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How Generative AI Changes RPA

by Ant Sh
How Generative AI Changes RPA

According to Antti Karjalainen, Founder & CEO at Robocorp, an open-source automation company with Python-based tech stack, Generative AI is transforming the RPA landscape, opening new possibilities for automation and redefining our approach to building bots. Its integration with existing RPA practices leads to faster, more efficient, and more intelligent automation processes.

In his recently posted article, Mr. Karjalainen listed the emerging trends that Generative AI brings to the world of enterprise automation today. These are the following:

  1. Generative AI is Replacing Low-Code

Low-code has been a big hit in enterprise for a good reason. It removes what we describe as the “blank canvas problem”. When your world is limited to a finite set of actions, composing an initial solution is easier, and you don’t have to be an expert with the tool to get started.

However, going beyond simple demos becomes increasingly difficult as you have to layer in error handling and more complex logic. Soon you will find yourself coding in a limited, restrictive framework and thinking “why are we doing this in low-code in the first place”. Not to even mention scaling your solution out to hundreds of complex mission-critical automations.

Generative AI is the perfect solution for getting over the blank canvas issue with actual code. You can prompt AI to create your script and collaboratively work together on fine-tuning it. No expert software engineering skills needed, and you will have the full power and flexibility of a scripting language such as Python at your disposal. We have already seen people with little to no programming skills building complete web applications with the help of Generative AI. Now this is coming to change RPA and automation as well.

Low-code / no-code is great for problems with very limited scope, but in the world of automation and bots, we are calling an end to these beautiful handcuffs that trade off too much against an initial wow effect. Let’s have AI handle that for us instead.

  1. Generative AI Brings New Automation Skills

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 a 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.

  1. Python Becomes Even More Relevant for Delivering AI + Automation

When it comes to the technology stack, Python reigns supreme in the world of AI and automation. LLMs are fluent in Python, and every application and technology has a Python library that allows connecting and interacting. In browser automation, the Playwright project stands out as the most modern and powerful tool.

Robocorp has developed extensive open-source tooling to make it as simple as possible to build and package Python automations in a way that they can repeatably run in any new environment without installation and portability hassles. All this is free to use for everyone.

On top of the bot itself, when we talk about enterprise usage, security, and governance is on top of everyone’s mind. Orchestrating bots and data is also a challenge that needs to be solved. This is where Robocorp Control Room comes into play. Its aim is to make deploying Python automation easy, secure, and seamless while providing great data connectivity across systems.

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