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Four key RPA challenges and how to solve them

Increasingly businesses are turning into intelligent automation techniques and tools to gain a competitive edge in a volatile market. In this article we'll go through one popular solution called RPA and view some of the key pitfalls and challenges for adopting it.

What is robotic process automation (RPA)?

Robotic process automation (RPA) is the use of software robots or agents to automate business processes. RPA can be used to automate repetitive, time-consuming tasks, such as data entry, to improve productivity and efficiency. RPA can be used to reduce costs, improve accuracy, and increase customer satisfaction. It can also be used to automate processes that would otherwise require manual intervention.

The global market for RPA reached USD 2.65 billion last year and is predicted to grow even further. The majority of Fortune 500 companies have used it to some extent, and even SMEs are paving their way toward automation through the use of bots. 

When starting with RPA, organizations aim to change the workplaces - achieve higher efficiency, harmonize the processes, and most importantly, attain more excellent customer responsiveness. However, RPA robots are not magic pills that can solve all problems. In this article, we look at the significant pitfalls of RPA that prevent it from scaling its capabilities to the entire organization. 

Challenges of RPA implementation

1. Sudden changes break the bots. 

RPA bots are very sensitive to changes in business applications and require a relatively static environment. For humans, it’s easy to adapt to small layout changes and new interfaces. Robots, however, need thorough reconfiguration. Most of the bots rely on UI element detection or screen-scraping techniques. These rule-based navigation paths don’t work anymore, as whenever there are any changes to the UI or the layout, robots get confused and break.

RPA technology is definitely getting more robust and sophisticated, with vendors advancing their robots’ adaptation functionalities. At the same time, more and more applications are moving to the cloud, where UI changes are a new standard and happen frequently. However, the RPA robots are still far from being independent or intelligent. When changes occur in a business application, that might reflect in the robot's work. As it is part of a larger workflow consisting of hundreds of steps, even the slightest change might result in more significant consequences for the entire work. 

For example, one company might have 100 different business applications, and each of its websites might have up to 50 web pages within it for various tasks. That quickly adds up to 5000 different screens that people use when executing workflows. If each of those windows has 20 movable/clickable UI elements like buttons and dropdown menus, that will be worth 100000 possible breakdown points that could damage the robot.

Changes in one part of the process might influence and break the algorithm

2. Difficulty finding the best RPA cases

RPA is not plug-and-play; you can’t build bots overnight. PwC report finds that RPA projects expected to take 4-6 weeks end up taking 4-6 months, with companies significantly underestimating the deployment time. 

Although RPA’s time-to-value is shorter than other IT projects, it still goes through different phases of IT project implementation. Therefore, the cases and workflows for which the robots are built must be selected carefully, and their impact must be understood before the whole multi-stage process. In addition, companies must make sure that what they’re working on for half a year pays off well and brings a good lifetime value. 

Businesses worldwide are experiencing crisis after crisis, with changes affecting every aspect of the business. It’s hard to find suitable RPA cases in such a fast-paced environment. What you started building three months ago might not be needed anymore because of changes in the process, IT systems, or overall management. 

Another problem arises when people manually evaluate RPA’s potential and do not find enough automation cases. However, the results could be completely different when analyzed by special software. Most teams have many manual and repetitive tasks that could be automated, although sometimes they might not be as evident. 

3. Measuring real business performance

Although RPA is easier to implement and brings value in a shorter time than many other IT projects, it is still an investment and usually a big one. Having good outcomes is critical with all the commitments made. 

Organizations implement RPA in different business units and shared service centers, and its effectiveness is usually estimated in hours saved or FTEs. However, that’s not always the best thing to measure. After all, what’s the end goal of using software robots - decreasing throughput time or improving customer experience? Therefore, not having the right targets to measure is one of the reasons why RPA can’t scale quickly. 

4. Identifying the need for RPA

Automation is overhyped. Most automation-related projects fail, and companies significantly underestimate the investments needed for RPA projects. Considering this, ask yourself - is this the right time and place to go and deploy the first robot? 

One of the primary pitfalls of RPA is that many times companies are simply not ready for automation. Most organizations work in an entirely fractured way, making it hard to find RPA cases that bring benefits and make substantial changes to work. The main question is - how to streamline work and change current practices to allow more straightforward implementation of automation projects in the first place? 

Sometimes, automation is not necessarily the best thing to do. It’s meaningful to reconsider the entire process and look at it from a fresh perspective - is it vital, or does it need modifications? 

RPA is just part of the bigger picture. It is not a magic tool that can solve everything alone. Instead, it’s one of the technologies that can make things easier if and when used for suitable cases and aligned with the overall strategy. 

How can process intelligence help with your RPA projects?

The initial reasons for starting with an RPA can be many - increasing work efficiency, improving customer experience, decreasing the staffing costs, or even scaling the company faster. Regardless of motives, these are how process intelligence software can help you.

  1. Identify exactly where the chain of breakages started and which UI or layout changes caused it. Workfellow can identify how significant the impact was - what bots got affected, how many FTEs worth of work it accounted for, and its effects on overall KPIs. 
  2. Monitor all the workflows and get a backlog of valid RPA cases in prioritized order. Most importantly, get all that information in real-time. 
  3. Measure your automation ratio and know the impact of automation on specified KPIs. Instead of maximizing hours saved, which might or might not be the most helpful measurement, you can see the real impact of automation on more important criteria. 
  4. Get data-backed justifications on when and where you should invest in bots next.
To learn more about process intelligence can support RPA, download the Work API whitepaper.

Written by

Kustaa Kivelä

CEO & Co-founder at Workfellow. Expert in organizational digital transformation.

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