Cognitive RPA leverages AI-powered technologies like Optical Character Recognition (OCR), Text Analytics, and Machine Learning (ML) to improve the experience of your workforce and customers.
Cognitive Process Automation with the rising of technologies, Robotic Process Automation (RPA) and artificial intelligence (AI) has seen a major surge in the last couple of years. Earlier, business process improvements were multi-year efforts and required an overhaul of enterprise business applications and workflow-based process orchestration.
According to Fortune Business Insights, the global RPA market size is expected to experience considerable growth reaching 6.10 billion by 2027 with a CAGR of 24.9 per cent between 2020 and 2027.
It is emerging as a disrupting technology across industries and geographies to perform huge amounts of operations in desktop and cloud environments. It also unlocks better ROI by enabling incremental revenue opportunities by easing digital transformation and freeing resources to emphasise process improvements.
Advancements in AI and ML have enabled automation to witness a surge in its growth and application. In November 2018, Kryon Systems collaborated with the Institute for RPA and AI to offer consulting services on assembling the technologies for corporate executives.
Why is Cognitive RPA on a Surge?
Due to RPA’s ability to rapidly drive the automation of business processes without disrupting existing enterprise applications, it is happening. Use cases on AI in the enterprise range from the front office to back office analytics applications. According to a study done by McKinsey, “customer service, sales & marketing, supply chain and manufacturing are among the functions where AI can create the most incremental value.” However, despite having the potential of AI, the study says that only a few pioneering firms have adopted AI at scale. The key of the adoption limitations is the availability of massive data sets, generalised learning, regulation and social acceptance due to potential bias in algorithms.
While RPA is used for attended automation, especially where it requires some assistance from people by automating repetitive processes, AI is viewed more as a form of unattended automation not requiring human labour at all by automating end-to-end processes. RPA uses structured inputs and logic, while AI uses unstructured inputs and develops its logic.
RPA Use Cases In Real-Time
Nowadays, organisations require more cognitive capabilities, which calls for the integration of RPA with AI to automate more complex tasks and business processes with data from unstructured sources like scanned documents, emails, letters and natural speech. As for some industries who think automation is a costly effort, they are witnessing early adopters of RPA outperform the rest of the organisations with an increased ROI and also round-the-clock work at reduced costs.
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BFSI (Banking, financial services and insurance) Sector
According to Grand View Research, the BFSI sector was a market leader in RPA adoption in 2019, accounting for a 29 per cent share of the global revenue. This sector requires streamlined and accurate handling of large volumes of sensitive data. Many of the business tasks associated with the BFSI can easily be automated — tasks like data entry, compliance regulations, statement processing, balance sheets, etc. By automating these processes, it is boosting their overall performance.
Speedier processing times, eradicating human errors and providing easier access to information is serving to boost customer experience and loyalty. RPA also assists with the stringent and complex compliance regulations which dominate the industry and allows for easier compliance processes and audits with 100 per cent accuracy.
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Healthcare Industry
The healthcare sector is arguably the most demanding and regulated sector. As a result, it has an acute need for efficiency and accuracy for the handling of large volumes of sensitive information. RPA helps reduce the amount of back-office tasks, patients can receive more value-based care and attention as the administrative pressures on healthcare workers are diminished.
A shorter waiting period, more detailed insights into patients’ histories and digitalisation of patient data create a more efficient healthcare process that dramatically improves the patient experience. In addition, the adoption of RPA helped the healthcare sector’s operational efficiency significantly, giving more time to focus on its primary objective, which is patient care.
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Manufacturing Industry
RPA facilitates various reporting and administrative tasks. Its adoption in the manufacturing industry helps strengthen supply chain procedures to bridge the gap between regular administrative operations and hands-on assembly line manufacturing. The result is a boost in efficiency, which translates into increased revenue, making the manufacturing industry’s back-office systems more efficient as its factory assembly lines.
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Transport and Logistics Industry
Transport and logistics companies are leveraging the speed and efficiency of RPA to streamline their business processes to bolster the efficiency of their end-to-end services. RPA increases productivity by automating front and back-office processes such as streamlining order management, optimising order distribution cycles and linking external supply chain applications to internal tools. Also, these processes need to be accurate as this industry accounts for downtime to the second. Transport and logistics businesses deploying automation within their business operations are reaping the rewards of efficient and on-time deliveries: happier clients and customer loyalty leading to increased revenue.
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Utility Sector
Utility sector (electric, gas, water, etc. deal with monetary transactions regularly, which results in demand for RPA implementation in accounts and billing departments. RPA is also being used to accurately and efficiently assist with meter readings, a cornerstone of the industry. With automated meter readings, utility companies can get precise numbers and bill accordingly, removing the presence of human error and tampering of meters to display lower readings.
