14 07

Applications can be as straightforward as generating an automated response to an email, through to sending out legions of bots to automate multiple jobs across a company’s ERP systems used across financial management, HR, supply chain management, manufacturing, and distribution.

Through the application of NLP and other AI systems, RPA systems can effectively learn through experience, allowing them to become adept at customer-facing processes, such as customer service, sales, and marketing; but also proficient at managing back-office jobs.

Chatbots are defined by several characteristics: they are applied to customer- or user-based conversations that take place by voice or text, be that phone, voice-activated interfaces, email, or online chat; they are put in place to react, rather than merely automate, adapting to changes as knowledge is gleaned from data and experience; and they are intended to simulate less structured (or robotic) human conversation.

If robots had a heart, then the beating heart of Chatbots would be Natural Language Processing (NLP), the AI that governs how computer systems analyze natural language data and identify and extract meaning from contextual nuance, upon which it can then base decisions.

From a business perspective, the different natures of RPAs and chatbots can complement one another to a powerful degree: conversational AI can interpret customer intent, for example, and pass on data to inform a more rigid RPA-driven processes.

Companies can use chatbots to transform user interfaces by using text-based and social media-style interfaces.

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