It’s a proven fact that Robotic Process Automation (RPA) delivers huge benefits to organizations that get it right. ROI is measured in months instead of years and operational costs typically drop by 40%, with average 41% productivity gains. But implementing the technology is not as simple as it looks - and RPA pitfalls abound for companies without the right expertise in their corner.
Although 53% of organizations have started an RPA journey, only 3% have managed to scale RPA successfully. Nearly half of RPA journeys stall after automating 10 or fewer processes – and 30-50% of implementations fail altogether.
In the post-pandemic world, a digital transformation that can optimize costs while increasing productivity, agility, and resiliency is in high demand – especially if it doesn’t require a pricey overhaul of legacy systems. Forrester Research predicts automation like RPA will be key to helping businesses prosper in the “new normal” - reducing dependencies on manual processes and individuals, and providing more timely access to actionable data and insights.
Nearly 50% of organizations aim to increase their RPA spend over the next year to combat COVID-19 challenges. For most business leaders, the question is no longer whether they should implement RPA – but how to get it right?
Several common RPA pitfalls lead to failure or slow adoption of the technology. Topping the list are unrealistic expectations, automating the wrong processes, missing critical RPA skillsets, and the wrong implementation partner.
Companies tend to underestimate the complexity of their business processes - and bots will frequently malfunction without an RPA design team that knows how to anticipate and prepare for most process exceptions. Unresolved process exceptions rank among the biggest RPA challenges, prompting frustrated users to revert to manual work.
Implementers that don’t control “scope creep” can also derail the ability to scale RPA. RPA providers paid on a “time and materials” basis are often incentivized to stretch timelines by continuously expanding the project. Deloitte reports that a whopping 63% of organizations fail to meet RPA delivery deadlines.
In contrast, choosing an RPA partner with a transparent risk-sharing model helps companies control costs by establishing the price tag upfront. Not only do these providers ensure companies derive the most value from their automation – which drives organizational support - but they are incentivized to deliver a quality product on time because they eat the margin if they don’t.
Aligning the leadership team behind an automation end-goal and strategic vision early on is also key to the ability to scale RPA successfully. An effective POC can be dazzling, and it’s easy for organizations to start automating for the sake of automating.
Whether the goal is cost savings, quality control, real-time reporting to achieve one version of the truth, or another business driver, crystallizing a clear vision at the start keeps an RPA program from imploding because it tried to accomplish too much or automated processes that didn’t make sense.
Failing to build an RPA Center of Excellence (CoE) with the right implementation capabilities for the first wave of automation is another common RPA pitfall. Whether an organization keeps its journey in-house, hires a third-party provider, or opts for a hybrid solution, the skillsets required to imagine, build, and maintain a company’s process robots never waver. An effective CoE includes a long list of critical roles, from Automation Champion to Solution Architect to Change Manager.
But the newness of RPA technology combined with the national shortage of skilled IT workers can make it difficult for companies to staff scarce and costly RPA talent in-house. It’s also hard to justify the cost of building a CoE for organizations starting a POC or without a large automation pipeline. So too often, companies cut corners and set their journey up for failure.
More than 60% of organizations use dedicated third-party partners for their RPA journeys, realizing the critical need for skills they don’t possess to drive their journeys and maintain executive buy-in. For most, a hybrid model that leverages the resources, knowledge, and experience of a reputable provider with the business expertise of in-house staff ensures the best business outcome.
Neglecting the need for a separate support structure is another significant obstacle to RPA scaling. Robots aren’t a “set it and forget it” investment – they inevitably require some tweaking to adjust to unexpected process exceptions.
But many organizations wrongly assume their developers will also maintain the bots without contemplating bandwidth or skillsets. As a result, broken bots languish before they are fixed, and organizational trust in the technology dissipates.
At a time when RPA scaling stands out as a unicorn, best practices lead to success. Carefully selecting processes that can deliver quick wins at the start of an automation journey builds trust and credibility, eliminating the skepticism that often accompanies new technology.
Hyperautomation can amplify RPA scaling as well. A hot buzzword on the IT landscape, hyperautomation integrates supporting technologies like Process Mining and Machine Learning with RPA. For instance, UiPath’s Long Running Workflow tool stops robots from malfunctioning if they encounter a process exception by enabling humans to make key decisions while robots complete other tasks.
It’s nearly impossible to find another transformation that achieves the same ROI as RPA – as long as it’s implemented effectively. Want to discover the “secret sauce” to scale RPA successfully? Download the Auxis webinar, “Why is it so Hard to Scale your RPA Program? Going from POC to Hyperautomation”, for more actionable insights organizations can use to overcome RPA challenges and reap the benefits of this game-changing technology.
- The importance of aligning behind an automation end-goal and strategic vision early on
- Common pitfalls when implementing RPA and how to avoid them
- How to build your implementation capabilities and RPA CoE
- How to structure your automation program for success
- Organizational adoption and change management