Top 7 Contact Center AI Applications in 2023: A Holistic Approach

55% of consumers have had a customer service issue remain unsolved, and 36% are dissatisfied with the agent’s response.¹Traditional contact center systems are outdated, and customers expect higher service standards. Contact center leaders are formulating their contact center strategy based on AI and automation:

•  95% of contact center executives have previously adopted and are currently adopting automation within the next year.²
•  “Automating customer service” is the top priority for 68% of leaders regarding new investments in tech.³

Contact centers increasingly rely on artificial intelligence (AI) to improve customer satisfaction. Tech users must understand contact center AI applications to adapt to customers’ ever-changing demands and differentiate themselves from competitors. In this article, we will cover the  contact center AI:

Contact center AI (CCAI) is the use of technologies such as artificial intelligence (AI), RPA, and NLP to make contact center operations more effective. Contact center AI software can extract in-depth insights from routine customer interactions, streamline repetitive operations, and enable agents to offer customized client experiences.

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1- Interactive Voice Response (IVR) systems

Figure 1: IVR system workflow

Interactive voice response (IVR) is an automated phone technology that interacts with customers and collects data by providing them with options through a menu. It then takes action depending on the caller’s responses through the caller’s dial or voice response.

The caller’s choices determine the IVR’s actions. It can greet callers with pre-recorded messages or in complicated scenarios it can redirect the call to a human agent.

2- Automatic Speech Recognition (ASR)

Figure 2: ASR workflow

Automatic speech recognition (ASR), also known as speech-to-text (STT) or voice recognition,  is the process of converting spoken language (an audio signal) into written text form using machine learning or artificial intelligence (AI) technology.

The system relies on a language model. It works by receiving a voice memo of someone speaking. The tool then analyzes the audio using ML training models and predicts the most likely sentences utilizing what it has learned in the training model. Once the audio is analyzed, a written description is created.

3- Automatic call routing

Figure 3: Flow of automatic call routing

Source: IDTEXPRESS⁴

Call routing, or ACD (automatic call distributor), is a tool that automatically routes incoming calls to a certain agent or  a team.

Call routing feature intuitively responds, queues, and distributes calls while displaying critical notifications such as user requests and contact traits, customer’s language, and agent’s accessibility.

Thus, ACD helps to avoid needless and inconvenient call transfers as well as extended durations on hold mode. ACD may also help firms improve their first-call resolution times for support and service issues.

4- Advanced virtual agents and chatbots

Figure 4: Example of a chatbot

Source: Quiq⁵

Virtual assistants and chatbots can comprehend and respond to any customer request. They can identify human speech, comprehend the meaning behind it, and reply to them.

• Chatbots can utilize AI technologies such as machine learning and natural language processing (NLP) to improve customer communication and match them to particular intentions. 
• Advanced virtual agents are an extension of AI chatbots. They use AI technologies not only to execute conversations but also to combine conversational AI with robotic process automation (RPA) by directly performing tasks without human intervention. They can provide exact and individualized solutions to requests.

You can learn more about the differences between chatbots and virtual agents.

5- Real-time agent guidance

Figure 5: Real-time agent guidance system

Source: NICEefn_note]”Real-Time Interaction Guidance”. NICE. Retrieved October 17, 2023.[/efn_note]

Real-time agent guidance is a tool that assists contact center agents on how to address client concerns step by step. The tools enable managers and team members to provide consistent service across all conversations. 

With agent guidance, contact center employees can access real-time updates and insights for each customer by receiving notifications, alerts, or chatbot messages. Also, managers can track agent behavior and find areas for development. They can see which agents are working well and which require assistance.

6- Advanced analytics

According to McKinsey’s report on contact center companies who have already applied advanced analytics can:

• Reduce agent costs by up to $5 million annually⁶
• Increase call conversion rates by nearly 50%⁷
• Decrease handle time by 40%⁸
• Increase the use of self-service by up to 20%⁹

Contact center analytics is a critical component of company productivity. Here are some of the essential analytics features that can be built into your contact center:

6.1- Speech and text analytics

Using speech analytics tools might result in cost savings of 20-30%.¹⁰

Figure 6: Speed and text analytics usage on a contact center agent profile

Speech analytics is a contact center technology that uses voice recognition, natural language processing (NLP), and machine learning algorithms to interpret, convert (speech-to-text), and examine audio interactions between consumers and agents. 

The purpose of speech analytics is to extract useful information from human speech and use that structured data to enhance service performance. Through sentiment analysis, contact center speech analytics software may recognize phrases and keywords or automatically evaluate the behavioral tone, mood, and degree of tension or disappointment in an audio speech.

