Revolutionizing Automation in the Era of Generative AI

Amidst the inevitable excitement, there’s a lingering question—Is Generative AI merely another passing fad or a lasting force for change? We are increasingly seeing our clients, entire businesses and Centers of Excellence (COEs), at a crossroads, grappling with the decision of whether to fully embrace this shift or adopt a more cautious wait-and-watch approach.

In this blog, penned by Vaishnavi Yadati, Senior Automation Engineer at Roboyo, we delve into the nuances of Generative AI, and explore how it’s powerful synergy with automation can create substantial impact. 

Embracing change

As much as we hate a cliché, the truth is that ‘change is inevitable’. We wouldn’t be in the business of automation if we didn’t recite this mantra every day with our morning coffees, and in the era of Generative AI, this rings truer than ever.

But with any emerging, and in this case, relatively untested technology, caution is key. After all, this isn’t just a minor development; it marks a profound transformation with far-reaching effects. It’s crucial that together we take proactive measures to assess the wide-ranging implications, tackle concerns, and establish a robust governance model to navigate this significant shift for both organizations and society at large.

Understanding Automation and AI  

Automation, in its traditional sense, involves the use of technology to perform tasks with minimal human intervention. It aims to streamline processes, increase efficiency, and free up human resources to focus on more strategic and creative endeavours.  

Hyperautomation takes this a step further by integrating advanced technologies like Artificial Intelligence (AI), Machine Learning (ML), Robotic Process Automation (RPA), Intelligent Document Processing (IDP), Process mining, and Low code. This integration enables the automation of processes that were once considered too complex or dynamic for traditional automation solutions.  

AI is technology designed to make machines work in an intelligent way, similar to the way the human mind works. ML, a subset of AI, relies on patterns and statistical models to learn and improve performance without explicit programming. 

Generative AI stands at the forefront of cutting-edge AI subfields, enabling machines to produce innovative content, such as text, images, and code, without the need for explicit programming.  

This groundbreaking capability has not only opened new possibilities for automation but has equipped AI Agents with the capacity to learn, adapt, and perform tasks with unparalleled flexibility and intelligence. 

From AI Hype to Implementation

For companies that have already adopted Generative AI, the identification of suitable use cases presents a considerable challenge – how do we use this technology in real world scenarios? 

Here are some simple yet impactful use cases which demonstrate the power of Generative AI. 

1. Chat with your data 

You can save many hours sifting through documents, such as Legal forms and customer reviews, in search of specific pieces of information. Just by securely uploading these documents as context to the LLM (Large language Model) and posing the right questions (Prompts), you can get specific answers or even the entire document summary if needed.  

But not all the information in these documents may be relevant, and it can be rather costly to pass a lot of data to LLMs. To solve this challenge, we can use Retrieval Augmented Generation (RAG), a solution designed to selectively bring in only relevant information for insertion into the model.  

The illustration below demonstrates how Process automation and Generative AI can revolutionize document automation:  

In summary: 

• Load documents from data sources  
• Split documents into manageable chunks  
• Store in vector stores for effective data processing 
• Retrieve relevant data from documents based on user questions 
• Generate final answers through LLM 
• Queue the generated data from LLM as required 
• Utilize the generated data for further automation 

2. Generate unique test data 

LLM facilitates the generation of test data, accelerating the work of testers and saving a huge amount of time. By running an LLM locally, we can tailor the model to specific requirements and keep sensitive data within its own secure network. 

The combination of Generative AI, Automation, and human expertise (something we call Human+) produces a number of customized test cases quickly and easily: 

Below is a short summary of the use case: 

• Create detailed and customized prompts 
• Generate test data by passing the prompt to LLM API 
• Refine prompts based on the output test data 
• Repeat the generation process for satisfactory and customized results 
• Create test cases using the generated test data from LLM 

Navigating the synergy of Generative AI and automation 

The use cases explored in this blog are just a starting point, showcasing the vast potential of combining Generative AI with automation to refine business processes. Whether its augmenting efficiency, realizing cost savings, handling complex tasks, accelerating time-to-market, or elevating customer engagement, the combined force is undeniably powerful. 

Acknowledging both the benefits and risks, proper Governance and risk mitigation strategies are crucial. The adaptability of Generative AI, coupled with automation’s ability to eliminate repetitive tasks, empowers human talent to focus on more strategic initiatives. 

If you’re looking to combine automation with Generative AI and maximize it’s potential, speak to us, your end-to-end automation partner.