- AI evolution. AI in contact centers enhances customer service by automating processes and personalizing experiences.
- AI platforms. Leading AI-powered platforms provide flexible and dynamic customer service solutions.
- AI challenges. Balancing AI capabilities and the human touch is crucial for effective customer service.
In a customer-centric world, brands are constantly seeking innovative ways to improve customer service. The integration of AI technologies into contact center platforms has improved customer experiences while boosting efficiency and performance. This article explores the evolution of AI in contact centers, its transformative benefits, an overview of the leading AI-powered platforms and the challenges that brands may encounter when using AI for customer service.
The History of AI in the Contact Center
AI was introduced to the contact center industry around the late 1990s and early 2000s. This was when Interactive Voice Response (IVR) systems began to incorporate more sophisticated AI technology. These systems used speech recognition software to handle incoming calls and were capable of understanding and responding to voice prompts from callers.
AI's role in contact centers today has dramatically transformed how customer service agents interact with customers. It now powers not only IVR systems but also chatbots, speech and predictive analytics, and customer behavior analysis. For example, speech analytics can discern elements of speech such as vocabulary, sentiment, pauses, tone and the age of the caller. These insights help route callers to the best-suited agent, considering factors like the agent's success rate, expertise, the caller's personality and more.
Ralph Bonaduce, president of Akorbi BPO, a woman-owned business process outsourcing provider, told CMSWire that AI has the potential to transform the way that businesses interact with customers including providing higher-level service. "AI helps us connect with the customer using the right language, on the right topic, and then to the first available agent with the correct skill set for the fastest service. The key to implementing AI effectively is to introduce it in less complex situations and then continue to evolve the applications," said Bonaduce.
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Generative and Conversational AI Vastly Improved Chatbots
Generative and conversational AI have significantly enhanced chatbots, making them more dynamic and user-friendly. These AI models have a deep understanding of natural language, which enables them to understand complex user queries and provide more accurate, human-like responses.
Unlike rule-based chatbots, generative and conversational AI are able to generate their own responses, which leads to more flexible and dynamic conversations. They also have a strong grasp of context and remember past interactions so they are able to use that information to provide relevant responses. These AI models also excel in handling queries that are uncertain. If a user's request isn't clear, the AI can ask follow-up questions to clarify, much like a human would. This ability greatly enhances the user's experience and ensures more effective communication.
Generative and conversational AI models are able to understand and respond in multiple languages, a feature that makes them invaluable for businesses serving customers globally. They are also able to handle a large volume of queries simultaneously, providing instant responses and reducing wait times.
Mike Clifton, chief growth and transformation officer at Alorica, a customer service BPO provider, told CMSWire that chatbots and virtual assistants have become staples of modern CX, creating a win-win situation for customers and contact centers with benefits such as shorter wait times, quicker and easier access as well efficient responses. "While these gains have disrupted and transformed the industry, we’re really experiencing a greater level of that with generative AI. Generative AI represents a giant step forward in digital transformation and offers tremendous opportunities for companies and consumers everywhere,” said Clifton, who explained that generative AI has enabled contact centers to automate tasks and analyze data with unparalleled accuracy and speed.
Over time, generative and conversational AI learn from past interactions. This continual learning leads to progressively better performance and improved customer experience. The introduction of generative and conversational AI has led to significant improvements in customer interactions and satisfaction, and AI-driven chatbots are now a regular feature on enterprise websites. According to a January 2023 Statista report, the size of the chatbot market is predicted to reach around $1.25 billion dollars in 2025, a large increase from the 2016 market size of $190.8 million.
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Speech and Predictive Analytics in the Contact Center
Speech analytics is often used in contact centers to convert call recordings into structured data for analysis. This technology is used to extract valuable information from customer interactions. One of its key applications is sentiment analysis, which involves analyzing the tone, pitch and speed of a customer's voice to determine their mood and satisfaction level. This helps customer service agents to understand how to respond, and it also helps managers recognize if the call might need further attention.
Liana Tonoyan, chief information officer at Phonexa, a lead distribution software provider, told CMSWire that real-time sentiment analytics are a great example to showcase how AI helps not only improve the customer’s experience, but also analyze the agent performance and focus on specific areas for improvement which, in turn, directly impacts the agent key performance indicators (KPIs). “Speech analytics also enables the transcription and analysis of call recordings in real-time,” said Tonoyan. “It can identify keywords, assess sentiment, and provide valuable insights into customer preferences, pain points, and satisfaction levels.”
Predictive analytics, on the other hand, employs historical data, statistical algorithms, and machine learning techniques to predict future outcomes. In the contact center, predictive analytics is often used for predicting call volumes. By forecasting call volumes based on historical data and trends, predictive analytics enhances workforce management, ensuring that enough agents are scheduled to handle expected call volumes.
Predictive analytics is also used for customer behavior prediction. Analyzing past interactions and behaviors enables it to predict a customer's future actions. For instance, it can identify customers who are likely to churn, enabling the business to proactively address their concerns. Additionally, predictive analytics contributes to personalized customer service by enabling agents to understand a customer's preferences and needs, which allows them to provide a greater level of personalized service.
