Integration of AI into Call Center operations – Enhanced Call Center Agent and Customer Experience

The term artificial intelligence (AI) refers to software driven computing systems that incorporate Data Analytics and Big Data. AI systems leverage this knowledge (data) repository to make decisions and take actions that approximate cognitive functions, including learning and problem solving. These tasks are normally considered within the realm of human decision making.

At the outset it is pertinent to focus on what the often used umbrella term, Artificial Intelligence or AI in short is, and how it integrates into the call center operations, Big data, big data technologies, data analytics, machine learning, algorithms.

Understanding big data:

Big Data involves dealing with an enormous volume of information (e-commerce transactions, mobile devices, social media and the Internet of Things (IoT) etc., creating new data at a very rapid velocity or rate (real-time streams of data from sources like Twitter, Face book, IoT sensors, RFID tags and mobile apps, ability to store a vast variety of data (text documents, photos, videos, audio files, email messages etc.)

A Variety of technologies are used to deal with big data.


Often enterprises choose virtualized cloud based storage solutions that offer excellent scalability.

Data management:

Vendors’ tools fit into a variety of categories, including data integration, data virtualization, data preparation, ETL, data quality and data governance.


For most organizations, the goal of big data initiatives is to generate valuable insights that the company can then use to become more efficient, better serve customers or become more competitive.

Big data analytics tools include data mining, business intelligence, predictive analytics, machine learning, cognitive computing, artificial intelligence, search and data modelling solutions.


The best big data security technologies contain encryption and access management solutions.

Cloud-Based Tools:

Via cloud you can manage store and secure data. The advantage in choosing a cloud big data tool is the affordability and easy scalability that the cloud offers.

Big data analytics:

Descriptive analytics:

This is the most basic form of data analysis. It answers the question, “What happened?”

Diagnostic analytics:

Once an organization understands what happened, the next big question is “Why?”

Predictive analytics:

Organizations don’t just want to learn lessons from the past “what and why” but they also need to know “what’s going to happen next.” That’s the purview of predictive analytics.

Prescriptive analytics:

Predictive and Prescriptive (latest) analytics are in vast use by enterprises. They use sophisticated models and machine learning algorithms to anticipate the results of various actions. Algorithms work by learning the patterns that exist in the historical data that is fed, then using those patterns to arrive at a model to predict future outcomes.

Analytics tools or modelling techniques are powered by several different models and algorithms that can be applied to wide range of use cases. Determining what modelling techniques are best depends on what analytic (predictive/prescriptive etc) questions one wants to ask and whose answers will provide the enterprise meaningful insights into decision making. Once your algorithm determines a pattern based on historical data, a modelling technique is arrived at, then, information (new data) about a new customer is passed on to the model and it will make a prediction.

Having understood the seamless integration process of AI into call center operations, it is worth examining the value-add of customer experience.

  • Prioritize customer queries and recommends solutions which, when put into action, can lead to increased customer satisfaction and reduced operational costs.
  • work without breaks
  • AI can easily learn new skills and it doesn’t make mistakes. Machines work tirelessly with consistent efficiency, resulting in greater productivity.
  • If you can personalize your customers experience through machine learning and predictive analytics, it will improve your brand image and enhance customer experience.
  • Customers can even place orders, or access devices with fingerprints and AI make businesses to build a more interactive, personalized customer experience.



 Relieve Repetition and Tedium: 

AI-powered chatbots are great at taking on many of the menial tasks that quickly become tiresome and repetitive to humans (e.g. password resets, account balance inquiries).

It relieves much of the burden placed on human agents and frees them up to focus on resolving more complex customer journeys, which is often more fulfilling.

 Improve Agent Handoff: 

Sometimes customer journeys that start with a chatbot need to be viewed to a human agent for resolution. When this happens, chatbots are able to transfer the full data of the journey to the agent, which prevents the customer from having to start over again.

When customers are talking to chat agents, the agent is able to use insights that given by machine learning technology to engage the customer with real-time offers so it will be much easier.

 Reduce Costs:

Chatbots are the preferred method of communication for many, because they allow customers to self-serve and resolve problems without having to call and wait (and wait) to speak with a human agent. Whenever you can streamline your customer experience and reduce resolution times by deflecting away from the more costly call center, it’s an ideal outcome for your organization and your customers.

Chatbots will work 24/7. This includes weekends and holidays – making them the perfect solution for handling questions.

 Quicker resolutions

Offering your customers an enjoyable, comfortable – and fast – self-serve experience that essentially eliminates wait times is a win for your organization.

AI enables contact agents to have the entire customer journey context and access to a wealth of on-demand information, so they can quickly identify the problem and offer an immediate resolution.

It will reduce your average handle time (AHT).

A review of the customer journey is imperative to find out where chatbots and human agents can complement or supplement each other in handling the customer tasks.

Companies that achieve self-learning algorithms via AI will gain a competitive advantage because they’ll be getting the foremost from their sea of knowledge. Thanks to that, systems that are currently rules-based will move toward “cognitive” systems that leave more intelligent prediction and reaction.

This will help to enhance the customer experience by improving the way contact centres both predict and answer demand.

Improving Self-Service

The main areas where AI is going to be utilized in the contact center are in advances to the self-service capability of day-to-day requests and straightforward interactions.

However, for AI to really fulfill its potential, sophisticated linguistic processing must make people desire they’re actually chatting with a person’s being, and systems got to be fully integrated to minimize the issues in its implementation.

For example, if a customer profile is incomplete, this might limit the power of AI to manage interactions successfully.

To allow AI to automate accurate responses and serve consumers most effectively, it must have the proper information and data.

If done correctly, instigating AI in contact centers can make sure that enquiries are managed more efficiently.

Communicating With Customers via Robots

Robots are consistently reliable, available and specialize at interfacing with self-service applications and intelligently checking out information.

However, getting the proper balance between human and robot is vital. Don’t just believe AI empowering self-service, also consider how it can empower advisors, by giving them longer to create rapport with customers while on the phone.

Identifying Call Types and spending Contacts to Relevant Channels:

AI can be used to help identify the type of incoming call request/background information/nature of enquiry so that it can be passed on to the relevant channel, be that a human agent or chat bot as the case may be.


Future success for companies are going to be somewhat hooked in to how they organize their customer interactions and their willingness to take a position in their contact center team, who are increasingly having to affect more complex customer interactions.

AI holds great potential for augmenting the power of a call center advisor. From reducing the time spent handling repetitive tasks to automated chat bots.

Some fear that AI might at some point replace call center jobs. It is quite the opposite. People will always value a person’s conversation quite an automatic message. Think call abandonment. How more guilt-free is it to hang up on a pre recorded call than hanging up on a human being?

AI should not be viewed as a replacement for a call center agent but as an enabler for him to work smarter by enhancing and augmenting his highly skilled role.

Routing of agent calls to AI enabled tools can increase call attendance and conversion rates thus reducing the cost per call.

Artificial intelligence (AI) is being continuously harnessed. Many enterprises have already started implementing AI because of the benefits it brings. As it stands today, AI has improvised every aspect of business (from prospecting to customer experience). , AI can also enable to identify your customers’ pain areas, helping enterprises to better understand their unique needs, arrive at the needed speed and accuracy in decision-making and provide solutions.

AI in contact centers delivers valuable insights that can be used to streamline, optimize, and personalize customer journeys to enhance customer experience. It is high time that enterprises develop their plans to leverage AI to significantly improve upon customer experience and thus stay ahead of the competition.


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