Robotic Process Automation (RPA) is the technology where a computer software, also known as a robot in this case, can be configured to work like a human within a digital system. The robot executes repetitive business processes through a user interface, captures data and is manipulates applications, just like a human. Robots can comprehend and trigger responses where needed. They can communicate with related systems, and the margin of error they ensure is zero. The best part? They can work 24/7, 365 days of the year.
However, the application of RPA has to be efficient, relevant and appropriate. In the webinar titled ‘Identifying the Right Use Case for RPA in the Covid-19 Business Environment’, we talk to C-level executives to understand their strategy, and their role, in introducing RPA in their businesses.
So what is the importance of RPA in a post-pandemic world? The pandemic taught businesses that there are processes that need to be executed fast. With RPA technology, this capability can be facilitated at a quicker pace and at a better cost.
“We have had use cases where providing the right access to enable work-from-home (WFH) became critical and robotics was able to step up and do that” says Sehr Saghir, Managing Director, Enterprise Robotics Process Automation and RPA Practitioner. “Organizations will need to identify focus areas where robotics can play a critical role. It must be remembered though, that not every application can be replaced or integrated. Companies will need to evaluate their processes to see which can be helped with robotics and automation”.
How does a business then understand which processes are best suited to RPA since every business has scores of manual processes?
A business will have to assess which of these can be taken out to allow for the workforce to focus on knowledge-based processes. “The first example that comes to mind is purchase order processes,” says Satya N Jayadev, Vice President and CIO, Skyworks Solutions, Inc. “Analytics, inventory management and reporting, order fulfillment, regulatory and compliance and logistics are all places that RPA can be used. The power of RPA is like macros on steroids – it can bring multiple forces together and make them work together seamlessly”. Satya adds here that he wouldn’t recommend RPA be used in places where process evaluation has not taken place. Without understanding the need and intent of automation, implementation can fail, as it is dependent on structured data.
Implementing RPA – What You Should Know
The first step to consider is whether to opt for in-house or to outsource your RPA implementation. A combination of both would work effectively. The key is to understand how you can operationalize the several opportunities present, to turn something over to RPA. In this process, the role of the CIO is critical to helping an organization prep for automation. The role of IT in the introduction of new technology is that of a demystifier. It is to help the leadership understand the change RPA will bring about and then, through them, get the rest of the organization on board.
“You will need in-house support from your IT team when it comes to tech like RPA to evaluate your needs. You then engage the experts to create a solution. You don’t need to be immersed in the creation of the solution but, rather will need to understand the concept, hand it over to experts who can do it for you and move on to the next challenge that you, as a stakeholder in a business, will always have to address,” advices Satya.
The key problem areas to watch out for, before, during and post implementation can be difficult to pinpoint considering every use case is unique. However, some common pitfalls are:
- Not taking enough time to choose the processes that will benefit the most from RPA technology.
- Encountering unexpected behavior when trying to implement RPA with green-screen applications or older applications.
- Not being clear about end goals and expectations can lead to several changes in the creation of the RPA solution and this can lead to delays.
With implementation, naturally one has to also look into evaluation for improvement and better results from automation. “This is typically based on a combination of attributes,” says Sidney Madison Prescott, Global Head of Intelligent Automation, Spotify. “The first is to know what expectations have been placed on this effort. The second is to ensure transparency in the communication of results. Ideally, leveraging a data visualization tool that can be implemented alongside your RPA tool will help. Monitoring results becomes easier when you understand expectations and have set realistic goals. The move towards the data transformation is a marathon, not a sprint,” Prescott adds.
There are of course limitations to RPA technology. RPA is extremely rule-based and so processes that are not rule-based are not suitable for RPA. Automating a process without a clear understanding of how RPA is going to impact it can be huge waste of resources and time.
The key metrics to measuring the success or failure of an RPA automation solutions project are:
- How long does the transformation take to go to market and within what budget?
- Once bots are deployed, what are the kind of SLAs they are hitting? What are the run times, outages, error ratios, resolution time they produce?
- What is the bot count deployed, the percentage of KPI improvement, run rate, YoY improvement that is taking place? In a successful project, costs will come down and overall improvement in functioning will be high.
The Future for RPA Technology
The one concern the industry has is whether automation will make the employee redundant. “The accuracy and productivity that automation can provide is unmatchable. By automating repetitive and mundane tasks, it improves focus and efficiency, while letting employees focus on more rewarding tasks. Having the right conversations about it, which drive the view that automation is not about eliminating jobs but rather augmenting it, will ease these concerns,” believes Sehr.
Covid has had a major impact. Organizations are looking to see how to automate so that their work forces can focus on judgment and knowledge-based work. With that being a catalyst, RPA journey has just begun for many. “Systems are getting more intelligent and the next step will be RPA 2.0, or intelligent RPAs where they will be combined with AI and ML. Imagine a forecasting process where RPA pulls data from multiple sources and brings it to the AI-ML engine which then processes it and gives up some amazing stats to forecasters for the next quarter, or year. This evolution will take 2-3 years,” feels Satya.