RPA is a trending technology that helps businesses achieve more efficient processes. According to Gartner, the RPA market grew 63% in 2018 and expected to reach from $0.8 billion to $1.3 billion in 2019. 

While it is applicable to many business processes from finance to human resources, it is critical to identify the most suitable processes for automation to maximize RPA benefits. For that, businesses leverage other technologies, like process mining and data extraction, along with the necessary skills for RPA deployment like strategic planning, process understanding as well as programming skills.

What is RPA?

Robotic Process Automation (RPA) is a growing automation technology to perform high-volume, rule-based, and repetitive processes faster and with fewer errors. As most of these RPA tasks include form-filling, copy-pasting, and systems-integrating, RPA bots allow humans to focus on higher value-adding tasks. Below are some examples:

  • Invoice processing: Automated extraction of data from invoices, validation and recording them
  • Loan processing: Automated document extraction from related documents and automated loan decisions
  • CV screening: Automated CV assessment of candidates and interviewing using bots

For more RPA applications, feel free to visit our in-depth RPA use cases guide

As a different perspective, Wikipedia defines RPA as follows:

Robotic process automation (or RPA) is a form of business process automation technology based on metaphorical software robots (bots) or artificial intelligence (AI) / digital workers

To learn more, you can read more about this in our related article.

Which processes can be integrated to RPA technologies?

It is critical to choose the most suitable processes for automation to maximize the benefits of RPA. Failure to do that might result in lower ROI since a high level of automation may not be achieved with RPA in processes that are not good candidates to be automated. Here are some characteristics of the best processes for automation to maximize the impact of RPA: 

  • Impacting both cost and revenues
  • High volume tasks
  • Fault-tolerant
  • Error-prone
  • Speed-sensitive
  • Requiring irregular labor

Choosing processes that are easier and faster to automate with RPA can save costs and provide a more straightforward implementation. Those processes tend to be:

  • Rules-based
  • With fewer exceptions
  • Company-specific
  • Stable
  • Not on the roadmap for new systems

Automation of processes that fits the above characteristics will provide the maximum impact for your business and an easier RPA implementation process. You can read more about these characteristics in the related part of our RPA guide

How to implement RPA technology?

Here is a short step-by-step explanation of how to implement RPA in your business.

  1. Understand your current performance and how you handle your as-is processes
  2. Identify processes that are most suitable for automation
  3. Convince your organization
  4. Choose your RPA tool
  5. Decide whether to outsource RPA development
  6. Develop your solution
  7. Test your solution
  8. Run a pilot
  9. Go live
  10. Maintain the RPA installation

You can read more details on this in our RPA implementation guide.

Which technologies to use with RPA?

AI & Machine Learning

The main limitation of RPA is the lack of cognitive ability. RPA tools cannot learn from their previous experience or perform tasks that require judgment. The technology can continuously handle repetitive processes in a predefined way. To overcome this limitation, introducing machine learning algorithms or other forms of AI can be a powerful solution.

With RPAAI, also called cognitive automation or intelligent RPA (IPA), is RPA powered by AI technologies. This is an emerging technology and these RPA bots aim to manage tasks requiring judgment without manual intervention. These bots can understand specific patterns within tasks and automate them according to those patterns.

Process Mining

Process mining technology enables businesses to fully understand the actual conditions of processes. It uses real-time performance measures and event logs to identify bottlenecks and present factual insights. By using process mining technology, businesses can:

  • Prioritize the best processes for automation
  • Implement RPA initiatives faster, as the business logic of processes are well perceived
  • Monitor RPA bots’ performance and measure their benefits

Considering the increasing amount of data, today’s processes become more complicated and harder to fully understand. While most of the data remain unused, process mining technology can leverage this unused data to give better insights about actual conditions of processes. If you want to read more, feel free to read our RPA deployment with process mining guide.

Data Extraction

Data extraction technologies help businesses convert unstructured or semi-structured data into structured data. While manually extracting data from different sources can be tiring and prone to errors, RPA can automate this process, save costs, and allow employees to work on higher value-adding activities. Hypatos shares that they can save up to 90% of document processing costs by using RPA and data extraction technologies. Most common examples of using RPA with data extraction technologies are:

  • Invoice processing
  • Accounting
  • VAT compliance

You can learn more about data extraction technologies in our in-depth guide.

What are the relevant skills to build your RPA solution?

For understanding how to deploy new RPA projects for companies, RPA developers need to know how RPA technology works and have specific skills to build successful RPA solutions. We can group these skill sets into two main categories:

Technical Skills

Understanding of programming languages/frameworks

A certain level of programming knowledge is necessary to develop new RPA bots. It is essential to build custom RPA solutions for businesses because:

  • They are required for complex processes
  • RPA marketplaces allow developers and companies to monetize their custom-built RPA solutions

Although RPA developers can use any language of their choice to build their solutions, this would be a more time-consuming way than leveraging other RPA vendors’ frameworks. Today, most vendors use .Net Framework to build RPA solutions. 

However, some vendors offer Java or Python options, as well. Python is the 3rd most popular programming language on Github and is among the fastest-growing languages in terms of popularity. Thus, Python would be a more sustainable option if you want to improve programming skills in understanding a programming language.

For companies that don’t have employees with a high level of programming knowledge, there are also low-code RPA solutions, like Argos Labs, emerging in the market. These low-code solutions require less knowledge of programming languages and help companies build customized RPA solutions thanks to their user-friendly interface.

Debugging skills

Besides developing a new RPA solution, it is also vital to monitor its performance and correct any possible errors. RPA developers need to test and fix bugs before taking RPA solutions live. By debugging RPA bots quickly, companies can save time and be less stressful as any error can be fixed promptly.

Analytical skills

RPA developers/analysts should analyze processes and make improvement recommendations to enhance the process workflow. They need to identify the correct processes to automate, map an effective workflow, and set correct project goals, which will make RPA implementation easier. Choosing the wrong process to automate might have undesired results, causing additional costs and more time to reorganize the RPA initiative. Deloitte and EY shares that nearly half of RPA projects fail because they don’t have full insights before implementation.

To avoid such problems, process mining is an effective solution that can help RPA developers. Thanks to this technology, companies can have an accurate picture of their as-is processes and identify the most suitable processes for RPA implementation. Process mining can detect problems in automation initiatives.

For example, Piraeus Bank tried to automate their processes without analyzing their processes. However, the company started to receive complaints from both its customers and branches. They also couldn’t identify the problematic part of their automation project because they didn’t have full insights. The company later implemented QPR’s process mining software to identify bottlenecks, discrepancies, and their causes. In the end, the company accelerated its loan application process from 35 minutes to 5 minutes.

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