This article dives deep into the details of the integration of both domains. What happens at an atomic level, the challenges we see & the opportunities that can further shape this integration.
What Is Generative AI?
Generative AI is leveraging Machine learning models to create synthetic data & entire applications, too, at times. The best example of generative AI is ChatGPT. Designed solely for conversational purposes. Then comes Google Bard, again for the same purpose. LLaMA is another brilliant example of use of generative AI to simplify things.
What Is The Need To Integrate Generative AI Services With RPA?
Generative AI begins playing a pivotal role when RPA needs test data for RPA bots. This data can be tricky & sophisticated at times. IT closely mirrors real-world situations, upscaling the test-quality each time. Since such situations exist, it is prominent that Generative AI acts as a positive push to RPA & yet, not everything is as cool as it seems.
What Are The Challenges In Such Situations?
Generative AI integrated with RPA comes with its own set of problems. The data that are required to mimic real-world scenarios are sensitive & at the same time, it requires complex models & extensive training. On top of it, data safeguarding dates back as an issue, when data was first heard of.
Since generative AI uses massive amounts of data for training & creation of synthetic data, safety becomes a colossal responsibility, particularly when the data contains sensitive information. A balance between utility & safety emerges as a deciding factor regarding how much generative AI can tailwind Robotic process automation.
Where Else Can Generative AI Be Used With RPA? Are There Any Challenges?
The integration can be used for optimization & process discovery. At the very core of it, RPA is meant to automate a business that already exists & for that, it needs an in-depth understanding of every process of that business.
This is where generative AI comes into play, as it can analyze copious amounts of data & look for patterns, inefficiencies, space of optimization & many more. This nature enables Generative AI to forecast any upcoming bottlenecks & recommend what needs to be done.
The collaboration holds the capacity to streamline the RPA implementation process and enhance the operations’ overall proficiency. One of the biggest challenges is getting the right personnel to take care of these two very sophisticated technologies. The ideal person needs to be holding a high degree of expertise. This situation, where skilled personnel are needed, may lead to a scarcity of professionals & might push them towards being able to navigate between two technologies rather than sticking to one & these are, again, complex technologies we are talking about.
In summation, the fusion has led to a revolution in the industry by augmenting precision, helping with process discovery & streamlining efforts that lead to optimization. There is no doubt that generative AI has helped Robotic Process Automation to new heights, but, at the same time, there are challenges that exist & they need to be addressed. Determining the role of generative AI in Robotic Process Automation needs a considerate amount of patience to watch where they both go individually & as an integration.
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