The ultimate guide to contact center AI

Contact center artificial intelligence (contact center AI) plays a pivotal role in enhancing the customer service experience through tools like chatbots, automated phone systems, intelligent email responses and more. This powerful technology has transformed how businesses interact with their customers — and organizations are adopting it at a record pace. According to research from Metrigy, over 70% of contact center leaders have indicated plans to ramp up their AI spending in 2024 and 2025.

What exactly is contact center artificial intelligence, and what should customer experience (CX) leaders consider when implementing it within their organization?

Read on to learn more.

What is contact center AI?

Contact center artificial intelligence refers to the application of AI technologies specifically designed to enhance and streamline customer service operations within contact centers. At its core, contact center AI aims to create a more intuitive, responsive and personalized customer service experience.

How contact center AI works

Contact center AI leverages subsets of artificial intelligence to understand, interact with and assist customers effectively. This includes natural language processing (NLP), which enables AI systems to understand, interpret and respond to human language in a meaningful and contextually relevant way. It involves several sub-tasks like tokenization (breaking down text into words or phrases), parsing (analyzing sentence structure) and semantic analysis (understanding the meaning of the text). In contact centers, NLP enables chatbots to comprehend customer inquiries, extract relevant information and generate appropriate responses.

Machine learning (ML) — a branch of AI that focuses on training algorithms to learn from data and improve their performance over time without being explicitly programmed — is also a critical component of contact center AI. ML algorithms can analyze vast amounts of historical data to identify patterns and trends. In customer service, machine learning can be used to predict customer needs, suggest next-best actions for agents and identify common issues. For example, if a significant number of customers are reporting a particular problem, ML algorithms can detect this trend and alert company leaders to take action.

Contact center AI use cases

There are many different applications of AI for the contact center. With the recent popularity of generative AI (GenAI) within customer experience operations, contact center AI capabilities are expanding exponentially.

Some examples of contact center AI in action include, but are not limited to:

• Conversational bots: Also known as chatbots, these software applications simulate human conversation through text or voice-based interactions. They use natural language processing and machine learning to understand and respond to user queries or commands. To manage voice-based interactions, chatbots utilize automatic speech recognition (ASR) technology, which converts spoken language into text. In customer service, chatbots are effective for handling simple inquiries and tasks around-the-clock, reducing the need for direct interaction with human agents.
• Agent assist bots: This technology provides real-time support to human agents by offering instant suggestions related to customer queries, retrieving customer data, automating routine tasks and more.
• Robotic Process Automation (RPA): RPA involves using software robots, or "bots," to automate repetitive, rule-based tasks that human agents typically perform. Robotic process automation can handle various tasks, including data entry, transaction processing, report generation and customer record management. For example, if a customer updates their contact information in a self-service application, RPA can automatically capture this new information and update all relevant systems and databases in real-time.
• Voice biometrics: Voice biometrics analyze voice patterns to authenticate users, considering factors like pitch, tone and speech rhythm. They can enhance security in the contact center by verifying a customer's identity through their voice, reducing the need for passwords or security questions.
• Analytics and reporting: AI-driven analytics tools collect and analyze large volumes of data from customer interactions. One key application is sentiment analysis, which uses natural language processing and machine learning algorithms to identify and categorize emotions expressed in text or speech. By detecting positive, negative or neutral sentiments, sentiment analysis helps gauge customer emotions during interactions. This information can be used to prioritize responses, escalate issues to human agents when needed and tailor responses to enhance customer satisfaction. Insights like these enable businesses to understand customer behavior, identify trends, measure the effectiveness of support strategies and make informed, data-driven decisions.

Benefits of contact center AI solutions

Each tool within the contact center AI suite has its unique advantages, contributing to a more streamlined and effective contact center operation.

Some of the most common benefits include:

Improved operational efficiency

According to the aforementioned research by Metrigy, the primary driver behind increasing AI contact center spend is the promise of efficiencies. By automating repetitive tasks such as answering FAQs, processing orders and gathering customer information, contact center AI applications like RPA and chatbots free up human agents to focus on more complex interactions.

For example, a large global technology company faced challenges with long average handle times and low customer satisfaction due to the manual data entry tasks required of its contact center agents. To address these issues and enhance both employee and customer experiences, the company engaged TELUS Digital to implement a custom RPA Solution. The solution incorporated feedback from frontline team members at the organization and involved connecting disparate systems with an application programming interface (API) to eliminate manual steps in the company's processes. Implementing RPA resulted in a 22% decrease in average handle time, a 35.3% increase in call capacity and an overall improvement in customer experience.

