A Comprehensive Guide About How to Implement Robotic Process Automation in Your Business

Robotic process automation (RPA), artificial intelligence (AI), and machine learning (ML) are all rapidly emerging technologies that are changing the way businesses operate. Automation is the ideal solution for businesses facing exponential growth to meet increased demands. Fortunately, countless software tools available can lead to the inevitable automation of entire business processes.

In this article, we’ll dive into RPA, AI, and ML details and explain how each technology can be combined to create a more automated and efficient workflow. We’ll also discuss the benefits of integrating AI and ML into RPA and how businesses can use this technology to save time and improve their operations. Finally, we’ll explore the best RPA tools with AI and ML.

With robotic process automation (RPA), businesses can streamline their operations and allow employees to focus on higher-value tasks. RPA software allows organizations to build and implement robots that imitate human behavior and actions. These robots can detect and process information, extract data, and take the appropriate steps to navigate systems.

RPA technology has many uses, from processing online transactions to performing security audits. By automating repetitive processes, companies can reduce their operating costs and focus on improving the efficiency of their operations.

Ever wished there was software that could handle all those repetitive tasks on your to-do list? Well, there is robotic process automation (RPA). Learn more about RPA and RPA developers in our article.

To build and manage an enterprise-wide RPA program, you need a technology that can do much more than just help you automate a single process. You need a platform to create and manage a new enterprise-wide capability, enabling you to become a fully automated enterprise. The RPA technology should provide end-to-end support—from discovering automation opportunities to quickly building robots to managing thousands of automated workflows.

Machine learning (ML) is a form of artificial intelligence (AI) that enables software applications to predict outcomes more accurately without being explicitly programmed to do so. Through a process of analyzing data, ML creates a model of typical workflow patterns and improves its algorithm based on common relationships and historical data. Essentially, it mimics the behavior of humans and thereby allows for more efficient and accurate problem-solving.

ML technology is important because it can learn patterns, recognize them, make predictions, and provide insights. It can also be used to automate tasks, detect anomalies, and optimize decisions.

Additionally, it can be used to create visualizations and interactive dashboards for data exploration and analysis.

Artificial intelligence (AI) is a rapidly-growing field of technology. It consists of tools to help machines grasp how humans think and make decisions. With these tools, machines can complete tasks in place of humans, freeing up their time for more important matters. AI is often used in automation software, allowing machines to check routine tasks off a to-do list quickly and efficiently.

Machines can mimic human functions such as learning and problem-solving using AI. ML, Natural Language Processing (NLP), and many other disciplines are part of AI. Algorithms designed for AI to make decisions using real-time data.

Natural Language Processing (NLP) is the “ability that allows machines to understand and interpret human language the way it is written or spoken”.

AI technology can provide many features and capabilities, including natural language processing, machine learning, computer vision, deep learning, robotics, and more. AI can be used to automate processes, make decisions, and solve complex problems. AI is also becoming increasingly important in the fields of healthcare, finance, marketing, and education.

Now that we understand these tools, we’ll look at the similarities and differences between RPA vs. ML vs. AI.

While these tools sound very similar, key differences between RPA, ML, and AI make investing in all three worthwhile.

Let's start with the obvious: how they are similar. These three software tools are used to automate routine tasks and speed up business processes by mimicking human actions.

An organization can achieve intelligent process automation by combining these tools. Automation allows businesses to progress toward digital transformation, relying heavily on technology to complete tasks, finish projects, and satisfy customers.

Furthermore, AI and ML are closely intertwined. ML is actually a subset of artificial intelligence. AI uses machine learning to understand the relationships between tasks and mimic those tasks independently.  

Despite their importance in automating repetitive tasks, each tool serves a different purpose. RPA differs from ML and AI in the following ways:

• Rule-based vs. data-based. Data drives both machine learning and AI. RPA, however, requires human input of rules to function correctly.
• Programming. Since RPA is based on the rules, a person needs to program directly by determining the behavior of the system. In AI/ML, the rules are displayed from many examples viewed by the machine, and many types of direct programming are lost. 
• Process improvement. If your RPA software doesn't work the way your team wants, you'll need to change the rules and give it time to adapt manually. With AI and machine learning, algorithms are automatically updated as new data is discovered to meet your business needs better.

Each tool can perform different tasks using different methods. That's why it's essential to integrate all three into your business if you want to achieve hyper-automation.

Using RPA, ML, and AI together can help automate processes, improve decision-making, and provide better customer service. RPA can automate manual, repetitive tasks. ML and AI can be used to improve the accuracy of decision-making and provide insights into customer behavior. Together, these technologies can help companies increase efficiency, reduce costs, and improve customer service.

