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Robotic process automation (RPA) is rapidly transforming how businesses operate. According to Gartner, 72 percent of companies will use RPA in some form during the next two years. With such a rise in RPA adoption, innovation and digital transformation teams are scrambling to do their due diligence in understanding the scope of intelligent automation technologies, with a focus on long-term scalability and performance.
On the other hand, recent advances in conversational AI are also set to disrupt how businesses approach automation. Perhaps the future lies in a combination of the two technologies, in the form of conversational RPA, where the power of natural language understanding and machine learning can join forces to enable end-to-end automation of customer-facing processes. The convergence of RPA and Conversational AI is the next step in the intelligent automation journey as they create powerful synergy by taking on where the other has left off. While RPA kicks off, automates, and supervises the execution of business processes behind the scenes, Conversational AI manages the key human interactions in the front end.
Let’s explore some use cases and understand how backend process automation meets conversation-driven automation to revolutionize many industries, both in terms of customer experience and process efficiency.
Consider insurance claims to be a complete customer journey with many interactions and processes handled by multiple systems and staff. Without speed, precision, and efficacy in the back-office, no matter how good and responsive the customer interaction aspect is, the time to settle a claim is reliant on the complete chain of events from a customer filing a claim to validation, approval, and lastly payment.
In such a scenario, a claim processing virtual agent can assist and gather documentation and details related to an incident, as well as enable uploading images, documents, or videos that provide more context to help in the subsequent process. Furthermore, the integration of optical character recognition (OCR) with RPA can enable insurers to automatically interpret content from uploaded documents and direct the information into the appropriate systems. This improves data quality and accuracy while curtailing insurance backlogs. Thereby, enabling a frictionless, human-like, end-user experience.
The challenge for the retail industry is to meet the towering expectations of the consumers as customers anticipate same-day or next-day delivery. In addition, the massive volume and fast-moving nature of the products increase the complexity of retail supply chain management. The question is, what is the best route ahead for the retail industry?
Well, A combination of RPA & Conversational AI can alleviate many of the issues that merchants encounter. The fundamental goal of AI and RPA technologies is to eliminate human error and improve the efficiency of business processes. Supply-chain management in retail is an excellent field where a mix of Conversational AI and RPA can bring forth surprising results, much as automated checkout, tailored recommendations, and churn rate reductions are some of the powerful use cases of applying AI solutions in retail. The fusion of these two technologies can elevate inventory management and the customer experience in the retail sector.
Across the globe, healthcare is one of the industries where human workers act as information routers, manually moving loads of data between systems by selecting, copying, and pasting. This repetitive work is tedious and unsatisfying, and it also has a high error rate since people lose focus and interest when doing monotonous jobs. This calls for intelligent automation in healthcare.
Conversational AI and RPA can de be deployed in hospitals and clinics to assist staff with the growing number of repetitive tasks, while also providing a superior experience to patients. AI-powered virtual agents can be used to route and direct patients to the most appropriate care when they are seeking information regarding the cause or treatment of their symptoms. An RPA component is incorporated as a virtual agent that can lead the patient towards picking a time to visit a doctor and automatically schedule an appointment.
The energy and utility industry is transitioning from a largely traditional, regulation-heavy sector to one that is driven by technologies. Not only do the industry's fundamental operations have a lot of automation potential, but there are also a lot of recurring and rule-based procedures in the back and front office, which opens up a lot of possibilities for process automation. This makes it a perfect candidate for initiatives to revolutionize customer service and boost operational efficiency.
Whether it’s a query on a utility bill, updating customers on the status of a service outage, or responding to other service requests, RPA and Conversational AI can be adopted by utility providers in their contact centers to improve the customer experience while cutting service delivery costs. This relieves people of monotonous tasks, reduces errors, and increases quality and compliance.
When it comes to banking, the process of applying for a loan or a credit card account and getting approval is another area where RPA and conversational AI can play a combined role.
Within a conventional loan process, the customer journey and approval process are way too complex, requiring multiple steps, systems, and handovers that are loaded with friction and inefficiencies. This can lead to a loss of loan customers during long and frustrating cycle times. Though, it’s quite challenging to deliver a flawless customer experience in the banking sector, but, if we can combine the two technologies in disruptive ways, we can reap the immense benefits from the synergy they generate.
Watch the video below to see how the combination of voicebot and RPA creates faster, more personalized, and responsive customer service during a loan application process.
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