RPA is a revolutionary technology rather than just another development with a set of frameworks. As with any novelty, it requires new, sophisticated approaches. It may be challenging to choose the right direction, especially if you are dealing with RPA for the first time and lack the appropriate expertise.
To save you from trouble in this delicate process, today, we share the most likely RPA development mistakes newcomers make. Knowing these before the development process itself should help you avoid them.
Top Mistakes in Building an RPA Solution you don’t Want to Make
Not Enough Domain Knowledge
Your RPA solution may meet the latest market demands, be fast and scalable, but what truly comes in the result? Mind your financial KPIs as you may develop a solution the market doesn’t need.
Why may it happen? Because you may be spending enough time to train your IT staff, and they do their job perfectly, but your marketing and even HR departments don’t know how to promote the solution. Everyone should be on board and have a common understanding of the new product and the market it should serve. Thus, make sure to provide comprehensive RPA training for the whole team.
Wrong Use Case
However, robust onboarding is not a panacea to marketing concerns, as you may choose the wrong set of tasks to automate with your RPA solution. Research the market beforehand; is there a perceived demand for automated tasks you wish to automate? Will your solution deliver the value?
If the answer to both questions is “no,” you should look for another automation candidate. But don’t make your focus too narrow, look at a broader picture instead. Ideally, your RPA solution should cover a broad spectrum of tasks, which will maximize chances for delivering an actual value to the market.
Underestimated Maintenance Requirements
Mind that your RPA application, whether a chatbot or an automated document processing system, won’t be running tasks smoothly once you build it. Instead, it needs constant control on your part.
For instance, chatbots develop through self-learning, so ideally, time should pass before they can perform tasks impeccably. In the meantime, you should oversee the learning process by running a continuous maintenance program. The goal is to check how the application handles peak loads to avoid unpredicted outages and downtime.
Integration with Third-party Tools
When it comes to interacting with third-party APIs, you may encounter another serious issue. Other applications’ interfaces aren’t uniform, meaning possible compatibility conflicts.
At this point, you should consider automating data retrieval from third-party sources, which will make your RPA operation more efficient. However, don’t overcomplicate the procedure, including only the relevant scenarios in your automation program. Typically, RPA applications don’t recognize context-specific patterns, working only with these you initially defined in your automation algorithms.
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