Advances in Artificial Intelligence and machine learning are set to make a big impact on the business environment in the years to come, particularly in the field of customer service. Already AI technology is being implemented in contact centres in the form of Amelia and other chatbots. What does this mean for the future of contact centre advisors and how can the handover from machine to human be facilitated so that it is beneficial and efficient?

Will the role of humans in customer service become obsolete?

The primary concern for people working in the contact centre industry is that the requirement for them will decrease resulting in job losses. Optimists claim that mainstream AI implementation is still a long way off and poses no threat to jobs in the immediate future. Others believe that despite advances in machine learning there is still a vital role to be played by humans in the field of customer service.  Is there a way to ensure job security while still taking advantage of the benefits of AI technology? The answer lies in how effective the handover is between advisors and machine.

There are certain tasks that machines are simply far more efficient at than humans – data analysis for example.  Machines have the ability to process vast amounts of complex data in a very short space of time compared to humans. However, machines also rely on humans to provide the algorithms used to calculate the outcomes. Integration is also key, it’s all well and good to have the data, but unless the information is made available in a way that is useful to humans, it is largely ineffective.

Work roles for humans and machines

To create effective integration it’s important to consider who does what, best. While machines can process data better, they are limited by inputs and set parameters. In other words, if a customer has a particularly complex or emotional query that cannot be answered in a linear manner, despite having access to vast amounts of data, a machine will not be able to provide an effective solution. Humans on the other hand can, because they do not have the same constraints as machines.

Machines are also limited in their ability to pick up on the emotional state of customers and convey empathy, whereas humans can do this instinctively. Often customer service queries become emotional issues and customers want to know that someone is on their side. A machine that can only operate according to set parameters may end up frustrating customers more than helping them.

One of the best examples of an effective handover between humans and machines is with regard to frequently asked questions in contact centres. For advisors dealing with these types of repetitive questions it can become boring, yet machines are only too happy to provide the same answers to hundreds of different people every day. Using machines to answer FAQ’s allows advisors to handle more complex tasks and helps to make their job more interesting.

Machines are also not constrained by normal working hours and can be made available to answer frequently asked question questions 24/7, providing at least a basic level of customer service. When unable to resolve the customer query they can escalate the query to a contact centre human.

When advisors are searching for answers to customer queries they can tap into data base resources and use machine learning to access information faster than searching manually. In this way machine learning becomes a valuable support tool that learns and adapts, for example ‘the last 30 customers called in about this and here was the best answer’.

Optimising machine and human resources

One of the largest costs in any business is labour and machine learning and automation provides an avenue for businesses to optimise costs. By reducing the amount of time advisors spend on repetitive tasks and using them for more advanced functions improves advisors engagement which in turn can reduce absenteeism and attrition. Both of these represent huge costs for business. Every time an advisors is absent it reduces the efficiency of the whole team and the workload per person increases. When advisors leave there is the cost of recruiting, onboarding and training someone new, which can equate to as much as five times the annual salary.

Thoughtful integration, using the strengths of both humans and machine learning can help businesses to optimize costs and make customer service more effective. With customers demanding multiple channels with which to interact and expecting almost instant answers there is a role for machines to play in supporting contact centre advisors and alleviating their workload. Machine learning can also provide valuable customer data to help personalise the customer experience, even matching customer’s personalities to advisors so that they speak to someone they can relate to and who understands them.

Ultimately, there is place for both human and machine to operate side by side and together to make customer service more effective.

For access to our thought leadership in this area please contact us on 020 7871 9797 or email info@emberrealresults.com