The Gist

  • Immense potential. Generative AI holds enormous potential for revolutionizing contact centers by automating mundane tasks and improving efficiency.
  • Industry applications. From retail and healthcare to financial services and travel, generative AI is finding its place in various industries.
  • Addressing generative AI challenges. To harness the full power of generative AI in contact centers, brands must address potential limitations, data privacy concerns, biases and ensure seamless integration with existing systems.

Generative artificial intelligence (AI) has the potential to revolutionize the contact center industry, offering customer service and technical support enhancements that improve efficiency and effectiveness, while reducing the need for live agents to handle mundane tasks. Large language models, such as GPT-3 and GPT-4, have demonstrated their capabilities in understanding and generating humanlike text, making generative AI an attractive option for online businesses of all sizes.

This article will delve into use cases, challenges and solutions for implementing generative AI in contact centers.

What Is Generative AI?

Unless you’ve been living under a rock for the past few months, you’ve likely heard about generative AI, if not tried it yourself. Generative AI is an innovative development in the field of artificial intelligence that has the ability to create content from simple text-based or verbal prompts. Generative AI models were created through the use of Generative Adversarial Networks (GANs), a type of machine learning algorithm that analyzes large sets of data to generate new text, images, music and even video that mimics the style and content of the original data. Some of the most popular generative AI models include Google Bard, Microsoft Bing and ChatGPT.

The potential applications of generative AI are numerous, from enabling content producers and marketers to effortlessly create new content, to assisting scientists and researchers in generating simulations and models. By using generative AI, brands can automate tasks that previously required human intervention, ultimately saving time and increasing efficiency.

As generative AI continues to evolve, it promises to play an increasingly important role in shaping the future of various industries and society in general. The technology provides new possibilities for content creation and automation and is paving the way for innovative solutions and enhanced creativity in a variety of fields. 

Aside from the search engines and chat applications mentioned above, many other industries and practices are beginning to leverage the functionality of generative AI: 

Related Article: Contact Center Technology and Strategies to Keep Customers Cool

Examples of Generative AI Platforms for Contact Centers

Ilya Smirnov, head of data science at Usetech, a tailored software development company, told CMSWire that we rarely think about the fact that people working in contact centers are faced with a large amount of information, for the processing of which huge resources are used. Smirnov suggested that modern machine learning technologies can help to significantly reduce the cost of providing services, as well as improve the efficiency of the contact centers.

The most primitive robotic systems, said Smirnov, are linear chatbots — the type most of us have been used to seeing for many years now as we navigate the web. “We meet them in messengers, social networks, mobile applications and websites,” Smirnov said. He added that these chatbots are not trained and work according to a certain scenario — but they are useful. “With their help, you can order a pizza or a table in a restaurant, specify the cost of sending a parcel or get a ticket to the doctor. At the same time, the average request processing time will be reduced by about 3 times,” said Smirnov. 

Smirnov worked with a company that was engaged in insurance and wanted to create its own first-line chatbot using AI to improve the quality of service and unload an overburdened call center. By the final stage of the project, the AI-driven chatbot was able to completely close 30% of requests in automatic mode without operator participation, while another 35% of requests required only operator confirmation. "As a result, the average time to solve an application in which the operator's participation is required was reduced from several hours to 10-15 minutes." It’s clear today that AI can reduce the need for agents to directly communicate with customers, allowing them to be available for the most complex or detailed tasks.

Conversational AI to Generative AI?

Many contact centers are now implementing AI-driven solutions as part of their technology stack. Some are using conversational AI chatbots to enable customers to instantly find answers to their questions, while others are moving from conversational AI to generative AI, which enables the chatbot to carry on full conversations with customers while providing them with answers to their questions. Other contact centers are using generative AI to provide transcripts of the conversations that contact center agents have with customers. 

Damien Thioulouse, head of AI at Simplr, a US-based customer service outsourcing platform and service provider, told CMSWire that generative AI has pushed the boundaries of what was possible in terms of automation. "Bots will be able to successfully engage with customers well beyond the single intent inquiries and also within more intricate applications such as technical troubleshooting," said Thioulouse. “Large Language Models are excellent at summarizing, organizing, and prioritizing topics. This is having a profound impact on using the customer's voice as input.”

robot ai typing

In August 2022, Gartner predicted that by 2026, conversational AI chatbots in contact centers will reduce customer service labor costs by $80 billion. Although generative AI for contact centers is still relatively new, there are already many contact center platform providers that have integrated generative AI functionality into their software, including:

Naturally, contact center platforms that integrate generative AI often include AI-powered chatbots that can handle routine inquiries and simple customer service requests. These chatbots are trained to understand and respond to natural language, empathize with customers and learn from previous interactions to improve their responses over time.

AI-driven voice recognition technology can be used to automate call routing and help customers quickly connect with the right agent or department. When used along with technology such as ElevenLabs’ voice synthesis APIs, generative AI is also able to have voice conversations with customers. 

