How Generative AI and RPA work together. Intelligent automation

A.

Intelligent automation:  combining AI and RPA is faster and more accurate, and contribute to gaining the following four efficiencies:

Increase productivity: Automated applications and processes run faster. Automation of applications and processes, plus the automation of decision-making, forecasting and predictions from multiple sources of structured and unstructured data in real time empowers organizations with greater productivity and accuracy in their planning cycles. For example, Deloitte, an IBM customer in the finance industry, recently used RPA to create bots assigned to automate production of monthly management reports.

Reduce costs: According to Deloitte, “Executives estimate intelligent automation will provide an average cost reduction of 22%,” although they also found that, “organizations currently scaling intelligent automation say they have already achieved a 27% reduction in costs on average from their implementations to date.”

Improve accuracy: The use of both structured and unstructured data and the automation of repetitive processes ensures better decision-making, and less human intervention results in more precise results. An IBM customer in the finance industry recently used RPA to create bots assigned to automate production of monthly management reports. This automation eliminated errors introduced into the process through manual data entry, improving the accuracy of these reports and many others. Also, the use of OCR can help speed up data processing and automate data extraction from many sources.

Enrich the customer experience: Organizations that use technology can better understand customers’ needs, communicate more effectively and bring higher-quality products to market. Customers, in turn, are typically more satisfied in their buying experience. GAM, an IBM customer in the asset management industry, used bots to provide first-line customer support and pricing quotations. This optimization dramatically improved the time it took to provide answers to customers questions, improving the customer experience and streamlining the buying process.

https://www.ibm.com/blog/intelligent-automation-how-combining-rpa-and-ai-can-digitally-transform-your-organization/

B.

CXOs and technology leaders should ask themselves if it’s RPA or AI they really need. With AI advancing at such a rapid pace, organizations can start to adopt AI in combination with traditional hardware and software to achieve holistic and resilient automation.

One of the biggest challenges of using traditional RPA in today’s market is that it’s highly brittle and still requires substantial human assistance and intervention to keep automated processes functioning seamlessly. While RPA does lessen the burden of manual work, it faces challenges around scaling for end-to-end automation and often fails to adapt to evolving processes, requiring a lot of hands-on intervention.

Ref https://www.forbes.com/sites/forbestechcouncil/2021/09/07/ai-or-rpa-which-is-the-true-automation-game-changer/?sh=2034b14583cd

C.

Generative AI is not just about simplifying coding. It brings with it new skills and capabilities to automation itself.

With zero-shot learning, Generative AI eliminates the need for large training datasets. Its ability to perform tasks such as classification and natural language processing (NLP) extraction without any learning data aligns perfectly with the RPA ethos, facilitating much faster deployment with less effort.

Moreover, bots are no longer limited to back office work – with text generation they can now engage in outbound communication with stakeholders. This will require a human in the loop to make sure that messages are aligned but it’s still very powerful addition to bot capabilities.

AI isn’t limited to public information or the training cutoff date either. Retrieval-augmented Generation (RAG) can be used to ground AI generated content to your own documents and business context. Securely available LLMs, such as Azure OpenAI, combined with easy to deploy vector databases is lowering the entry barrier here significantly.

Ref  https://robocorp.com/blog/how-generative-ai-changes-rpa