Contact center AI's enablement of self-service options, such as chatbots and interactive voice response (IVR) systems, also plays a primary role in enhancing efficiencies, as they empower customers to handle simple queries without engaging with a live agent, according to Max Ball, principal analyst at Forrester. "AI gives you a lot of power; it allows you to do more in self-service, which is really valuable," he said in the webinar Driving CX success with AI and automation in CCaaS, hosted by TELUS Digital. Ball further explained that the ROI of self-service is significant, making the implementation of AI in the contact center a "no-brainer." He emphasized that brands should capitalize on AI capabilities as much as possible to maximize their benefits.

And with contact center AI's ability to predict call volumes and optimize staffing levels, its efficiency benefits also extend to workforce management. AI can accurately forecast demand and adjust staffing levels by analyzing historical data and real-time interaction patterns. It can help ensure that the right number of agents are available during peak times, thereby reducing customer wait times and preventing overstaffing during slower periods.

Driving CX success with AI and automation in CCaaS

Join featured speakers from TELUS Digital (formerly TELUS International) and guest, Forrester, as they explore the powerful synergy between artificial intelligence, automation and Contact Center as a Service (CCaaS) solutions for transforming customer experience (CX) strategies.

Watch the video

Enhanced employee experience

When employees are equipped with the right tools, training and support, they can perform their jobs more efficiently and with greater satisfaction. This translates to more engaged and motivated agents who can better assist customers, resolving issues quickly and delivering exceptional service. Additionally, increased job satisfaction leads to lower employee attrition, reducing the need for costly retraining.

Contact center AI tools help reduce the cognitive load on human agents by providing real-time information, suggested responses and automated call summaries. Research by HubSpot shows that "78% of customer service professionals believe AI and automation allow them to focus more on the most important aspects of their roles."

For example, a growing consumer robotics company looking to maximize the performance of its customer support agents engaged TELUS Digital's automation experts to implement an agent-assist chatbot. By training the bot on information housed in the company's existing knowledge base, agents could look to the bot for help in navigating company and product information. The bot is capable of effortlessly surfacing the correct information, as well as tips, scripts and reminders. Implementing the agent-assist bot improved agent satisfaction by 37%, lengthened tenure for in-house agents and reduced the learning curve for new hires, decreasing the ramp training time for the client's growing CX operation.

Data-driven insights

With countless customer interactions occurring across multiple channels daily, there is a vast amount of data rich with essential insights waiting to be mined. AI-powered contact center analytics tools can analyze the data to identify trends, uncover insights and generate actionable recommendations, such as determining where to use chatbots, when to hand-off to the right agent and much more.

In the aforementioned webinar hosted by TELUS Digital, Ball noted that 100% of interactions in the contact center are recorded, but only a very small handful (1%) of those interactions are actually reviewed for quality assurance or other insights. "It was data that was just dead. It was too hard to get to, you had to listen to recordings and it didn't make sense to leverage it. There was no way to get value there."

But with AI making speech-to-text effective, 100% of contact center calls can now be transcribed, and all of those transcriptions, added Ball. This gives companies greater visibility into their contact center operations. "That data is now accessible; that data is now sharable. This is the real voice of the customer. The insights that are available here are huge."

Contact center AI adoption challenges

While the benefits of contact center AI applications are significant, organizations can face challenges in their adoption and implementation. Understanding these obstacles is crucial for overcoming them effectively and ensuring seamless implementation.

Some key challenges include:

• High initial costs: Implementing contact center artificial intelligence involves significant initial investments in technology, infrastructure and training.
• Integration with existing systems: Ensuring that AI tools work seamlessly with existing customer relationship management (CRM) systems, databases and other contact center software is critical to avoid disruptions in service and data flow. However, some contact centers operate on legacy systems that may not be compatible with modern AI technologies. Integrating contact center AI into these outdated systems can be complex and costly.
• Data quality and management: AI's effectiveness depends on the quality of data it learns from. Inaccurate or incomplete data can lead to poor performance and misguided insights. Moreover, some organizations may store their data in disparate systems, creating silos that hinder AI from accessing a comprehensive view of customer information.
• Data privacy: AI systems require access to large volumes of data — sometimes including sensitive customer information — to function effectively. Ensuring this data is collected, processed and stored in compliance with stringent data protection regulations, such as GDPR, can be complex. Additionally, brands must safeguard against data breaches and unauthorized access, as any lapse in security could erode customer trust and result in significant legal and financial repercussions.
• Change management: Introducing AI to your operations can lead to apprehensions among employees about job displacement and changes in their roles. Resistance to change can impede successful contact center AI (CCAI) implementation.
• Optimizing the user experience: According to Statista, almost half (48%) of consumers said that if it made their CX with a brand more seamless, consistent and convenient, they would interact with AI more frequently. With that said, ensuring that AI interactions are smooth, intuitive and effective is crucial. Poorly designed AI systems can frustrate customers and negatively impact their experience.
• Balancing AI and a human touch: According to a report by Execs In The Know, sponsored by TELUS Digital, 40% of consumers listed "difficulty reaching a live person" as the most frustrating aspect of resolving a customer care issue with a brand. While AI can efficiently handle routine tasks, it lacks the empathy and nuanced understanding that human agents provide. Ensuring customers feel valued and understood requires a seamless integration where AI manages simple inquiries, while complex or sensitive issues are escalated to human agents.
• Monitoring and maintenance: AI systems require continuous monitoring, fine-tuning and maintenance to perform optimally, which can be resource-intensive.

