks of Using Artificial Intelligence in the Contact Center?
Much of the recent global conversation around AI has centered around fears. According to an Ipsos poll, 71% of Americans are concerned about the impact AI could have on jobs and society and 76% are concerned about deep fakes and the spread of misinformation. These fears have merit, and governments worldwide are grappling with how AI should be regulated to mitigate potential risks while realizing the benefits.
While AI in the contact center clearly provides many benefits, it’s not without risks. When assessing the risks of AI or any technology, it’s important to understand how those risks apply to your specific industry and use cases. The following are potential risks CX leaders should be aware of when implementing AI:
Misapplication: Using AI to fix one problem can often lead to another problem if you haven’t clearly defined your end goals. For example, if you use AI to reduce average handle time, but now customers have to make more calls to resolve a problem, then your customer satisfaction scores plummet – costing your company money. You “solved” one problem but created a bigger one.
Hallucinations: Often, if you say something confidently, people will believe you – even if you’re wrong and have no idea what you’re talking about. AI can do that too. A hallucination happens when an AI system gives a confident but incorrect response. Like its human equivalent, this is a problem because often people believe it. In a contact center scenario this could look like your AI bot confidently telling a customer that their purchase is fully-refundable when it isn’t.
Lack of empathy: AI isn’t sentient. It can’t sympathize with customers. Often people need human connection that they can only get from, well, humans. If you’re planning on replacing every one of your human agents with bots, you should think twice. AI can enhance and optimize many aspects of the customer experience, but it shouldn’t replace human interactions entirely.
Privacy and security: Concerns about AI collecting and sharing personally identifiable information, trade secrets, proprietary information, and other vulnerabilities are valid. Organizations must be careful when using AI around sensitive areas where security and privacy could be compromised.
Social Bias: From credit card application algorithms that discriminate against minorities to resumé screening algorithms that exclude women – the list of examples in which AI algorithms have shown bias just keeps growing. Obviously, bias is bad, but AI can make it even worse because it can perpetuate bias at an incredible scale.
Lack of transparency: AI is complex, which makes it hard for many people to understand exactly how it works. That lack of transparency can lead to lack of trust because people don’t know how the AI arrived at an answer or solution.
Implementation challenges: Even plug-and-play AI can be difficult to integrate with your existing contact center platform. If not implemented correctly, you risk breaking existing systems or investing in expensive technology that doesn’t get used.
What’s Next for AI in CX?
With all the hype around AI, it’s easy to get overwhelmed, but for TTEC Digital, Contact Center AI is nothing new. With partners like AWS, Google, Genesys and Microsoft, we’ve been bringing conversational AI and machine learning solutions to our customers for many years. Now, with those same partners, we’re harnessing generative AI for the benefit of the customer experience.
The truth is that technology alone – even generative AI – can’t deliver exceptional customer experiences. Technology must work in tandem with people who have a deep understanding of customers – their channel preferences, their needs, and their unique customer journey.
That’s where TTEC Digital comes in. Our decades of innovation on the world’s leading technology platforms, as well as our proven expertise in CX strategy, data and analytics, AI, and more, have made us a leader in creating deep customer relationships at the point of conversation. We can help you identify how prepared your company is to leverage AI with our readiness assessment, gain a foundational understanding of AI and build an action plan with our AI roadmap, and help guide you along the way.