As technology continues to progress in the workplace amongst industries, most have implemented their enterprise development initiative with robotic process automation. This large embrace of digitalization has urged shared services leaders to focus on scaling RPA operations across their organizations. These strategies don’t necessarily require introducing more bots, but rather leveraging advanced intelligent automation technologies; in this scenario the next stage is machine learning. When increasing your robotic processes, it can be easy to make a small mistake leading to scaling failure. Instead of learning from your own mistakes, take a step back and learn form the mistakes of other; analyze the process of those who failed the first time. 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 automated processes across operations can be, setting the tone with appropriate rules and partnerships can create a much easier journey. The first key step to scaling successfully is confirming you have the proper governance set. Having a set governance means prioritizing the right functions to automate and sidestep those that are too complex. Start with identifying the easily replicated and repetitive tasks similar to the ones you’ve already automated, and then make a second set of processes to automate later in the process. Prioritizing your problems and processes simplifies the process and can lead to speeding up your scaling project.
An additional 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 people frees up workforce capacity, but these automated processes are still relying on humans to keep them in check.
Automating the process of managing, or maintaining, bots frees up more time for the human workforce permitting them to conduct more value-added work. Scaling up robotic process automation operations requires consistently taking action to reinvest in freeing up workers’ capacity making way for improvement in their skills and knowledge on cognitive technology. This strategy delivers an opportunity to enhance operations without having to hire more people. In other words, scaling your RPA program and upskilling your workforce simultaneously results in consistent process improvement. This kind of investment in cross-skills training for your human workforce teaches them how to evaluate the proper automatable functions and ultimately start creating the bots themselves.
Encouraging the enthusiasm of RPA in your company culture is a big driver for implementation. Empowering the choice to automate is a great way to motivate teams to develop their own robotics, or bring in a team of developers, to solve their problems. It is wise to be cautious with this strategy because it can result in too many tasks being automated. The automating too many tasks adds more complexity to the bot. As a bot becomes more complex you can run into a long term scaling failure that is too intricate to maintain.
Scaling your 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 last, but not least, tip for successful scaling is setting realistic expectations by prioritizing your targets appropriately. Implementation is about the outcomes it delivers for your customers, not 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 in that order. Scaling robotics is a key force to improving performance, but it is easy to over-automate without setting a target for your outcome.
Analyzing your problems prior to implementation is the best strategy to take when scaling RPA. The best 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. Access How to Ensure Your RPA Can Be Scaled from the Start for more strategies and tips on successful RPA scaling.
