Generative AI and RPA: Exploring the Potential

Generative AI and RPA: Exploring the Potential

Robotic process automation (RPA) is adept at automating repetitive, rules-based tasks–such as data entry and extraction or report generation—using software bots. However, these bots are typically restricted to rules-based workflows and struggle to adapt to changes, or function in processes that require decision-making.

Generative AI crucially bridges this gap by providing software bots with the ability to interpret and generate text, helping RPA bots better understand data (especially unstructured sets) and generate outputs that help close the loop in workflow automation. 

In this article, we explore RPA’s strengths and limitations, and the role of generative AI in enhancing its automation capabilities.

A quick recap of RPA’s strengths and limitations

Although RPA is a powerful tool for process automation, it’s not without drawbacks. Here’s more on RPA’s strengths and limitations.

Strengths:

1. Integrates systems without APIs

API, or Application Programming Interface, is a set of rules and protocols designed to allow different software to communicate with each other. RPA acts as a “layer” of automation across your systems–instead of needing API integrations, software bots can simply be deployed to interact with software systems similar to how humans do. These bots can log in, navigate, click buttons, extract and input data, and more. 

Thus, RPA facilitates automation without the need for APIs, which is especially helpful for companies with custom and legacy systems because custom API development costs are high (especially for legacy software).

2. Versatile

Automation with RPA can be either:

  • Attended automation, bots work collaboratively with human employees, helping them perform their jobs efficiently. An RPA bot can, for instance, retrieve relevant information from databases during a customer call to help customer service agents provide speedy and effective service.

  • Unattended automation. RPA bots can also execute labor-intensive back-office tasks independently, such as processing invoices and entering data, freeing up human employees to focus on strategic tasks.

This versatility means RPA bots can be deployed for both front-and-back-office processes.

3. Offers scalability

As your business grows and operations expand, RPA automation can be easily scaled up or down without adding to your human workforce. Bots can be replicated and deputed across departments quickly and affordably to handle the increasing workload.  

Limitations:

1. Dealing with unstructured data

RPA bots can deal with structured data, which has a predictable, well-defined format. However, they struggle with interpreting and extracting unstructured data, such as emails, social media posts, and live chat logs. 

2. Decision-making and problem-solving

Typical RPA bots work by pre-defined rules and instructions, which means business processes must be broken down into repeatable, predictable steps following a logical sequence to qualify for RPA implementation. However, this means RPA bots are reliant on human intervention for tasks or steps involving independent decision-making or cognitive thinking.

3. Adapting to changes in workflows

As RPA bots lack cognitive abilities and the ability to learn from experience, they cannot adapt to new situations, such as changes in workflows or systems, without intervention. Therefore, a change in process logic, user interface, or data format may require a reconfiguration or update to allow the bots to adapt accordingly. This inflexibility poses a challenge in adapting to evolving business dynamics. 

How does generative AI augment RPA?

With its ability to learn from data and generate creative content and intelligent solutions, generative AI presents a powerful opportunity to augment robotic process automation (RPA) to increase productivity. While RPA’s automation capabilities have yielded notable benefits, such as helping companies save between 35 and 65% on costs and earning up to 4 times their investment on RPA, the demand for AI integration is now increasing. According to research by Gartner, 90% of RPA vendors are expected to offer generative AI-assisted automation by 2025. 

Generative AI coupled with RPA helps with workflows and can optimize productivity in the following ways:

  • Decision-making. While RPA bots struggle with decision-making, generative AI’s training data can prepare it for scenarios requiring humans-in-the-loop, like responding to simple customer queries or determining conditions for call routing. Thus, combining genAI with RPA helps offset the cognitive limitations of software bots.

  • GenAI with RPA helps process large, unstructured datasets. GenAI, based on LLMs with advanced natural language processing abilities, can understand the content of unstructured documents. It can also effectively process large amounts of unstructured data such as resumes, purchase orders, invoices, live chat logs, surveys, and legal contracts and extract relevant information from this data. LLMs increase the accuracy of data extraction in document automation.

    Integrating RPA bots with genAI helps automate tasks that involve extracting, interpreting, and classifying unstructured data.

  • Personalized support. Equipped with generative AI’s text generation and natural language processing capabilities, RPA bots can more effectively perform front-office functions like understanding and responding to customer concerns and providing personalized support. While RPA bots can now handle more complex tasks and interactions with clients, human employees are still needed to oversee and manage these processes and to intervene when necessary.

  • Adapt to changes. Generative AI combines artificial intelligence (AI) and machine learning (ML) algorithms to help RPA perform tasks beyond pre-defined rules, providing advanced cognitive abilities to allow it to adapt to changes and learn from mistakes.

Generative AI and RPA in action: 3 Use cases

While RPA has helped companies across industries to streamline their business processes, intelligent automation (IA)—i.e., the combination of RPA with AI technologies–helps organizations unlock new opportunities for automation and drive productivity gains.

Here are four use cases of how enterprises can use genAI and RPA to automate business processes.

1. GenAI-powered chatbots with RPA

Unlike basic chatbots, GenAI-powered chatbots leverage NLP and text generation to enable more contextualized learning, generating personalized creative solutions and human-like interactions. Integrated with RPA, which automates repetitive tasks and allows chatbots to easily handle a massive volume of routine customer queries like account balance inquiries or order status checks, GenAI empowers chatbots to provide exceptional customer service by combining complex problem-solving and decision-making with automated handling of high-volume queries. 

In comparison to traditional chatbots, which handle specific tasks and queries with a predefined scope, GenAI-powered chatbots offer contextual understanding, emotional intelligence, tailored conversations, and the ability to answer unique queries. 

2. Training learning instance models for structured & semi-structured documents

An exemplary use case for generative AI and RPA involves training learning instance models for structured and semi-structured documents. Integration of these technologies would automate the extraction and validation of information from unstructured documents like contracts, reports, and emails and semi-structured documents such as invoices and supply-chain documents like arrival notices and packing lists. 

For instance, in the context of a financial institution processing loan applications, GenAI and RPA integration would automate the extraction and validation of information from multiple document formats like PDFs and scanned images. GenAI-powered chatbots could then interact with customers to gather the necessary documentation and guide them through the application process. Upon receiving the documents, robotic process automation, aided by machine learning algorithms, would take over to process the documents. 

Therefore, while GenAI-powered chatbots would collect required documents and offer real-time assistance and updates to applicants, RPA bots would handle the tedious task of document processing.

3. Intelligent invoice processing

Invoices often come in various forms and layouts, making them structured or semi-structured documents that can be understood by GenAI-powered algorithms trained to understand and extract relevant data from such documents. Therefore, upon receipt of an invoice, RPA bots would extract the required information using pre-trained generative AI models.

The bots would then process this extracted information and automatically process invoices through rules-based processes like data validation, matching against purchase orders and receipts, and initiating payments. 

Summary: Generative AI and RPA

Robotic process automation is a reliable, versatile foundational tool for automation. Additionally, unlike more traditional methods of workflow automation, RPA doesn’t rely on API integrations, making it easy and cost-effective to implement. 

However, despite its strengths, RPA is constrained by limitations–such as being confined to rules-based, repeatable processes, struggling to interpret unstructured data, and a lack of cognitive capabilities. 

Author

Shahzad Malik

Shahzad is experienced in conceiving ideas and developing solutions that deliver on their promise. He has been involved in project planning, product design and development, manufacturing, quality control, risk analysis, and customer services. With exposure to a diverse range of projects spanning from renewable energy to formation-flying satellites, Shahzad has worked across industries including healthcare and telecom, gaining valuable insights into technological innovation and business strategy.
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