RPA and AI can work together to form an all-encompassing platform for intelligent automation, allowing businesses to automate front- and back-office processes and coordinate work among human and bot teams. This powerful combination of technologies can save businesses time and money by reducing human errors, improving data management, and providing tailored dashboards and reporting capabilities. 

RPA can automate more than 70% of enterprise processes that are manual and repetitive, according to some experts.  There is also the possibility of automating 15–20% of processes that require human judgment.

With AI and RPA now easily accessible to business users, automation is no longer limited to programmers. Businesses can now use intelligent automation to skyrocket the productivity of their workforce, free them from tedious and repetitive tasks, and ensure accurate results.

Let us look at a couple of examples:

Use case 1: Automated document management can help simplify the complex task of organizing documents, reducing time, improving the quality of manual classification processes, and producing more consistent results quickly and at a lower cost.

Automated document management, for instance, can become increasingly efficient with NLP-powered RPA. By processing more documents, the solution becomes more adept at managing variations.

A document management system can also detect new documents beyond classification. It is possible, for example, for an invoice to recognize the vendor's name, which triggers an account payable action without the need for the accountant to intervene. The process incorporates AI technologies such as optical character recognition (OCR), neural networks (a subset of machine learning), and natural language processing (NLP).

Use case 2: Unstructured legal documents (contracts, sentences, agreements, etc.) often pose a challenge to law firms and corporate legal departments. To help them better understand and analyze these documents, they can benefit from specialized tools that enable them to extract complex data such as parties in a contract, the terms of a particular clause, those affected by a legal procedure, and how they are affected. This will allow them to find connections and gain deeper insights.

RPA software can be used to automatically extract the contents of contracts that are sent as attachments to law firms. This data can then be passed to an NLP tool. This tool will extract complex data, such as the names of parties in the contract, the terms of clauses, those affected by legal procedures, and how they are affected. Also, the NLP will be able to detect and identify the title, parties, date, term, assignment, change of control, audit, governing law, force majeure, indemnification, and limitation of liability clauses.

ML can also be used to improve business processes in the following ways:

• Customer Service Automation. Businesses struggle to manage the growing number of online client interactions. There aren't enough customer service reps to handle calls, causing delays. Customers are put on hold or forced to repeat their requests if transferred for escalated support. A digital assistant and RPA help representatives process customer requests more efficiently by gathering and displaying customer information on their screens so they can focus on the customer conversation.
• Cybersecurity. Cybersecurity experts are working hard to stay ahead of growing security threats as networks become more complex. The rise of the Internet of Things (IoT) has changed the cybersecurity landscape, providing more pathways for malicious actors to target an enterprise. Machine learning algorithms have enabled cybersecurity operations to keep up with rapid developments, allowing them to observe user activity within the network for any abnormalities or security flaws.
• Process Analysis. A machine learning approach analyzes process data, such as user activity and system logs, automatically identifying repetitive patterns and opportunities for automation and optimization.

RPA, ML, and AI will expand their applications and use cases as technology advances. By doing so, businesses can increase productivity, reduce costs, enhance user experiences, and drive growth and development. The opportunities presented by these technologies are set to expand in the coming years as technology advances.

Below is an exclusive comparison of the top best robotic process automation tools.

UiPath is an advanced and extensible Robotic Process Automation (RPA) tool that enables organizations to design and deploy robotic workforces for their operations. It is an open-source RPA tool that allows global enterprises to automate their desktop and web applications.

Features:

• Uipath can be hosted in virtual terminals, cloud environments, and other platforms.
• Offers support for a wide range of applications, including web and desktop applications. 
• Auto-login features so that bots can run without manual intervention. 
• Scraping solution that works with .Net, Java, Flash, PDF, Legacy, and SAP, with great accuracy.

Pros:

• High-speed execution.
• Platform independence.

Cons:

• You should have programming skills.
• High price.

Pricing: $ 15000 to $ 18000 annually.

Blue Prism Robotic Process Automation (RPA) is a powerful, user-friendly tool for medium to large organizations. It is designed to provide all the core capabilities needed to automate processes and tasks, regardless of the platform or application used. Developers will benefit from its programming skills requirement, making it easy to use.

Features:

• It supports a multi-environment deployment model.
• Security provided for network and software credentials.

Pros:

• High-speed execution.
• Platform independence.

Cons:

• You should have programming skills.
• High price.

Pricing: $ 15000 to $ 18000 annually.

Pega is an ideal Business Process Management tool for medium and large businesses. It works on Windows, Linux, and Mac and provides cloud-based solutions and services. It is available for use on desktop servers.

Features:

• It will help you deploy your solutions to the customers.
• It provides a cloud-based solution.
• It doesn’t store any execution data in a database. Rather, everything gets stored in the memory.
• With this tool, you can distribute the work to the desktop, server, and employees.

