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Whether you choose native or hybrid mobile application development, one of the first hurdles you’ll need to overcome is the relatively limited resources on mobile devices. Your target mobile device will have much less processing power and memory than desktop computers or enterprise servers. These constraints might seem like a significant challenge, especially if you’re more familiar with the comparatively boundless resources for conventional software development for web apps. Put simply, RPA involves automating menial and repetitive tasks; cognitive automation adds an all-important extra layer of AI and machine learning. This is a branch of AI that addresses the interactions between humans and computers with natural language. NLP seeks to read and understand human language, but also to make sense of it in a way that is valuable.

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(1) A programming language that uses symbols, or mnemonics, for expressing operations and operands. However, if a business needs to automate repetitive and relatively simple tasks, symbolic AI could get them done. For example, if an office worker wants to move all invoices from certain clients into a dedicated folder, symbolic AI’s rule-based structure suits that need.

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For many companies, leapfrogging over RPA and starting with cognitive automation might seem like trying to run before you can walk. ● Better analytics – Today, technology is making data visualization, data mining and predictive analysis very easy. Fintechs are able to collate humongous amounts of data and analyse it to arrive at accurate decisions thus driving customer engagement higher.

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According to Saxena, the goal is to automate tedious manual tasks, increase productivity, and free employees to focus on more meaningful, strategic work. “RPA and cognitive automation help organizations across industries to drive agility, reduce complexity everywhere, and accelerate value of technology investments across their business,” he added. You must start somewhere though and most organisations tend to do so at the more basic end. Your automation could use OCR technology and machine learning to process handling of invoices that used to take a long time to deal with manually. Machine learning helps the robot become more accurate and learn from exceptions and mistakes, until only a tiny fraction require human intervention.

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In the struggle to optimize customer service, insurance agencies are actively adopting virtual assistants and chatbots. In an ever-evolving digital landscape, the insurance industry finds itself at a crossroads, seeking innovative ways to enhance customer experiences and adapt to changing expectations.

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“Cognitive automation is not just a different name for intelligent automation and hyper-automation,” said Amardeep Modi, practice director at Everest Group, a technology analysis firm. “Cognitive automation refers to automation of judgment- or knowledge-based tasks or processes using AI.”

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