Since robotic process automation (RPA) hit the marketplace hard in 2015, there’s been a lot of buzz and mixed feedback. But given the enormous promise of intelligent automation, hyper automation and its inevitability, why do so many RPA projects fail?
Most of the RPA project failures are highly subjective, and not very extreme. But, by most measures in the information technology world, RPA fails have been fast and small—and that’s part of RPA’s attraction.
The 5 types of RPA project fail
it is necessary to understand the word “failure.” What exactly is meant by “failure” when describing a typical RPA implementation?
To make sense of why RPA projects fail, you need to classify the types of failures that occur. These categories, in descending order of frequency and intensity, include:
-
Financial—Failure to deliver business value or meet business expectations
-
Governance—Failures due to errors in management and oversight or wrong expectations
-
Operational—Failures due to operational errors or missed operational expectations
-
Design—Failures due to poor design practices or principles
-
Technical—Failures of the software to perform as expected
Financial failure. The vast majority of RPA failures are financial in that they fail to deliver the expected business value. This is nearly always due to a lack of expected cost savings, since few RPA implementations focus on revenue generation. In these implementations, the bots themselves might have worked perfectly, and often do, but the results did not meet expectations of value and financial returns.
Given that the attraction of RPA is that it can reduce costs, financial failures are particularly annoying both to the companies using the technology and the companies selling it.
Governance failure. Governance failures, those caused by not using bots correctly or effectively, are the second most common cause of RPA failures. Bots are a workforce that, just like any other, must be managed and governed to maximize their effectiveness. Most organizations devote significant time, money, and energy to building out a center of excellence (CoE) for RPA.
The key here is balance; have enough governance to absorb complexity as your bot population expands, but not so much that the cost and complexity make RPA lose its value.
Operational failure. Operational failures occur when the bots don’t perform as expected when placed in production. This is different from governance failures, which occur when bots aren’t correctly or effectively coordinated. Operational failures deal with how individual bots operate, rather than how the bots coordinate with others.
Operational failures examples are (1) Scheduling bots to run at times when the systems they must access are unavailable (2) Building bots that require a level of security access that company policy precludes (3) Requiring access to systems that cannot be accessed through firewalls or other technical constraints
Operational failures result not from poor design or poor functionality, but from poor planning and governance. And while financial and governance failures aren’t overly costly, operational failures can cause millions of dollars in losses.
Design failure. Design failures occur when the bot was programmed erroneously. The issue could be related to missed requirements, misinterpreted requirements, poorly executed designs, or errors in solution architecture.
Despite growing experience with RPA, poor design practices are still prevalent in many organizations. Companies that hired their own staff often didn’t know what qualifications were necessary and were unable to adequately verify the skills of those they hired. Like operational failures, design failures can be very expensive—particularly if you have discounted the importance of testing.
Technical failure. Technical failures occur when the selection of RPA software goes wrong and does not perform as expected or intended. Examples of this include incompatibilities with certain tools, settings, or standards that prevent the underlying tool from fulfilling its intended purpose.
RPA has few technical failures, because the underlying technology is either too both basic or too mature. Macros are hardly new, screen-scraping & OCR has been around for decades but artificial intelligence (AI) capabilities are getting into main stream very rapidly.
Conclusion. The truly new part of RPA is its coordination and governance capabilities, and some of the development tools that allow developers to create bots rapidly. But these functions are enhancements to the underlying technologies, and hence rarely cause functional failures.
While the list of organizations that have succeeded with RPA projects at scale is growing. Organizations that achieve early success with this wave of automation will create a structural advantage that their competitors will be hard-pressed to counter.
This article is abstracted from Chris Surdak’s book, The Care and Feeding of Bots: An Owners-Manual For Robotic Process Automation, which covers the reasons behind RPA failures—and best practices for avoiding them—in more depth.
