Article

Intelligent Automation – Future of RPA

  • Sudhir Gundala
  • |
  • Founder & CTO, Cognine Technologies

Adoption of emerging technologies across industries is rising at a break neck speed. Besides digital transformation, organizations are pushing into digital optimization initiatives like Machine Learning, AI and automation to become more competitive, resilient and efficient.

Robotic Process Automation (RPA) has been one of the most successful and widely adopted automation tools. According to the latest forecast from Gartner, Global RPA software revenue is projected to reach $1.89 billion in 2021, an increase of 19.5% from 2020.

Intelligent Automation – Future of RPA
Over the rest of the article, we will focus on how we help enterprises with:

1. Facilitating RPA implementation

Over time, having worked with various clients across the industries, one of the most important imperatives for successful RPA implementations has been management buy in.

Getting started with RPA

In this step, you will lay the foundation needed for successful RPA implementation. At the end of this phase, you would have a completed a pilot to show the benefits of implementation.

Opportunity discovery

We work closely with your teams to identify gaps, savings potential and ROI as compared to the peers and industry benchmarks. This includes data collation, workshops with your team and value stream mapping. Study data collated to confirm on the opportunities identified.

Platform selection

Once you have identified the opportunities for automation, the next step is to pilot the process. Having worked with leading automation software providers across the ecosystem, we help you identify the right tool for automating the identified opportunities. This includes considerations ranging from no code/low code platforms to cutting edge automation using computer vision, NLP and AI.

POC execution

Build automations, technical flows quickly for the identified automation opportunities. Collect the results, evaluate the feedback and build score cards to measure long term success and focus on creating an opportunity pipeline.

Scaling across the enterprise

To scale off on the back of successful pilots, organisations need a team responsible for opportunity pipeline creation, automation governance, process assessment and enterprise-wide support. This team ensures efficient usage of RPA resources, increased integration/access to new technologies with in the enterprise and increase in the throughput capacity.

We work with you to build teams/capabilities to support continuous improvement, identify cross enterprise opportunities, help you reengineer the processes and track the results after deployment, to ensure you realize the full potential of your automation effort.

2. Challenges and strategic navigation

While RPA adoption has been gaining significant attention in some industries, there have been a plenty of failure stories too, exceeding the implementation time, cost and overall ROI. According to Gartner, “By 2021, 50% of RPA implementations will fail to deliver a sustainable ROI.”

Below are some of the most common challenges you will likely face if you and your company choose to implement RPA.

a. Process Issues

It is recommended that you map your automation journey, identify gaps across various departments and saving potential before you set out on your automation journey. While most enterprise’s successfully implement pilots, they lack a clear opportunity pipeline to scale the efforts.

Tasks that are repetitive, rules-based, high volume, and that do not require human judgement are the ideal candidates for automation using RPA. This can include activities involving moving files/folders, copy & paste data, scrape data from web, connect to the API’s, extract and process structured and semi-structured content from documents, PDFs, emails and forms. RPA implementation might be difficult with the process that are non-standardized and require significant human intervention.

Redefining business process for efficient use of bot’s time or modifying the business process itself might speed up implementation. For example, it might prove to be efficient, if you could get all the data first, feed it into the application and then call the next flow instead of calling the next flow after every single data entry point.

It is easier to reach automation levels off 70-80% for most applications and the remaining 20% might require significant investment of time and cost due to the complexity, throwing out the whole purpose of automation. Hence it is crucial to make a cutoff between desirable level of automation versus efficient level.

b. Organizational pitfalls

From getting the management buy-in, it is important to rally support from IT department to successfully execute RPA projects. IT department plays a crucial role in speeding the RPA implementations with resource allocation, exposing API’s or even building certain custom scripts over components. Some of the other IT support functions that play a key role include RDP access, network stability, bot run context and issue resolution time.

c. Technical Issues

It is advisable to choose low code/no code RPA solution over some the outdated solutions available in the market. It is easier for your internal teams to adopt or transition later, should you work with an outsourced service provider to develop the initial components. It also helps you keep the development costs under control.

Some of the other best practices include:
  • Initialising certain applications before hand
  • Implementing best practices like modularity, re-usability and efficient looping into the code
  • Securing credentials using orchestrator

d. Post implementation adoption

Scalability, Maintenance and de-commissioning the process are the three most important post implementation challenges. We have covered scalability in the earlier part of the article (RPA centre of excellence is most important to address scalability). Changes in business processes or applications require the components to be modified. Since most the bots are programmed using best practices it is relatively easier to re-configure and change as per changing business needs. As the process evolves over a period of time with the changing business needs, we should also be able to analyse when to de-commission a certain process based on the complexity and effort it takes to maintain and bot’s run time.

3. Future of RPA

RPA market is expected to grow at double digit rates through 2024 as per predictions from Gartner. Here are the trends that are expected to shape up the RPA market in the short term.

Non-IT Buyers/Low Code and No code Platforms

The RPA adoption is over 90% in certain industries and most of these revenues has been coming from the IT buyers. Over the course of the time, business buyers are expected to drive the revenue growth for RPA players given the complex business landscapes and for simple reason there aren’t enough programmers around the world to meet these demands. By 2024, half the new RPA revenue is expected from non-IT buyers.

While the existing pure play RPA leaders like Ui Path, Blue Prism & Automation Anywhere are working on simplifying their platforms, some of the tech giants like SAP, Salesforce, Oracle, ServiceNow, Google and AWS are focusing on low code RPA platforms. There also has been some of the innovative starts ups that are already making some big success stories in the no code RPA space.

Cognitive Automation

Leveraging NLP, AI and ML with RPA enables enterprises to expand the scope of process it can automate. While some the tools provide these features by default, some of them may require custom coding or installing certain plugins from the RPA market place.

Process Modelling Automation

One of the top priorities for RPA research is auto-extraction of process knowledge from logs and videos. The workflow creation and process definition acts as a bottleneck in the creation of opportunity pipeline and is manually intensive. Automating the process modelling can speed up RPA implementation and deliver substantial ROI.