As technology advances in the workplace among industries, majority have executed their enterprise improvement process via robotic process automation. With this wide embrace of digitalization, shared services leaders are now focused on scaling RPA operations across their businesses. This type of strategy doesn’t necessarily require bringing in more bots, but rather leveraging advanced intelligent automation technologies; in this case the next step is machine learning. When expanding your robotic operations, it can be easy to take misstep leading to scaling failure. Rather than learning from your own mistakes, take a step back and analyze the process of those who failed the first time. Learn how to ensure your RPA program can be scaled successfully from the start with these key tips to building scalability into your automated operations.

As challenging as expanding robotic processes across an enterprise can be, setting the tone with proper rules and partnerships can make it a whole lot easier. The first key to scaling successfully is making sure you have the proper governance set. Having a set governance means prioritizing the correct functions to automate and avoid those that are too complex. Start with highlighting the easily replicated and repetitive tasks similar to the ones you’ve already automated, and then make a secondary set of processes to automate later on. Prioritizing your problems and processes first simplifies the process and can result in speeding up your scaling project.

Another tip to keep in mind is automating the human-fueled risk management to free up time for your workforce. Automating repetitive tasks that were once completed by humans frees up the capacity of your workforce, but these automated processes are still relying on humans to keep them in check.

Automating the process of managing, or checking, on bots frees up more time for the human workforce allowing them to conduct more value-added work. Scaling up RPA requires consistently taking action to reinvest in freeing up worker’s capacity making way for improvement in their skills and knowledge on cognitive technology. This strategy provides an opportunity to speed up operations without having to hire more people. In other words, scaling your RPA program and upskilling your workforce simultaneously results in consistent process improvement. Investing in cross-skills training for your human workforce teaches them how to analyze the proper automatable functions and eventually start creating the bots themselves.

Embracing the enthusiasm of RPA in your company culture is a big drive for implementation. Empowering the choice to automate is a great way to encourage teams to develop their own robotics, or bring in a team of developers, to solve their problems. It is wise to tread lightly with this strategy because it can result in too many tasks being automated. The bot becomes more complex the more tasks you automated. As a bot becomes more complex you can run into a long term scaling failure that is too complex to maintain

Leveraging your scaling automation means embracing the power of machine learning. Machine learning is not a one-man show, but must be accompanied by other robots. Machine learning is implemented by being supported at both the input and output of its code with historic bots, or bots that have already been within operations. This allows historical data to bee fed into the machine learning module to improve the automation’s reliability. This implementation should be conducted with care due to its complex design. Once this operation is mastered, is has the ability to improve the impact of your robotics. When scaling across your organization, it is just as important to improve the intelligence of your robotics to yield better returns.

The final key for successful scaling is setting expectations with prioritizing your targets properly. Implementation is about the outcomes it delivers for your customers rather than the amount of bots in place. This key factor means focusing on the needs of your outcome quality and not your implementations. If your goal is to improve your quality of service and turnaround, then set that as your target specifically in that order. Scaling robotics is a key force to improving performance, but it can become easy to over-automate without setting a target for your outcome.

Analyzing your problems before implementing is the best strategy to take when scaling RPA. One thing to keep in mind is that the technology is not the leader but the follower. Essentially, you should be identifying the gaps in your operations and those challenges should prescribe the appropriate tools to fix them. Prioritizing your business objectives delivers better results and delivers a better quality of output. For more on this topic, check out How to Ensure Your RPA Can Be Scaled from the Start.