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Beyond RPA and Limitations of RPA Tools

Kognitos

Beyond RPA: Why it’s time to say goodbye

TL;DR

Traditional RPA cannot process unstructured data, which makes up 80% of organizational data, making it a poor fit for modern enterprise automation at scale.
RPA's inability to handle exceptions forces developer intervention every time an unanticipated error occurs, driving up maintenance costs to roughly $5 in services per $1 of tooling spend (Forrester).
Intelligent Automation powered by Generative AI overcomes these gaps by processing both structured and unstructured data, enabling non-technical users to build automations in natural language, and handling exceptions without developer involvement.
Organizations that continue relying on legacy RPA face compounding costs and poor scalability as data volumes grow; upgrading to AI-driven automation unlocks use cases like Intelligent Document Processing and full exception handling.

Historically the most sought after automation tool for most enterprises, RPAs are slowly and steadily becoming irrelevant as businesses are moving towards automating complex, end-to-end processes from the simple, rules-based repetitive tasks they were used for.

It is evident that this is a result of the inherent limitations of RPA Solutions. This blog discusses, in depth, these limitations, and how modern tools and technologies like Generative AI can help you automate better.

Processing Unstructured Data

Unstructured Data, as the name suggests, refers to all information acquired via sources such as text-heavy documents, emails and media formats like images, videos, etc. Processing this unstructured data, however, is RPA’s Achilles Heel! As per a report from Gartner, about 80% of all organizational data is unstructured.

RPA’s reliance on rigid rules and templates does not allow it to read information from documents such as contracts and bills that do not conform to a set template. It is, therefore, not surprising that many of the limitations RPAs face are stemming from, in one way or the other, this inability to process unstructured data.

Increased Inefficiencies

RPAs depend on manual resources to process unstructured data. This leads to slower processes reducing the overall organizational agility, ultimately leading to increased inefficiencies.

Ballooning Costs

As per Forrester, conventional automation tools such as RPAs necessitate $5 in services for every $1 spent on the automation tools themselves. The expense balloons further in processes with many document variants or exceptions to business logic, leaving many RPA projects on the shelf. Most of the cost of maintaining traditional automations comes from the cost of handling exceptions.

Poor Scalability

As an organization grows, it needs to scale its automations keeping in mind its increasing size. However, as it grows, so does the volume of unstructured data. RPA’s inability to process this data thus becomes a major problem for these organizations when scaling.

Lack of Cognitive Skills

Another problem created by not being able to process unstructured data is the lack of cognitive skills. Unstructured Data contains very valuable insights that any business could leverage to improve their knowledge and make better business decisions. With RPAs, however, businesses miss out these insights and the opportunities attached with them.

Inability to Handle Exceptions

Another major problem associated with RPAs is the inability to handle exceptions. When an RPA encounters an unanticipated problem in its working, it throws an error that needs to be addressed by software developers and the likes. Yikes!

Time to say goodbye to Legacy Automation Solutions?

The question that arises then, is, if not RPA, then what? The answer to this, in simple words, is Intelligent Automation.

Intelligent Automation refers to the next generation of automation wherein technologies such as Generative AI are leveraged to address the shortcomings of legacy automation solutions. This empowers automation solutions to process both structured and unstructured data, allowing them to automate more complex tasks with minimal dependence on manual resources, such as IT/Tech Teams, etc.

These technologies democratize the power of automation to business users while maintaining IT governance and controls in place. Natural Language Processing Automation, for example, allows even non-technical employees to build, check and verify their automations, allowing businesses to significantly reduce their costs on the maintenance of their automation solutions, as was the case with RPAs.

Additionally, this unlocks hundreds of potentially crucial use cases such as Optical Character Recognition (OCR) and Intelligent Document Processing. But what just might be a gamechanger is Exception Handling: a major source of inconvenience for existing RPA users.

AI for IA?

The future, it seems, belongs to those who adapt with the times. And the times: they’re a-changin! AI is changing the way business is done across functions in companies in every industry. The ramifications are huge, and so are the opportunities. It is up to organizations to decide if they still want to go ahead with an outdated technology, or give automations an upgrade they deserve in today’s day and age.

Frequently Asked Questions

RPA, or Robotic Process Automation, is a technology that automates simple, rules-based repetitive tasks by mimicking human interactions with software. Its core limitation is an inability to process unstructured data, which makes up roughly 80% of all organizational data according to Gartner. Because RPA relies on rigid rules and templates, it cannot read information from documents like contracts or invoices that do not conform to a fixed format. As enterprises grow and their data complexity increases, this fundamental gap makes RPA increasingly irrelevant for modern automation needs.

When RPA cannot process unstructured data such as emails, text-heavy documents, or images, organizations must rely on manual resources to fill that gap. This dependence on human labor to handle what automation cannot slows down processes and reduces overall organizational agility. The result is a cascading set of inefficiencies across workflows that were supposed to be automated. Additionally, RPA systems throw errors when they encounter unanticipated exceptions, requiring software developers to intervene and resolve issues that interrupt operations.

According to Forrester, conventional RPA tools require approximately five dollars in services for every one dollar spent on the automation tools themselves. Costs balloon further when processes involve many document variants or exceptions to business logic, which causes many RPA projects to stall or be abandoned. As organizations scale, the volume of unstructured data grows proportionally, making RPA's core weakness an even greater obstacle. This combination of high maintenance costs and poor scalability makes RPA an expensive long-term commitment with diminishing returns.

Intelligent Automation represents the next generation of automation by leveraging technologies like Generative AI to address the shortcomings of legacy RPA solutions. Unlike RPA, Intelligent Automation can process both structured and unstructured data, enabling it to handle more complex, end-to-end processes. It also significantly reduces dependence on manual IT and technical resources by empowering business users to build, check, and verify their own automations through Natural Language Processing. This democratization of automation keeps IT governance and controls in place while dramatically lowering maintenance costs.

Intelligent Automation enables a wide range of use cases that traditional RPA cannot support, including Optical Character Recognition (OCR) and Intelligent Document Processing for extracting data from non-templated documents. One of the most significant capabilities is exception handling, which is a major pain point for existing RPA users since RPA simply throws errors when something unexpected occurs. Intelligent Automation can also process insights from unstructured data, giving organizations access to valuable business intelligence that RPA users miss entirely. These capabilities make it suitable for complex workflows in industries like procurement, logistics, healthcare, banking, and manufacturing.

Organizations should assess the volume of unstructured data in their processes, since this is the primary driver of RPA's failure to scale. They should evaluate the total cost of ownership of their current RPA deployments, including the services and maintenance burden cited by Forrester at five dollars per one dollar of tooling spend. Key capabilities to look for in an AI-powered replacement include Natural Language Processing to enable non-technical users, native exception handling to reduce developer dependency, and the ability to process diverse document types without rigid templates. Finally, organizations should consider whether the new platform maintains IT governance and audit controls while expanding automation coverage to complex end-to-end processes.