Pros:

• Because of the event-driven approach, it works faster.
• It is a robust and reliable tool.

Cons:

• There is no on-premise solution.

Pricing: Starts at $200 per month.Contact them to learn more. 

Rapise by Inflectra is a powerful test automation system designed to accommodate complex applications such as MS Dynamics, Salesforce, and SAP. Rapise offers support for hybrid business scenarios in its seventh version and allows automation of Web, Desktop, and Mobile applications.

Testers and engineers can use Rapise to interact with the user interface of the applications they are testing, replicating user actions to achieve their goals. Rapise is designed to be user-friendly and accessible to both developers and non-developers and is available as an on-premise solution.

Features:

• Supports automation projects of any size.
• A unique code-less methodology called Rapise Visual Language (RVL) for ease of recording and maintaining automation processes.
• REST, SOAP calls, and email processing (Gmail, Office 365, private mail servers).
• Provides an open platform for enhancements and integration.

Pros:

• Non-developer friendly
• Backed up with training and certifications
• Fast execution

Cons:

• Windows-only platform

Pricing: $4,999 / Single developer license, unlimited execution agents. 

Eggplant Software by Keysight provides Robotic Process Automation Solutions to increase productivity and reduce errors. It is equipped with a universal fusion engine that enables the system to test any type of system, from mobile to mainframe, and can be hosted on Windows, Mac, and Linux. This automation solution can interact with various systems to complete the task, making it a comprehensive end-to-end automation solution.

Features:

• Eggplant has data-driven automation features that enable the linking of data sources and executing a task for each record.
• Eggplant Functional has capabilities for automating any front-office and back-office applications.
• Eggplant DAT has features for working with any data repository and scraping data directly from the screen.
• It has extensive verification and validation functionalities that validate the execution of the process.

Pros:

• Eggplant Robotic Process Automation is a solution designed for process experts.
• It supports automated and manual processes or a combination of both.
• It can be deployed in unattended and attended modes.
• It is compatible with common packaged apps such as SAP, Oracle, etc.

Cons:

• No such cons to mention.

Pricing: Contact them to learn more about their pricing details.

So, we found out what tools are presented in the market and their features. Let's talk now about how to implement them in your business.

1. List out processes to automate. Businesses should develop a strategy to select the processes suitable for RPA and prioritize them based on metrics such as complexity and ROI. Consider what automating these processes will look like, their purpose, business context, and how it fits into future business operations or the overall automation journey.

2. Perform a feasibility assessment. To evaluate how much the process can be automated, it is necessary to carry out a two-step feasibility assessment. This two-step process is where process examination and technical feasibility are carried out—by an SME (Subject Matter Expert) and an RPA expert.

3. Readjust. In the feasibility assessment report, look for processes that are not structured, standardized, optimized, recorded, or not executed as planned. Then, attempt to re-optimize and restructure those processes.

4. Gather user stories. A user story is a detailed description of the features of an application from an end-user perspective. It should include a comprehensive explanation of user requirements and a thorough description of each process that needs to be automated. Based on this information, a process definition document should be created that outlines the RPA workflows for development.

5. Start the development process. Start with creating RPA workflows. Developers then use RPA tools, such as UiPath, Blue Prism, etc., to create automated scripts and program code. It is important to select a tool most suitable for business needs, as each RPA tool has its capabilities.

6. Test the RPA process. You must test the process in various scenarios to understand performance and identify potential issues and bugs thoroughly. If any issues or bugs are detected, they should be reported to the development team for resolution.

7. Reconfirm and deploy. The development teams have corrected the initial tests and errors. Confirm that the results are perfect, then deploy the full RPA solution.

Robotic Process Automation (RPA), Artificial Intelligence (AI), and Machine Learning (ML) are all rapidly emerging technologies that can be used to automate and improve business operations. RPA builds robots that mimic human behavior, while AI and ML are used to understand data and make predictions. Features important in RPA technology include end-to-end support and the ability to manage a new enterprise-wide capability. ML features important for businesses include detecting patterns and anomalies, automating tasks, making predictions, and providing insights. AI features important for businesses include the ability to understand how humans think, make decisions, and complete tasks in place of humans.

And what about choosing the tools? It's important to consider several factors when evaluating RPA tools to ensure you select the best one for your use case. First, consider how the tool discovers and defines process steps and rules, as well as the impact of any manual steps on overall efficiency. Additionally, evaluate how easy it is to create bots using the tools and whether the low-code or no-code platforms are truly low-code or no-code. Furthermore, evaluate how well the prospective solution does bot deployment, management, and maintenance. Finally, ensure the tool is aligned with your use case and business goals.