Hyper Automation: Just Another Buzzword?
Currently, many companies and organizations are working tirelessly to implement business automations at the core of their operations to gain several benefits. These include improving clarity of core business processes within the organization, enhancing the efficiency of these processes, optimizing them, and consequently improving the overall user experience for their customers.
Implementing this will require significant time and is an investment for the long run. But with the recent rise of AI technologies, we’re hearing more about hyper-automation, which is the next step in the company’s workflow automation journey. So, what is it, why does it matter, and most importantly, what are the main challenges to implementing it in real-world use cases?
What is Hyper automation?
Hyperautomation is the process of increasing the automation of business processes by integrating technologies such as Artificial Intelligence (AI), Machine Learning (ML), and Robotic Process Automation (RPA). It aims to automate almost any repetitive task, allowing for quicker and more efficient processes, reduced errors, and improved employee satisfaction.
Hyperautomation involves the orchestrated use of multiple technologies, tools, or platforms, including AI, ML, RPA, and other automation tools.
For exmaple : Designing a BPMN process and running it through a BPMN engine is an example of workflow automation. Workflow automation typically focuses on automating individual tasks or sequences within a broader process. Hyper-automation, on the other hand, goes a step further. It covers a wider array of technologies and aims for a more comprehensive level of automation throughout the enterprise. It doesn’t seek only to automate processes but also to fundamentally transform and optimize them through intelligent decision-making and predictive analytics.
The journey to hyper-automation and its challenges?
- Cost and Measuring ROI : The initial investment for hyper-automation technologies and expertise can be significant. And to demonstrate the return on investment can be complex, especially if the benefits are qualitative or long-term.
- Integration Complexity : Integrating multiple systems and technologies is a complex task that requires sophisticated strategy and planning.
- Data Quality and Accessibility: Effective automation relies on high-quality, accessible data. Gathering, cleaning, and structuring data for use across different systems pose significant challenges.
- Scalability : As organizations grow, scaling hyper-automated processes while maintaining efficiency and control is challenging.
- Security : Ensuring that hyper-automated processes comply with regulations and standards, particularly regarding data privacy and protection, is crucial and complex.
Addressing these challenges requires a strategic approach, investment in training and technology, and a willingness to adapt and evolve processes continuously.
