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issue remain unsolved, and 36% are dissatisfied with the agent’s response.1Traditional contact center systems are outdated, and customers expect higher service standards.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:
What is 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 thanks to real-time data analytics and automated workflows. Contact center AI software can extract in-depth insights from routine customer interactions, streamline repetitive operations, and enable agents to offer customized client experiences.
Top 7 contact center AI applications
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 more 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
Call routing, or ACD (automatic call distributor), is a tool that automatically routes incoming calls to a certain agent or a team.
The call routing feature intuitively responds, queues, and distributes calls while displaying critical notifications such as user requests and contact traits, customer language, and agent accessibility.
Thus, ACD helps to avoid needless and inconvenient call transfers as well as extended durations in 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
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 it.
- 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 centers, companies that 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 centers 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 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 is autonomous and in compliance with specified standards.
Agents receive a notification if customer interactions are misplaced. Contact center AI may automatically manipulate false customer data by filing, updating, and tracking data for each contract, saving agents time out of the monotonous post-call paperwork.
How does AI work in contact center operations?
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.
What are the benefits of contact center AI?
Almost 80% of the customers in the 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 “Where is my order?” 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
Agents frequently handle 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 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.
What are the challenges of contact center AI?
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.
What is the future of contact center AI?
69% of the firms have implemented AI in their contact centers; however, only 14% believe they are transformational in their use of AI.17
There is still a significant runway to push toward the transformational use of AI in contact centers. 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 that AI can assist in the resolution of fraud and data issues.21
Institutions must develop an AI strategy to fully realize the value of automation and overcome the imagined limitations.
For more on contact center AI software
Check out our other articles on contact centers and automation to learn about other popular options like Zoho Desk, Talkdesk, Five9, UJET, and many others.
- Top 7 Contact Center Software
- Top 10 Tools For Contact Center Automation
- Top 8 Customer Feedback Tools
- 4 Ways To Gain Competitive Edge With Intelligent Call Routing
- Customer Service Chatbot: Top 7 Vendors & 10 Use Cases
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.
Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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