Contact center AI best practices

Implementing contact center AI can elevate customer service operations, however, to reap the many benefits of the technology, it's important to follow best practices that ensure a smooth and effective deployment.

Here are some key steps to consider:

• Define clear objectives: Before diving into implementation, outline your specific goals. Are you aiming to reduce call handling times, improve first-call resolution rates or enhance customer satisfaction? Clearly defined objectives will guide your AI strategy and help measure its success.
• Start with pilot projects: Small-scale pilot projects are a great way to test the effectiveness of AI solutions in your contact center environment. Pilots allow you to gather valuable data, identify potential issues and make adjustments before a full-scale rollout. Choose a manageable segment of your customer interactions to start, such as handling common queries or support requests.
• Ensure data quality and integration: As previously mentioned, high-quality data is the backbone of any AI system, making effective knowledge management essential. Knowledge management tools, practices and personnel play a crucial role in maintaining data integrity by ensuring that information is accurate, relevant and up-to-date. These systems help in organizing and curating the data that AI systems use, setting them up to deliver more accurate insights and recommendations. Integrating AI with existing CRM systems, knowledge bases and other relevant platforms enables seamless access to comprehensive customer information. This integration ensures that AI can interact with the most current and complete data, leading to more accurate and personalized customer interactions.
• Keep humans in the loop: It's critical to ensure that the path from AI tools to speaking with a human agent is easily accessible, allowing customers to reach a real person without frustration or delays. Striking the right balance between AI and human agents involves carefully designing workflows so AI enhances, rather than diminishes, the customer experience, maintaining a personal and empathetic touch in interactions.
• Focus on user-friendly interfaces: For AI to be effective, it must be user-friendly for both customers and agents. Design interfaces that are intuitive and easy to navigate. Train your agents to work alongside AI tools, leveraging their capabilities to provide better service rather than seeing them as replacements.
• Prioritize continuous learning and improvement: AI systems learn and improve over time through machine learning and natural language processing. Continuously monitor AI performance, gather customer and agent feedback, and make iterative improvements. Regular updates and fine-tuning are essential to maintain high performance and adaptability to evolving customer needs.
• Maintain transparency and trust: According to a TELUS Digital survey, over 70% of Americans said they expect companies to be transparent in how they use generative AI. Customers should be informed when they are interacting with an AI system. Transparency builds trust and sets clear expectations. "[Companies] should also disclose how they collect, use and protect customer data, as well as the benefits and risks of data sharing," wrote Michael Ringman, chief information officer at TELUS Digital in a Forbes article. "Clear and easy-to-understand privacy policies that explain customers' rights and choices regarding their data should also be communicated."
• Invest in security and compliance: Implement robust security measures to protect data and comply with relevant regulations. Regular security audits and updates are necessary to safeguard against breaches and maintain customer trust.
• Monitor metrics and key performance indicators (KPIs): Track metrics and KPIs such as customer satisfaction scores, average handling time and resolution rates to evaluate the effectiveness of your AI implementation. Data-driven insights will help you understand AI's impact on your contact center operations and identify areas for further enhancement.
• Foster a culture of innovation: Engage your team in the AI implementation process, solicit their input and provide ongoing training and support. An empowered and informed team is more likely to leverage AI effectively, driving better outcomes for your contact center.

By following these best practices, you can ensure a successful CCAI implementation that enhances customer experience, boosts efficiency and drives long-term value for your organization.

Partnering for contact center AI success

The complexities involved in integrating AI into customer service operations demand expertise, strategic vision and a deep understanding of both technology and customer experience dynamics. Choosing an AI and automation partner to assist in designing, building and delivering your chosen CCAI tools is paramount to the success of your strategy. A skilled partner brings a wealth of knowledge and resources that can make the difference between a smooth, impactful deployment and a costly, ineffective endeavor.

Reach out to our team of experts to see how our deep domain expertise and cutting-edge AI-driven tools can help your brand streamline its CX operations and exceed your customer's expectations.