Will AI replace human support executives in the future?

Artificial Intelligence, or AI as it is more commonly known, has been an object of fascination for creators and audiences alike. 

While the fictional versions of AI often show a supercomputer of sorts, often capable of destroying humanity, the actual technology has much humbler roots. One can trace these roots back to 1956 when American Computer scientist John McGarthy adopted it as an academic field. 

Today, AI has come a long way. From being a basic academic subject to exhibiting multiple use cases across verticals ranging from healthcare, R&D, public works and corporate organisations. 

Artificial intelligence allows the digital simulation of human processes through machines, especially computer systems. AI is able to do so by computing and analysing large amounts of training data for correlations aimed to predict future scenarios.

AI in customer service

One of the latest entrants in the portfolio of verticals incorporating AI-enabled systems is customer support and relationship management. Through automation using AI, businesses can expect to enhance customer experience through quicker resolution and bringing down the cost of operation. Several new factors have emerged supporting this adoption.

Customer expectations are very fickle. With the dawn of the digital era, these expectations have risen to the point of demanding quick and reliable resolutions at any place and time. 

Companies are also realising the importance of customer support. Brand perception is very dependent on a strong foundation of customer recognition and therefore, customer service becomes an indispensable process to sustain a huge customer base. 

Further, it is virtually impossible to manage the expectations of a large customer base through conventional methods. With one of the biggest customer complaints being the time taken for resolution, insisting on traditional methods would either lead to high operational costs or a substantial lapse in support quality. Neither of these is an indicator of a successful business operation. AI in customer service solves that by automating several other manual tasks.

The current AI play

When we talk about Artificial Intelligence, we fail to note that AI in itself is a larger umbrella of various technologies. 

Similarly, AI in customer service usually refers to two such technologies: Machine Learning and Natural Language Processing (NLP). These technologies are applied, either individually or together, on different operational tasks to augment the customer experience. 

Machine Learning, the latest buzzword in the tech circuit, is a form of AI which 'teaches' machines (such as computer systems) to collect huge volumes of relevant data and analyse it to simulate future projections using pattern recognition. 

On the other hand, NLP teaches machines to understand various forms of communication such as text messages or audios and generate the appropriate response.

Chatbots is one popular example that displays the properties of both Machine Learning and NLP. Based on the input, which could be the customer message or audio command, the chatbot tries to understand the issue by using the programmed NLP capability and provides the appropriate next steps. Such conversations are stored and analysed for further improvements in the resolution process through in-built Machine Learning algorithms.

Models of deployment

The deployment of AI for customer support is not a straightforward task—there is no set blueprint for operations that can differ across type and size. Based on the individual requirement of each operation, AI can be deployed in multiple forms within the organisation infrastructure. 

1. Frontend bot substitution 

This model substitutes frontend support agents with completely automated systems. These systems would be capable of interacting with customers by themselves and provide the appropriate solution based on the interpretation.

However, the current level of this approach is not sufficient to provide customer service at the same level as human support.

2. Backend AI support 

This approach can be used to replace commonly asked questions or frequently committed errors and remove the redundancy of explaining the entire solution to each customer. Such systems can be configured to redirect the more complex tasks to human agents, thus reserving the critical workforce for important and technical matters while automating repetitive tasks.

3. Augment agent service 

This deployment combines the best features of the human workforce and AI technologies. Customer service is enhanced by equipping the support executives with relevant information supplied by AI-enabled contextual tools. These tools can easily sort huge pieces of data to mine the exact piece of information that would be relevant to the customer's issue.

The big question

News features about Artificial Intelligence talk at length surrounding the disruption of customer support by AI. The increased use and scope of AI for customer service raises the question: is there any point when Artificial intelligence will completely overrun the customer support vertical, putting hundreds of thousands of support executives out of a job? We believe, not exactly.

To delineate this properly, let us try to visualise the entire spectrum of customer interactions along the axes of urgency and emotion. Urgency refers to the level of customer expectation with regard to the time taken to resolve the issue. 

Emotion refers to the ideal amount of empathy demanded by an agitated or emotional customer. Therefore, you can put every kind of customer interaction in one of the four quadrants formed by these 2 axes.

The current wave of AI development is highly inclined towards enhancing support systems for urgencies. They are capable of sifting through huge heaps of data to figure out the correct answer in the shortest time possible, therefore fulfilling the requirements of the 'high urgency' customers. 

Imagine a customer looking for the process of registering themselves for your company's service? An integrated chatbot can quickly redirect them to the correct portal.

However, the current AI technology is not developed to the extent of simulating human emotions in conversations with customers. 

This makes these systems unsuitable for 'high empathy' scenarios, wherein providing the resolution is as important as calming an angry customer. Many customers prefer to talk to a human agent since they associate them with trust and care. Surveys indicate that as many as 50% of people in the UK prefer to talk to a human rather than a machine.

A computer can be taught to provide the correct information to a traveller, but the empathy axis is not configurable. 

This would backfire if the customer is exhibiting high emotions due to the delay in an important flight to a family emergency. The need for human empathy in customer interactions came to the limelight during the COVID-19 pandemic, during which several people were under a huge mental strain owing to social distancing protocols. 

The discussion surrounding AI in customer service has been too focused on the concept of Artificial Intelligence replacing human agents. However, this debate forgets the actual objective driving the development and adoption of AI: to 'enhance' customer interaction. 

The biggest opportunity is to deploy AI in the form of tools that can ably support customer service processes and make them more effective and more reliable.

To know more about this, get on-call with our cloud experts at 0800-084-3663 or drop an email at contact@acefone.com!