Robotic Process Automation (RPA) has its roots in the early 1990s but only gained prominence after 2000. Over the next 20 years, RPA has been shaped into what we know it as today.
Macros, scripts, workflow automation, and screen scraping tools, each designed for specific platforms or applications, were predecessors to RPA. The demand for end-to-end process automation with minimal manual intervention drove the development of RPA technology, enabling seamless automation across various platforms and applications, facilitating data transfer, and generating reports for human validation. RPA, when used in tandem with more advanced technologies, has evolved into Intelligent Automation (IA).
My stint with RPA has been a rewarding journey, and here’s everything I’ve learned over the years that has helped me position Mindfields as a leading Intelligent Automation Services Provider. Continue scrolling!
Evolution of RPA into IA
Initially, automation tools lacked user-friendliness and required coding expertise for implementation. Additionally, it had limited application compatibility, presenting challenges for automation.
Fast forward to 2024, RPA tools have significantly matured. Today, it's possible to achieve automation using multiple methodologies, such as surface automation, object, or OCR-based. Additionally, capabilities like AI, Cognitive Automation, Customer Service automation (Chatbots), Machine Vision, Intelligent Document Processing, Natural Language Processing, the empowerment of Citizen developers, Cloud Computing, and Advanced Analytics have been introduced to augment RPA. The synergy of RPA with these technologies contributes to more intelligent RPA solutions, known as Intelligent Automation.
Navigating the Evolution
I was first introduced to RPA testing with the Assist Edge tool, a product by EdgeVerve. Back in 2014, the tool included a provision to create a user interface that was presented to an end-user (Attended bots). Users could interact with the UI and trigger the underlying automation workflow. Initially, the entire automation piece was accomplished using custom coding and was invoked as libraries inside the tool. Subsequently, seamless RPA solutions (Unattended bots) were developed similarly using custom code. This was when people started recognizing RPA technology as a process automation tool rather than just a tool for automated software testing.
Following this, in 2015, the drag-and-drop interface was introduced across different RPA platforms, becoming the new norm for the design interface across multiple RPA tools. Even citizen users began using these tools. Parallel to this, I was introduced to UiPath in 2016, which featured a design interface similar to Assist Edge's. It quickly rose as one of the best RPA players, along with Automation Anywhere, Blue Prism, and PegaSystems.
This was also the period when the low-code/no-code approach to automation began gaining traction. During this time, we conducted multiple POCs, implementations, and POVs involving clients across domains like banking, healthcare, logistics, supply chain, retail, manufacturing, etc., and a wide variety of applications like web-based applications, SAP, thick client applications, BPM systems, Excel, Outlook, Mainframes, etc. This diversity shows how much the RPA tools had matured by then, capable of handling various applications with a low code/no code approach.
Subsequently, in 2021, I began my role as a Senior Developer at Philips GBS, where we automated internal processes using UiPath as the RPA tool. This experience highlighted the real-world benefits of RPA, as I had the opportunity to work closely with the stakeholders. I observed the reduction in manual work, increased processing speed, accuracy, and cost-effectiveness of RPA technology.
Later, I embarked on my journey with Mindfields in 2023, where I continued to work with UiPath and explored other tools like Automation Anywhere and Power Automate. During this time, I delved into Document Understanding, Testing Suite, Communication Mining, and IDP features of RPA, as well as Generative AI use cases. I also explored its integration with AI models and other LLMs.
By then, RPA tools had become complete suites themselves. For example, UiPath offers a Studio for development, an AI hub for hosting DU/ML models, a Testing Suite for testing, a Dashboard for business users, Communication Mining, Document Understanding, Apps, and human-in-the-loop concepts, all as part of a single platform.
This marked the phase when RPA evolved into its next form: Intelligent Automation (IA), also known as Hyper-automation. Intelligent Automation combines traditional automation with an added layer of intelligence. Cutting-edge technologies like AI, ML, IDP, and NLP are used to augment traditional RPA, effectively adding a brain to RPA bots. This evolution brings bots closer to human capabilities.
IA also enhances the capabilities of bots by introducing context-aware automation. It enables a bot to understand the context, learn from data, and adapt in the future, handle unstructured data, recognize emotions, and understand processes. These capabilities further enable bots to mimic humans more effectively.
Evolution of Automation Life Cycle Management
While we have witnessed the evolution of automation tools over time, other key factors involved in the automation lifecycle have also evolved. This evolution can be effectively divided into two key phases: pre-development and post-development.
