5 Common RPA Pitfalls and What You Can Do about them

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Elizabeth Mixson
03/24/2021

RPA isn’t a silver bullet. Successful implementation as well as long-term success require careful planning and strong governance
Scaling RPA is not only paramount to achieving ROI, but driving transformational results
Don’t think you have to automate 100% of all processes. Attended automation and HITL RPA can deliver more business value than unattended RPA
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RPA is ill-suited to change and instability

There are few things more integral to the success of robotic process automation (RPA) than process selection. The process must be simple and repetitive enough to be a good candidate for automation, but also high value enough to make it worth the trouble and monetary investment.

Process mining, process intelligence and automated process discovery tools can help automation leaders overcome these challenges. Using the insights gleaned from these tools, business leaders can more effectively identify which processes are the best candidates for RPA based on suitability (i.e. whether it’s rule based or requires human intervention) and ROI (i.e. how frequently the process is run, how time consuming it is, etc.). In addition, as processes should never be re-engineered and automated at the same time, these tools can also be used to optimize and prime processes to prepare them for automation.

Furthermore, RPA does not do well in unstable operating environments as even small changes to the process or interface can cause a bot to malfunction. When selecting a process, make sure it’s not only rules-based and consistent,

See a successful RPA implementation in Action: How bots played a key role in process transformation at Infosys – A case study

Scale Matters

RPA isn’t only expensive, as mentioned before, it can be rather brittle and RPA “bots” aren;t compatible with every system. By scaling RPA and developing an enterprise approach to implementation that is closely linked with digital transformation efforts, automation leaders can overcome both of these limitations.

For example, Pascal Bornet’s latest book, aptly titled “INTELLIGENT AUTOMATION,” shares a case study whereby a bank was able to deploy one RPA tool and realize a 30% improvement in resolving fraud. However, when the bank scaled the tool across an end-to-end process and integrated it with multiple digital technologies, it was able to solve 70% more instances of fraud and saved $100 million.

In other words, RPA works pretty well on its own. However, when it is scaled across multiple functions and fully integrated into a larger IT ecosystem, it can deliver transformational results.

In fact, scaling automation is so important, we created a full 3 day event on the topic.

Scalable RPA & Intelligent Automation Live

The key for global corporate enterprise is to benefit from the collective intelligence presented by RPA and cognitive technologies along with human workers. Only by having technology combine with human talent can global corporate enterprise achieve scalable intelligent automation. And only with scalable intelligent automation enterprise resiliency be realized.

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However, full end-to-end process automation isn't always economical

Automating 70-80% of a process is fairly easy and cost effective. However, beyond that, technology costs can skyrocket. In fact, according to some research, automating a process completely, though desirable, may be five times more expensive than automating a process up to 80% because that additional 20% will require automation code that’s a lot more complicated than the code required to automate up to 80%.

Instead of automating business processes in their entirety, many organizations opt for a human-in-the-loop (HITL) approach or Attended RPA. Simply put, attended RPA requires some sort of human interaction to complete it’s assigned process.

For example, let’s say the RPA bot runs into some sort of outlier in the data or error. Instead of proceeding as usual, the bot will notify a human to verify the information.

According to Automation Anywhere, attended RPA has a higher adoption maturity and larger installed base in terms of license volumes vs. unattended RPA (no human intervention required) and currently over half of all RPA processes (~57%) require human intervention.

Implementation time

According to a recent study, 60% of RPA implementations fail to meet expectations when it comes to the time it takes to implement. In other words, RPA implementations often take much longer than expected. In fact, research by PWC has found that RPA pilots often require 4-6 months instead of the standard 4-6 weeks because companies “fail to identify the right process or have insufficient information about their existing processes.”

As Nintex's Manager, Tech Evangelism, APAC region, Chris Ellis, told Jon Reed for Diginomica, “Businesses underestimate the time it takes to implement, and I don't just mean from a 'creating the bots' point of view, but also training the bot to handle exceptions when it comes to changes in underlying applications or sites, popups, errors and sanitizing data.”

To overcome many of these challenges, Ellis recommends, “integrating a digital process automation workflow tool, which essentially merges a "human-centric review-and-approval overlay" with RPA.”

Don’t overlook post-implementation maintenance & governance

Last but not least, one major pitfall of RPA is that many automation leaders underestimate how much work goes into maintaining and updating RPA post-production. Afterall, any time a bot malfunctions it has to go offline to be repaired or replaced, a time consuming process that can quickly drive up costs especially if a business critical process is disrupted.

According to blueprint, there are three main reasons why bots break or require maintenance:

Changes to the user interface (UI) of the applications they interact with
Changes to regulations, policies, or controls that impact the bots
Missed requirements during the development process

Instead of relying on a “reactionary” approach to bot maintenance (i.e. waiting until something goes wrong), embracing a “proactive” approach to whereby with, careful planning and sound RPA governance, breakdowns are addressed before they happen.

According to xenonstack, effective RPA governance frameworks typically include:

Strategy, Leadership & Organizational Fit: Ensuring sponsorship, accountability, and the fitting mindset will pave the way for the digital workforce.
Organizational Expedition & Change: Make RPA in Supply Chain – Reimagining Your SCM with RPA Botssure your rules-of-engagement, roles & responsibilities, and change impact are properly defined and understood.
Deployment and Operations: Align process pipeline management and methodology to avoid silos, redundancies, and operational vacuum.
Security and Compliance: Manage your business continuity, data access, and security as an integral part of the digital workforce.