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How RPA and Cognitive Automation Differs

Traditionally cognitive capabilities were the realm of data analytics and digitization. Robotic Process Automation (RPA) works best if you have a structured process, involves a large volume of data and is rule based. If this process involves complex, unstructured data that requires human intervention then Cognitive automation is the answer.

RPA uses technologies like screen scraping, workflow automation whereas Cognitive automation relies on technologies like OCR, ML and NLP. RPA provides immediate Return on Investment (ROI) whereas Cognitive automation takes more time for realization.

Maturing to Cognitive RPA

According to IDC, AI use cases that will see the most investment this year are automated customer service agents, sales process recommendation and automation and automated threat intelligence and prevention systems. (IDC, 2019). Here are some practical examples organizations are implementing or prototyping:

1. Customer support and engagement chat-bots which can help cross-sell and upsell products, understand and send voice commands to users in a social media integrated environment. A MEDICI research study of 34 major banks across several geographies (US, EU, Singapore, Africa, Australia, and India) found that 27 out of these 34 banks have implemented AI in their front-office functions chat-bot, virtual assistant, and digital advisor.

2. Retailers using RFID (radio frequency identification), facial recognition, and image recognition technology to monitor retail activity and enable cashless payment. Using ML-based systems to forecast demand for their product list, using robotics and AI for inventory management, automated customer service agents and expert shopping advisors & product recommendations

3. Security teams in organizations use BOTS to monitor their network day and night against vulnerability, Machine learning and predictive analytics improve threat detection by identifying network anomalies, malware detection, user behaviour patterns analysis to find risky users within an enterprise and potentially thwart fraud or insider threats.

4. Airlines automating their operations like intelligent baggage handling using bag tracking technology, using face recognition for self-service bag check-in kiosks, using AI to determine food to be served on flights based on weather, type of passengers and time of year.

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