Intelligent automation defines a class of new technologies that includes robotic automation and cognitive learning, which can help organisations to deliver customer service more efficiently and effectively. Growth in this market is accelerating at a rapid pace, but with it comes the emergence of new terminology that is often confusing and at worst, misleading.
In reality, organisations have to deal with a plethora of information that originates both internally and externally. If this data originates internally it is typically structured, but the content that flows in from external sources is often unpredictable, unstructured and is received every day from customers, suppliers and employees.
In simple terms, robotic automation is able to automate structured data processes and cognitive learning, considered to be the next step towards streamlining and automating the handling of unstructured content.
Robotic automation software has existed for some time and as a result has a broader awareness and acceptance than cognitive learning technology. It offers efficiency gains where a business process requires an army of workers to perform labour intensive data entry, checking or movement of structured data from one system to another. However, robotic automation can only handle structured data, which is typically generated by systems. This is because robotic automation is pre-configured to follow rules based on logic.
Cognitive automation, by comparison, is considered to be the next generation, able to streamline and automate even the most unstructured content that flows into organisations from external sources, such as customers who will use unpredictable and descriptive language. With cognitive automation, the technology learns the pattern of content through the natural consequence of processing and watching how people make decisions on it.
With every transaction the technology becomes more confident and capable of autonomous processing. People remain essential to the process by helping with decision-making where confidence in the learning cycle is low. Ultimately, it is their actions that help cognitive automation to achieve confidence levels that enable more autonomous operations.
Cognitive learning is artificially intelligent and is therefore more complex. This, along with machine vision, analytics and NLP (Natural Language Processing), to name a few, is able to perform tasks that require a level of intelligence that is acquired through learning. This makes them ideal for customer service operations, especially those who deal with demanding consumers.
With learning capabilities, cognitive automation is able to continuously optimise its knowledge with every customer transaction and in doing so, customer service operators are no longer buried under emails, claims and complaints and can spend more time engaging with customers, utilising the human strengths of empathy and creativity.
Jo Causon, CEO for the Institute of Customer Service says, “Artificial Intelligence can take the emotion out of the process to put emotional intelligence of the heart of the customer – organisation relationship”. In addition, organisations will be able to process what customers are saying in real-time and respond appropriately across the omnichannel world. Cognitive learning technology can give meaning to even the most unstructured content, providing insights that provide understanding of customers and making sure that relevant information is shared with the right people and systems without delay. The productivity of staff increases and customer satisfaction improves, which ultimately leads to an improvement in reputation and financial performance.
More importantly, because the technology is able to learn, the workforce doesn’t need to scale to cope with growth or even unexpected surges in demand. For example, cognitive automation would be useful when a company receives a surge of complaints due to a rail strike or a faulty product. The system implemented could upscale accordingly without the need to employ temporary staff or outsourcing.
In conclusion, there are a plethora of options for an organisation to choose from dependent on their individual use case and often it is not easy to see the ‘wood from the trees’. It is therefore essential that when you are investigating intelligent automation solutions you ask yourself the following questions:
- How are you going to use intelligent automation to improve your processes, to innovate and to grow?
- How will you integrate the solution with your workforce, processes and IT architecture?
- What data sets will you use for a proof of concept or for machine learning?
- What staff training can you initiate to enhance and adapt skill sets?
- What are the risks to your business and customers?
In an age where customer experience could make the difference between success and failure for your organisation, intelligent automation could be key to gaining competitive advantage.