Contact center platforms such as Cogito, ThankfulAI and Yobi use generative AI for sentiment analysis, allowing contact center agents to gauge the emotional state of customers based on their tone of voice or the words they use. This enables agents to better understand customer needs and improve the overall customer experience.

Generative AI can also help brands personalize their interactions with customers. By analyzing vast amounts of customer data, including service tickets, preferences, history and previous interactions, it can gain a deep understanding of customer needs and desires, allowing it to respond to inquiries in a more personalized and effective manner.

Another key feature of contact center platforms that integrate generative AI is the ability to automate workflows. By analyzing customer inquiries and previous interactions, generative AI is able to determine the nature of the inquiry and direct complex queries to the right departments or service agents, making the customer service process more efficient and timely. Such automation allows contact centers to manage increased request volumes without the need for additional staff, making it ideal for rapidly growing businesses. 

Related Article: Kinder AI in the Contact Center: Best Ways to Improve Your AI Customer Support

Learning Opportunities

Industry-Specific Use Cases for Generative AI

Azam Mirza, president and co-founder of Akorbi, a multilingual digital transformation group, told CMSWire that generative AI has been around for a few years now in the contact center industry. “Initial use cases were around emotion deduction based on tone or voice, speed of conversation, and choice of words used,” said Mirza. “Then we started seeing trends in bots which are learning while being used.” Mirza said that generative AI, which uses natural language processing (NLP), not only learns while an agent is on the phone, but can also function as a chatbot that has learned from all the data it received while listening to the calls.

Generative AI is being used in contact centers across various industries, with each having its own challenges and customer expectations. In the world of retail and ecommerce, AI-powered chatbots are helping to guide customers through the purchasing process, answering questions about products, handling returns, and providing personalized product recommendations based on browsing history and customer preferences.

In the healthcare industry, AI is being used to assist patients in scheduling appointments, answering general health inquiries and directing them to the appropriate medical professionals. AI is also facilitating communication between patients and healthcare providers, improving the overall patient experience.

In financial services, AI is helping customers understand complex financial products, providing information on account balances, transaction history and investment options. Additionally, it can help detect and prevent fraudulent activities by monitoring customer interactions and account activities.

In the travel and hospitality industry, generative AI can assist customers with booking flights, accommodations and tours, as well as provide real-time updates on travel advisories, delays and cancellations. By analyzing customer data, it can also offer personalized travel recommendations based on customer preferences and travel history.

Finally, in the telecommunications industry, AI is being used to troubleshoot technical issues, assist with plan upgrades or changes, and answer questions about billing, coverage and device compatibility.

Generative AI Challenges: Inaccuracy, Customer Data Privacy

While there are numerous benefits to adopting generative AI solutions in contact centers, there are also challenges and limitations to consider. Large language models may struggle with complex inquiries, leading to inaccurate responses. Additionally, there are concerns surrounding data privacy, security and potential biases in AI training data.

Joe Bradley, chief scientist at LivePerson, a conversational AI platform provider, told CMSWire that generative AI and LLMs such as ChatGPT are not ready for enterprise use right out of the box, and that there are several issues that must be addressed when using generative AI. “LLMs can include bias, whether through toxicity, hurtful language or polarizing responses leading to unintended consequences.” This has already been seen in the past with Amazon’s employment pre-screening AI, and more recently with the new AI-driven Bing

Additionally, generative AI models must be fine-tuned using data that is relevant to the task, brand or industry. “Using LLMs to generate content that is relevant to your business with little or no customization or fine-tuning will result in responses that are too generic or even irrelevant to the brand’s products or the consumer needs,” said Bradley.

If generative AI is going to be used with workflows that are not included in the contact center solution a brand is using, it will have to be integrated with a brand’s current software and procedures. “If a company wanted to use an LLM to generate automated responses to customer complaints, the LLM would not inherently understand how to integrate with the company's complaint management system and follow the appropriate workflow,” said Bradley. 

Similarly, while generative AI is great at having conversations, it does not innately have the ability to follow up with specific actions. “Large language models, such as GPT-3 or BERT, are designed to analyze and generate humanlike language. However, they are not typically connected to inventory systems and do not have the capability to perform orders, order tracking or checking if something is available for customer service,” Bradley explained.

Integrating AI solutions into existing contact center infrastructure can be complex, and it may be more effective to use a contact center platform that already includes generative AI functionality. Brands that wish to stick with their current platforms should evaluate their current systems and processes to determine where AI can be useful and more easily integrated. This may involve updating software platforms, implementing APIs or working with third-party providers to ensure a smooth integration.

Final Thoughts on Generative AI Contact Center Solutions

Generative AI contact center solutions present numerous opportunities for enhancing customer service and technical support. By carefully considering the potential drawbacks and limitations, addressing implementation challenges, and encouraging collaboration between human agents and AI systems, brands can approach the full potential of AI-powered customer service.