Similar to the aims of BPM technology as a broader concept, robotic process automation (or RPA) is intended to improve the productivity and efficiency of common business processes. In essence, RPA can take over tasks previously performed by humans, removing the human limitations that BPM often bumps up against.
RPA uses the same platforms, software and user interfaces that humans utilise to execute transactions. However, unlike humans, robots do not grow tired and do not take time off from work. Robots are also less likely to introduce errors into processes. So, when taking a holistic approach to managing business processes, companies can include the speed, productivity, and reliability introduced by RPA into the equation. Broadly speaking, RPA can quickly deliver large benefits wherever a business encounters repeated processes that are structured and well understood.
BPM practitioners have long realised that digital processes can deliver outstanding returns. However, these digital processes can be dramatically improved by introducing intelligence into the way processes function. Artificial intelligence (AI) can, for example, be used to optimise processes during process design to ensure maximum efficiency and reliability.
By 2022, 75% of enterprises will embed intelligent automation into technology and process development, using AI-based software to discover operational and experiential insights to guide innovation, according to IDC.
AI technologies like natural language processing (NLP) can free up humans to focus on tasks that ultimately require human cognition, instead of getting stuck with tasks that can be performed by technological solutions. Furthermore, AI can offer predictive abilities, enabling processes to become more pro-active rather than merely reacting on data inputs. In essence, as with RPA, AI allows BPM decision makers more flexibility in the way processes are designed and managed.
Data science has come a long way: advanced data analysis is easier to apply in practice than ever before, and new fields in data analytics are fuelling data science applications in a broad range of areas, including BPM.
IDC says, by 2024, the productivity gap will double between enterprises that deploy ML-enabled data management, integration, and analysis to automate IT and analytics-related tasks and those that do not.
Measuring performance is a key tenet of BPM technology, and data science can deliver the insights that lead to actionable performance improvement measures. For example, projects are a frequently repeated business process, and project management stands to gain from more insight into the success of projects. Even though project management and BPM are distinct sets of responsibilities, they are correlated and work together towards organizational success.
For instance, at ELEKS we have developed a business intelligence dashboard capable of measuring the health and success of large projects. Using advanced data science, including statistical tools such as Bayesian Networks, we are able to provide actionable project insights, enabling management to get clear visibility into the overall status on projects company-wide.
Next, data is key to decision making during many business processes. Here data science can also provide increased BPM performance by improving the decision-making capabilities of processes. Processes can make better decisions thanks to a powerful combination of additional information alongside better analysis during the decision making process.
The main driver for BPM technology has moved beyond merely trying to find cost savings to include other factors, including better product delivery and improved customer experience. Companies stand to gain from revisiting their business processes and by investigating the application of the three technologies we outlined.
ELEKS can, of course, help your company find the most optimal ways to apply new tech including data science, RPA and artificial intelligence. Get in touch with the Data Science experts at ELEKS for insight into how technology can transform your business processes.
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