“Our intelligence is what makes us human, and AI is an extension of that quality.” – Yann LeCun, Professor, New York University
The above quote makes us think that robots, chatbots, artificial intelligence, machine learning are extensions of ourselves. Various industries are leveraging these qualities to solve problems, improve conditions, and save lives.
Robotic Process Automation is one such quality seen and applied in the industry today to automate and improve the efficiency of several activities that emulate human interactions, which are repetitive and time-consuming. With the help of RPA, companies, teams, individuals can focus on more critical tasks, which require our intelligence.
1. What is RPA?
Figure 1.1 Visualizing RPA implementation in an insurance industry
A more detailed or complex definition would be the automation of specific business processes that are repetitive, labor-intensive, and involve at least some sort of elemental rule-based decision making.
Figure 1.1 provides a fair idea of business processes in an insurance company. All of these processes and tasks which are interdependent are candidates for well-orchestrated RPA implementation. Next, let us understand how RPA works.
2. How does RPA work?
Let us consider an example of repetitive workflow, that involves:
- Save PDF attachments from an inbox.
- The data in each PDF is then copied into an excel document, which gets saved on a machine.
- The data from each excel document is then copied over to a website or a GUI to generate invoice reports.
- Status report of invoices generated needs to be reported to the operations manager.
Figure 2.1 Converting several repetitive tasks to RPA workflow
The above workflow may take approximately fifteen minutes on an average for an individual to complete the first three tasks, and this does not involve the summary report yet. We also need to account for how many invoices get generated. Not to forget, there may be hundreds of PDF attachments in the mailbox and copy-pasting errors. This list of questions continues to grow.
Referring to the above repetitive tasks seems like a good use-case for implementing RPA workflow.
So how does the RPA work?
- It replicates human interaction (keyboard inputs, mouse clicks)
- It operates on User Interface Layer (Like GUIs)
- It reads applications
- It can be implemented on a desktop or a virtual environment
Figure 2.2 RPA implementation provides long term benefits and analytical capabilities too
Applying sound RPA design principles, testing, and deploying the above workflow can significantly reduce the time spent on each step, providing individuals more time for creative and intuitive tasks at hand.
3. Advantages & Limitations
- 24/7 Operations – Robots or software applications can work uninterrupted to complete a set of rule-based workflows or routines.
- Cost Reductions – May generally cost one-tenth of human employee capacity.
- Improved Efficiency – Three to fifteen times gain and more efficient workflows.
- Improved Quality and valuable work.
- Short Payback Period – Depending on the problem and RPA implementation approach, one can reap benefits anywhere within six to eight months after implementation.
- Logging – Every RPA step gets logged, leading to future scope for data analytics and data science.
- Internal Control and Traceability – Offers traceability and makes it available for analytical purposes.
- Not all tasks apply to RPA implementation.
- Inadequate processes or tasks that are unpredictable should not be automated.
- Any change in the process means updating RPA code.
- There may still be a need for human intervention.
- It still not at the expected level of intelligence to update a change in process and handle tasks without rules applied – Applying Artificial Intelligence and Machine Learning to RPA is a work in progress.
4. Piloting RPA in your enterprise
“The only limit to our realization of tomorrow will be our doubts of today.” — Franklin D. Roosevelt
Are you ready to implement an RPA solution at your company? If you are wondering where to start, the following guidelines may help you prepare better:
- Establish interest and an understanding of RPA within in your organization or company
- Manage Expectations – Get to the technology and opportunities of RPA
- Involve IT – Requires collaboration and coordinated efforts with the tech gurus
- Choose the right vendor – Select a vendor that meets your requirements
- Use the following five-step approach for piloting and implementing RPA as shown in Figure 4.1
Figure 4.1 Five Step Basic Plan for Implementing RPA
5. RPA in Data Science and Analytics
From being simple rule engines, which make decisions based on a complex set of interlinked decision trees, RPA implementations are evolving.
Authentic ML or AI capability in RPA implementation is currently developed and tested by leading RPA vendors and may soon be the future.
RPA has paved the way for efficient and robust workflow while capturing a significant amount of data generated by logging during implementation. RPA can create clean, digitize, and structured or semi-structured data. This expanse of data serves as the source for data science and analytics.
Figure 5.1 The future state – RPA and Data Science become complimentary and drive business decisions
Robotic Process Automation is the future state of work automation, where remote work may be the new black. Being a low-cost initiative may spark more demand for implementations and evolving innovation in the field of RPA. With AI and ML capabilities currently explored by RPA vendors, we may be void of repetitive work in the future.
RPA cannot displace humans or our jobs. It will help us focus and spend our creative energies on transforming teams, business units, companies, organizations, and society. An approach worth considering!
While our consultants are not robots, we learn from previous projects and engagements, which helps us solve today’s problems better. Syntelli will work with you to help you understand and implement a successful RPA solution.
Vishwas Subramanian, Sr Analytics Associate
Vishwas provides solutions to big data problems like real-time streaming data, Traditional SQL vs NoSQL, Hadoop or Spark, Amazon Cloud Services (AWS) vs Personal Cluster. His focus is on analyzing and providing optimum solutions for business use cases.
Vishwas received an M.S. in Electrical Engineering from the University of North Carolina at Charlotte. His research interests are Spark development, Visual Analytics, Android Devices, Machine Learning.
When Vishwas is not providing incredible big data solutions to our clients, you can find him hiking, playing soccer and travelling.
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