Organizations that use speech analytics in their contact center can:

• Improve quality assurance
• Minimize compliance discrepancies
• Promote upselling and cross-selling 
• Enhance agent coaching and training

To learn more, you can also check out our article on audio sentiment analysis.

6.2 Predictive analytics

Predictive analytics forecast customer behavior, requirements, and preferences to anticipate client requests and key business metrics. It may be used to simplify your contact center processes during busy periods, promote items, and identify problems before consumers call for assistance.

6.3 Self-service analytics

Self-service analytics uses data gleaned from channels, such as the company websites, chatbots, or FAQs to determine the most commonly mentioned concerns and inquiries. This enables your company to expand its knowledge base to enable customers to resolve their concerns without requiring them to contact the company. 

7- Insights automation

Figure 7: Illustration of misplaced data alerts

Customer data input can often get misplaced on its journey through your contact center operations. Insights automation can ensure that the flow of tasks, paperwork, and data across business operations processes autonomously and in compliance with specified standards.

Agents receive a notification if customer interactions are misplaced. Contact center AI may automatically manipulate the false customer data by filing, updating and tracking data for each contract, saving agents time out of the monotonous post-call paperwork.

Figure 8: Contact center workflow

Contact center AI software works by collecting and analyzing all of the organization’s customer-facing engagement channels—including phone, webchat, text message, video, and email—into a single application that a service agent can control through a multichannel platform.

Contact center AI performs a variety of functions, such as:

• Anticipating customer needs
• Deploying chatbots or AI-powered virtual assistants to answer common questions
• Distributing customer service queries to the most suitable agent
• Assisting customers with sales transaction
• Automating reporting and analytics
• Delivering clients self-service alternatives

Discover the top 10 contact center AI vendors.

Almost 80% of the customers in US agree that the most essential aspects of the customer experience are speed, simplicity, and courteous service.¹¹Contact center AI may improve customer satisfaction, save operational costs, improve productivity and operational efficiency, improve scalability, and sustain meaningful data insights.

Here are some examples of how implementing contact center AI helps organizations:

1- Enhance customer satisfaction

Nearly 65% of customers are willing to wait no more than two minutes before hanging up. ¹²Customers who get connected with a contact center are often placed in a queue before connecting with an agent. Contact center automation may significantly improve service quality by reducing wait times and accelerating responses. 

Contact center voice bot tools can interpret a user’s inquiry and deliver a suitable resolution using natural language processing (NLP) and speech analytics. When the voice bot cannot solve a question, it can transfer the contact to the next best agent so that customers will feel priority since their questions will be resolved quickly.

2- Workforce optimization

Workforce optimization (WFO) allows contact center operators to save time by streamlining non-essential tasks and enabling agents to focus on more critical tasks, such as:

• Capturing incoming calls
• Responding to consumer inquiries
• Handling daily operations

3 -Better first contact resolution

Customers want their concerns fixed as quickly as possible. If contact centers can address a customer’s problem during the first contract, customers become more satisfied with the service. First Contact Resolution (FCR) is an important indicator used to assess how successfully organizations satisfy the demands of their customers. Many contact centers utilize FCR as their major performance indicator or measure it as part of their customer experience metrics, and they are always looking for ways to enhance it.

Contact Center AI may improve your FCR since it fully handles typical issues without redirecting consumers to an operator or another channel where they can ask a common question such as (“what is my order status?”). Customers can address issues promptly through any language or channel, regardless of the time.¹³

4- Increased agent productivity

One of the reasons for agent attrition is repetitive work.¹⁴

Agent frequently handles routine and transactional tasks throughout a shift. If contact center automation handles manual work, they can focus on high-value tasks where creativity and problem-solving skills are needed such as calling clients about overdue payments or informing them of exclusive deals. 

5- 24/7 support 

Customers want to contact companies whenever and wherever they want, yet hiring employees around the clock is costly. Contact Center Automation provides 24/7 assistance without increasing work hours.

Contact Center Ii minimizes the number of issues that demand a customer service call-back by handling over 80% of Tier 1 issues (repetitive, frequent questions like “What is my order status?”)

6- Better insights

Automation in the contact center can provide more accurate insights since large volumes of client data may be captured, recorded, and sorted. AI can record data more precisely and thoroughly than humans by automatically categorizing chats and extracting each word from speech and text, allowing businesses to identify raw data efficiently.