Pre-Development
"The well-begun is half done," holds true for RPA projects as well. The initial phase of RPA projects can be challenging because RPA use cases follow a pattern different from conventional development projects. The requirement gathering, designing, and documenting of the use case are crucial for a stable RPA solution, ensuring that the solution is stable, flexible, and easily maintainable in the long term.
During the early days of RPA, gaps in requirement gathering, especially in exception handling, were prevalent. Analysts approached RPA projects similarly to conventional development projects, focusing on the ideal workflow and relegating exception handling to the developers. Numerous assumptions were made regarding data source, transformation, and handling, without keen exploration of alternatives for achieving the same result more efficiently. These practices led to gaps in requirement gathering, resulting in increased development time, failures post-deployment, and the rollout of incremental project versions, among other issues.
Over time, individuals involved in the RPA lifecycle began to recognize and understand these gaps. They learned from past experiences, stakeholders clarified their requirements, business analysts posed the right questions, and developers focused on proper designs and implementations.
The discovery process for identifying automatable processes within an organization can be facilitated using Intelligent Automation tools like Task Mining and Process Mining. These tools operate in the background, recording the steps, applications, and event flow on a machine, subsequently generating insightful charts that highlight processes suitable for automation.
Post-Development
It was commonly assumed that an RPA model would operate on autopilot indefinitely, without the need for further manual intervention post-deployment. However, the disconnect between the tool capabilities and predevelopment activities led to numerous post-deployment failures, necessitating costly redevelopment and maintenance efforts.
With the integration of experience and enhanced intelligence in tools concerning Intelligent Automation, many of these gaps have been addressed, resulting in more robust solutions. Many tools have become adept at accommodating application-level changes and functions seamlessly. While notable improvements have been made, situations requiring human intervention, particularly in handling exceptions, remain to ensure uninterrupted bot operation.
Choosing the Right Automation Tool
As of 2024, Intelligent Automation (IA) has transcended its status as a niche concept to become a globally recognized term. Numerous organizations have embraced IA to automate routine tasks, thereby shifting their focus towards creative, strategic, and customer-facing functions, enhancing overall value and satisfaction. Leading this innovative wave are organizations like SAP, ServiceNow, and Microsoft.
The diverse needs of an organization pose a challenge in selecting the appropriate automation tool, whether it be native automation tools, dedicated tools like UiPath, or a combination thereof. Here are key considerations:
- Cost
Automation seeks to reduce costs by minimizing manual process involvement, obviating the need for hiring specialized personnel. Organizations should select tools based on their specific needs. For processes predominantly using a native app with automation capabilities, leveraging that app for automation is preferable to avoid the costs associated with acquiring new tools. Conversely, for organizations with a broad spectrum of applications requiring automation, opting for a dedicated tool is advisable.
- Ease of Use
Organizations must delineate responsibility for developing and maintaining automated processes. Native app automation may necessitate a technically skilled team, particularly for interfacing with other applications. In contrast, dedicated tools offer a more user-friendly environment supported by a vibrant community, influencing the choice of the right tool for the organization.
- Technical functionalities
When selecting a tool, organizations should assess technical features such as screen scraping, scalability, and cognitive capabilities. Security is paramount, especially for handling sensitive data. Selecting a tool with adequate security measures is critical to thwart external attacks, data misuse, and privacy breaches. Advanced requirements may warrant the selection of a dedicated RPA tool over native providers due to its superior capabilities.
- Exception Handling
Exception handling is a critical component of Intelligent Automation. Select a tool that enables swift error detection and effective management. Errors requiring expert intervention should be promptly addressed, while others should be automatically resolved. For processes prone to errors, choosing a tool with robust exception handling capabilities is recommended, as dedicated tools typically offer advanced features, thereby reducing development effort.
- Automation Platform Ecosystem
Several automation platforms offer a complete suite of tools, not just User Interface-specific automation tools. For example, add-ons such as Process Mining, Task Mining, OCR, Conversational AI, Test Suites, Monitoring tools, and Visualization tools are built into the platform itself, rather than relying on third-party applications. It is crucial for an organization to understand its needs regarding automation. If they anticipate that their automation requirements will extend into the areas mentioned above, it would be ideal for them to select a comprehensive automation tool.
- Processing Speed
Minimizing processing time is a critical goal of automation. If a solution predominantly utilizes a native application, native automation proves more efficient than external tools. Utilizing APIs, where available, enhances process robustness, speed, and reduces dependency on UI automation. If an organization's automation requirements are primarily centered on native applications, native automation is preferable; otherwise, external tools may offer a better solution.
Embracing the appropriate intelligent automation services is imperative for streamlining operations and boosting productivity.
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Topic: Blog
