A quick review of the RPA-pegged industries and how they have thrived through implementing the technology and learning from their mistakes

The RPA trend has been one of the hottest tech trends in the past 36 months/especially with the COVID-19 outbreak: It drives digital transformation initiatives by automating processes that otherwise remain manual. According to Gartner, RPA is the fastest-growing segment of enterprise software, and pure-play RPA providers like UIPath are valued at $7 billion.

The reports indicate that companies frequently get stuck after deploying just a few bots, with up to 50% of RPA deployments initially failing.?

How do we underestimate?

To understand this, it is essential to recognize what RPA is good at and where it excels. Using Robotic Process Automation (RPA), manual, repetitive human processes can be automated (i.e., made "robotic"). In one form or another, data entry or management are the most common examples. As a result, RPA accelerates throughput and eliminates errors while reducing costs in these scenarios.

Further, RPA is generally easy to implement and evident in its transition from analogue to digital, enabling organizations to score quick, visible results in their transformation process. i.e. UiPath is one of the best in the town!

It is these same strengths, however, that cause challenges in the use of RPA, and the reasons are subtle but inherent in the technology. The result has been stalled deployments, as well as organizations with only a few bots in actual use, even in cases where deployments have been successful. It is entirely possible that RPA can successfully automate a particular task while failing to meet the larger expectations and requirements of the organization.

It is within this broader corporate context that an RPA reality check is necessary.

It's not what you think it is

The use of robotic process automation is not a silver bullet when it comes to digital transformation; in fact, if its limitations are not properly understood (and at the risk of mixing metaphors), it can quickly become a dead end.

It is often the case that RPA initiatives that are unsuccessful fall into the following high-level categories:

1. Governance

The most common problem is a lack of governance. When an organization fails to deliver sufficient ROI, it's usually due to ineffective management and oversight. Initially, RPA was surrounded by significant hype, making companies mistakenly believe it would be a silver bullet.

The business workforce may not have approached the program with sufficient rigour, assuming that they would generate enough extensive automation without IT support to scale the program.

"Although there are undeniable benefits of using user-friendly automation tools, most ROI-generating automation are delivered by a professional automation team".?

2. Choice of automation candidate

It's sometimes hard for enterprises to choose the right automation candidate. It's relatively easy to automate basic tasks and with the combination of AI, which will enable us to automate endless opportunities is every business.

In the enthusiasm to drive automation, many managers don't consider the true TCO (Total Cost of Ownership).

Using automation bots for easier tasks creates incremental value for a single user. Instead of discrete task automation,it would be recommended to adopt a value-driven approach to automation. This way, the bot can have a major impact on an entire function and not just tweak tasks on one individual's desktop.

3. Management challenges

The failure of RPA is often caused by the management of the digital workers. Once the bot is built, it may appear that the work is done, and it will run autonomously without supervision. "In reality, automation is more like a human worker than a piece of software,". Much like a new employee, the automation will encounter scenarios in its early days in production that it did not see in the training.

In spite of comprehensive development models anticipating these scenarios, nearly every automation requires some level of retraining until it has run long enough to have encountered most scenarios. Additionally, automation experience changes in their environment, which require them to update.

4. ?Scaling challenges

A bot is a good stopgap measure in many scenarios that involve copying data from one application or system to another, but it can have scaling challenges.

The bots are a great technology but, like any technology, they have a point of diminishing returns. The bots consumed similar amounts of resources as information sets and data moves increased.

Having said that Bots gave us the ability to implement more quickly, but we also realized we needed a more forward-looking monitoring and management policy to identify when moving to an integrated process made sense.?

5. Third-party problems

As a result of inconsistent interfaces between third parties, using RPA for retrieving data from third parties can sometimes and most of the time lead to complications.

If you need to determine those data in a more accurate manner, then find alternative methods in order to do so.

6. Shadow deployments

It is important to set guardrails for bot deployments so that frontline users are less likely to make mistakes during the deployment process. "Bots make it easier for people outside the core development team to create code," and this can result in shadow development in organizations and a lack of oversight.

As with any other technology, bots require proper management, monitoring, and logging. It may seem easy to create a bot and deploy it. However, without the underlying technology and process, the bot will cause more headaches and create more work than doing the task manually.

7. Unrealistic expectations

In most cases, failures occur as a result of unrealistic expectations being set. It is common for many projects to begin with the hope of instant gratification. The prevalence of this issue is less prevalent than it was two years ago, but it still exists today. In some cases, these types of RPA failures have resulted in RPA environments that have flaws, missed requirements that become poor designs and automated processes that have not been tested with production data.

8. Poor change management communication

Before embarking on their RPA journey, many leaders fail to consider the organization's culture that will need to adapt and support changes to its working environment. It is not easy to adopt new technologies, particularly RPA, since it will not only change how individuals perform their jobs, but also change the behavior of the organization as a whole." In order to ensure success, it is essential to communicate these changes effectively, listen to employees' concerns, and obtain their buy-in.

In addition, it is crucial to train employees in terms of their skills and their ability to navigate this new bot-human culture.

9. Improperly defining success criteria

By failing to define success, IT leaders also inadvertently doom their automation initiatives from the outset. At the beginning of the RPA journey, it is important to identify the desired outcomes, yet in many cases, organizations focus solely on cost savings. While saving time and money is certainly valuable, this perspective obscures other tremendous benefits of successful RPA implementation. Among them are the quality of the deployment, the increase in overall productivity, and, most importantly, the impact on the workforce.

Summary

It's important to remember that RPA will not transform the organization on its own, but it can digitally transform certain processes within the organization. And, customer and vendor enthusiasm can overhype the technology to its detriment over the long term.

Ultimately, RPA's rapid growth is an indication of how businesses are transforming themselves digitally. However, the industry is still in its infancy and is at the beginning of a much larger journey.

Thank you!

Reading Source - Forbes Tech Council