1- Loss of human effect

~60% of customers prefer human interaction when using self-service. Humans find it difficult to connect emotionally with machines. Reducing the human aspect of your contact center operations approach restricts client interaction. Customers cannot receive empathy and get personally connected with robots, as opposed to human agents. This has the potential to weaken the bond between customers and organizations, which is essential for customer loyalty.¹⁵

2- Weak conversation flow  

60% of surveyed customers stated they frequently answer the same questions when interacting with a chatbot; moreover, 50% said they feel frustrated while using chatbots.¹⁶

Poorly built artificial intelligence (AI) chatbots and speech bots may provide inaccurate responses or trap clients in a never-ending stream of unwanted messages. If a chatbot cannot grasp user input, it should immediately provide the consumer with the option of routing their call to a human. Customers are frequently left trapped when bots are not designed to handle complicated conversations.

3- Improper configuration 

To create excellent client encounters, service agents and automation must collaborate. It is difficult to integrate AI technology into conventional customer service. 

A few of the challenges are:

• Onboarding and training the agents
• Complying with new regulatory rules
• Ensuring current technologies integrate with newly acquired software.

69% of the firms have implemented AI in their contact center, however, only 14% believe they are transformational in their use of AI.¹⁷

There is still a significant runway to push toward transformational use of AI in the contact centers. Majority of businesses expect to formulate heavy AI usage in the next few years. They intend to do so with the idea that AI will not be a substitute for humans, but rather empower agents to better fulfill the customer experience. 

Some of the future predictions about contact center AI are as follows:

• Businesses will increase their investments in AI capabilities: 64% of customer experience contact center executives see investments in AI capability as a priority.¹⁸

• Self-service options in contact centers will improve customer satisfaction and operational efficiency. 84% of the businesses think customers expect self-service options 24/7.¹⁹

• AI-powered contact centers will still rely on human decisions: ~80% of executives think AI will function as an “assistant” to human agents, rather than eliminating them.²⁰

• AI will enhance security by minimizing data fraud: ~70% of professionals believe thatAI can assist in the resolution of fraud/data issues.²¹

To fully realize the value of AI and overcome these imagined limitations, institutions must develop a multidisciplinary AI strategy—one that evaluates the tasks at hand against up-to-date technological gaps while concentrating on long-term viability and ROI.

Check out our other articles on contact centers and automation to learn about other popular options like Salesforce Service Cloud, Talkdesk, Five9, UJET, and many others.

If you are ready to use contact center AI in your organization, here is the complete data-driven software list of our top 10 contact center vendors.

Find the Right Vendors

External links

1. ”Qualtrics Announces Consumer Trends That Will Shape 2023”. Qualtrics. November 01, 2022. Retrieved October 16, 2023.
2. ”Benchmark Report: Contact Center Automation is Moving to the Mainstream”. Replicant. Retrieved October 10, 2023.
3. ”Benchmark Report: The Impact of Economic Uncertainty on Contact Centers”. Replicant. Retrieved October 10, 2023.
4. ”What is Automatic Call Distribution (ACD) & 7 Signs That You Need it”. IDTEXPRESS. May 26, 2023. Retrieved October 16, 2023.
5. ”Customer-Facing AI Assistants”. Quiq. October 2, 2023. Retrieved October 16, 2023.
6. ”From speech to insights: The value of the human voice”. McKinsey. Retrieved October 16, 2023.
7. ”From speech to insights: The value of the human voice”. McKinsey. Retrieved October 16, 2023.
8. ”From speech to insights: The value of the human voice”. McKinsey. Retrieved October 16, 2023.
9. ”From speech to insights: The value of the human voice”. McKinsey. Retrieved October 16, 2023.
10. ”From speech to insights: The value of the human voice”. McKinsey. Retrieved October 16, 2023.
11. ”Experience is everything. Get it right.”. PWC. Retrieved October 16, 2023.
12. ”Arise Customer Service Frustration Series: Phone Hold Times”. Arise. Retrieved October 17, 2023.
13. ”Digital customer care in the age of AI”. IBM. Retrieved October 16, 2023.
14. ”US Contact Center Decision-Makers’ Guide 2021”. ContactBabel. May 20, 2021. Retrieved October 17, 2023.
15. ”Deconstructing Delight”. (PDF) Freshdesk. Retrieved October 16, 2023.
16. ”Customers Aren’t Mad, They’re Just Disappointed”. (PDF). Forrester. January, 2023. Retrieved October 16, 2023.
17. ”The future of AI in the contact center”. Talkdesk. March 2021. Retrieved October 17, 2023.
18. ”The future of AI in the contact center”. Talkdesk. March 2021. Retrieved October 17, 2023.
19. ”The future of AI in the contact center”. Talkdesk. March 2021. Retrieved October 17, 2023.
20. ”The future of AI in the contact center”. Talkdesk. March 2021. Retrieved October 17, 2023.
21. ”The future of AI in the contact center”. Talkdesk. March 2021. Retrieved October 17, 2023.