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Can robotic process automation (RPA) really do it all? You might think, based on the market hype, that it is the cure-all for any digital transformation issue. We know RPA offers a compelling way for businesses to automate mundane daily tasks so employees can focus on higher value work. But have our expectations overshot the reality of RPA in practice?

The truth is RPA can provide a lot of value to the enterprise — but it isn’t always the quick fix many claim it to be. In fact, many organizations face challenges related to slow deployment cycles, broken bots and rocky roads between beginning goals and end results. 

RPA can be a significant first step in the digital transformation journey, but it isn’t the only step, and it often results in more headaches if not implemented properly. It’s important to be aware of common mistakes being made with RPA as it is increasingly woven into the fabric an organization’s infrastructure. With this is mind, let’s look at three of the most critical errors businesses commonly make with RPA deployments and how best to avoid them:

1. Thinking RPA Is Easy as Pie

RPA is not the “set it and forget it” solution many think it is. In fact, a recent survey by my employer, Pega of enterprises deploying RPA found 50% of respondents stating RPA was harder to deploy than initially expected. When implementing RPA, understanding the deployment will be a work in progress is half the battle.

The quickest way to see positive results is building sustainable automations that are part of broader business processes and systems. Complex automations shouldn’t be developed in isolation. Without a holistic strategy, you risk opening the business up to even more potential issues down the road. 

Most high-impact processes have human variations to account for, and many of the variations are undocumented or unknown. For example, if your bot links to a third-party application, the automations may break if the underlying business systems, rules, variations and processes change — such as a change by a software vendor to its user interface. 

That’s not to mention governance, security, compliance and hardware infrastructure needs, all important considerations that affect the overall success of the automation initiative.

Related Article: Why RPA Implementation Projects Fail

2.  Not Bringing Human Work Into the Equation

Most businesses focus their attention on unattended RPA when devising an automation strategy, but this isn’t the only game in town. Combining this traditional RPA approach with attended RPA (also known as Robotic Desktop Automation or RDA) is often the most agile way to automate at scale and bring bigger value to the business. 

For example, many customer contact centers provide customer service agents with myriad legacy applications to complete their multiple manual tasks. These employees are forced to switch back and forth between these applications to find and log new information for the customer. In fact, a 2018 Pega study found agents switch between applications around 1100 times a day. This is extremely inefficient. By enabling humans and robots to collaborate, businesses empower agents to deliver better and faster outcomes for the customer. 

Related Article: Combine Chatbots and RPA Bots for Better Customer Service

3. Treating RPA as a ‘Platform’ … Because It’s Not

Finally, it’s important to see RPA as just one piece connected to a much larger puzzle. 

Thinking about RPA as a standalone solution will lead to inefficiencies and disconnected outcomes. Enterprise systems are complex, and RPA is only one of many different technologies and processes in play. There are long-running processes bridging internal and external systems, humans working alongside machines, and a large number of third-party apps and systems running at the same time. RPA will not be the central solution for ALL of these coexisting factors, but it must work strategically alongside them. 

For example, despite market claims to the contrary, RPA is not a platform for artificial intelligence. RPA’s primary function is to automate legacy UI where an API doesn’t exist. What RPA is adept at is automating high-volume, low-complexity work. While this is important work, by itself, there’s not a lot of high-level computing intelligence needed to complete these rules-based tasks. It’s more appropriate to think of RPA as the muscle working alongside the AI brains and other elements of a broader intelligent automation platform that includes business rules and end-to-end process orchestration.

Related Article: Why You Need to Know the Difference Between AI and Automation

Succeeding with RPA Takes a Village

RPA success clearly can’t happen in a vacuum. To meet business goals, IT needs to work alongside business leaders to design an end-to-end intelligent automation solution on a much deeper level than automating simple tasks.

In the end, it’s important to think bigger. RPA success is attainable if you understand its role and what it’s capable of — and ultimately what it’s not capable of doing. While it automates high-volume, low-complexity, rules-based tasks, it works best as part of a broader effort. Leveraging RPA within an intelligent automation platform will result in greater ROI as together, they will orchestrate and streamline complex enterprise operations while ensuring outcomes.