AI-Enhanced Contact Center Platforms
AI and machine learning have revolutionized the contact center over the past few years. With the introduction of conversational and generative AI models, contact center platform providers have enabled their software platforms to take customer service to a level that was previously unheard of. There are a multitude of contact center platforms to choose from, including the following best-of-class offerings:
- Ada – Ada is a multi-language AI-powered contact center platform that provides businesses with automated customer service functionality across all of a brand’s channels.
- Google Contact Center AI (CCAI) – CCAI is an omnichannel contact center platform with AI-powered operations, self-service options, and embedded experiences, and features a virtual agent and Agent Assist, which empowers human agents with continual support.
- Microsoft Digital Contact Center – This omnichannel cloud-based platform provides personalized and secure interactions, AI-powered chat agent and collaboration tools, and predictive insights to adapt to future needs.
- Five9 – Five9 is an omnichannel, multi-language Call & Contact Center As A Service (CCaaS) provider that enables brands to engage customers on their preferred channels, providing an intuitive, personalized, and human-like experience.
- Gladly – Gladly is an omnichannel platform that provides agents with a complete history of interactions and offers personalized self-service options, with a focus on building long-term customer loyalty.
- 8x8 – 8x8’s contact center platform features functionality that includes conversational AI, personalized workspaces for supervisors, and accurate transcription and translation services.
- Uniphore – Uniphore is a conversational AI and automation platform that features omnichannel self-serve bots, AI-driven analytics, agent assistance through Robotic Process Automation (RPA), and real-time facial expression and tonal emotion analysis.
- Talkdesk – Talkdesk is an AI-powered cloud-based contact center platform designed to help businesses deliver world-class customer service. It features omnichannel engagement, workforce engagement, employee collaboration and customer experience analytics.
- Genesys Cloud CX – Genesys Cloud CX is a cloud-based contact center platform that unifies customer and agent experiences across phone, email, chat, text and social channels.
- Nice CXone – Nice CXone is an AI-powered cloud-based contact center platform that improves workforce performance and delivers comprehensive customer analytics along with intelligent omnichannel routing.
- Salesforce Service Cloud – Salesforce Service Cloud is built on the company's CRM software, and includes ticketing, tracking and routing customer service issues. It features AI-powered capabilities for automated service processes, predictive support and intelligent recommendations.
- Amazon Connect – Amazon Connect is an omnichannel cloud-based contact center platform that improves agent productivity and customer experience using AI and machine learning.
- Sprinklr Service – Sprinklr Service is a customer care platform that enables brands to manage and enhance their customer experience across multiple channels. It includes AI-powered automation functionality to improve customer service processes and efficiency.
A great example of the use of AI for customer service can be seen at HomeServe, a leading provider of home repair service plans. The business was recently spotlighted in a Wall Street Journal feature about how AI is transforming the way call centers operate. In the article, Jessica Cloud, VP of automation and innovation at HomeServe, discussed how HomeServe’s artificial intelligence-powered virtual agent, Charlie, is able to handle over 11,000 calls each day, providing answers to customers, initiating the claims process, and routing calls that need a higher level of assistance to agents.
The Challenges of AI Contact Center Platforms
One of the challenges that come with the use of AI-powered contact center platforms is that there is a tendency to think that because AI is able to automate processes and handle service tickets, live agents will rarely have to be involved with customer service requests. The reality is that while AI functionality will inevitably enhance the role of customer service agents, it will never replace them entirely because customers will always want a human element in their experiences with a brand.
"Ultimately, AI will help us provide better service and better customer experience in a more cost-efficient way," explained Bonaduce, who reiterated that AI will never fully eliminate customer service agents — not in our lifetime. "But it will help make them more responsive. The challenge with any new technology is to balance the need with availability and novelty. Additionally, AI implementation will require an open, transparent conversation and collaboration between providers and clients about the use of technology."
In fact, a 2018 PWC report revealed that 59% of consumers feel that brands have lost touch with the human element of customer experience, and a 2022 Gladly Customer Expectations report indicated that 73% of consumers want to be treated like a person, not a case number. The pressure is on brands to provide customers with an exceptional customer service experience, as an even more recent 2023 Gladly Hero Experience report showed that 42% of customers said they would stop buying from a brand altogether after just two bad service experiences.
“Collaboration between AI systems and live agents is crucial in delivering individualized customer service,” said Tonoyan. “Striking the right balance involves using AI to automate routine tasks, with the ability to seamlessly transition the more complex calls from virtual agents to live agents.”
Final Thoughts on AI in Call Centers
AI's integration into contact center platforms has vastly improved customer service by enhancing customer experiences and boosting operational efficiency. Despite AI's capabilities, the human touch remains vital in customer service, and as such, the future of customer service lies in blending AI technology with human empathy, ensuring an efficient and deeply personal customer experience.