Shared services have undergone significant changes over the last decade as businesses seek to optimize operations, improve scalability, and reduce costs. At first, many businesses opted for Robotic Process Automation (RPA), which helped in streamlining routine activities like invoices and payroll processing. However, organisations are increasingly adopting hyperautomation to automate more complex processes.
Understanding Hyperautomation
Hyperautomation is the combination of different technologies, including artificial intelligence (AI), machine learning (ML), process mining, analytics, intelligent document processing, and workflow automation. As opposed to RPA, which involves automating routine activities based on certain rules, hyperautomation combines these technologies to support data analysis, decision-making, workflow automation, and continuous process improvement.
In the context of shared services, the use of hyperautomation technology will enable organizations to automate advanced operations relating to financial, human resource, purchasing, IT support, and customer service departments.
Limitations of Traditional RPA
RPA has proved successful, but its application comes with certain challenges. RPA bots excel at structured, rule-based tasks but can face limitations when handling unstructured information, changing processes, or tasks requiring judgement. For instance, a bot can perform tasks like processing invoices seamlessly, yet it will need further enhancements before it can understand handwritten documents or identify suspicious activity.
Shared services teams frequently deal with complex processes that necessitate context interpretation and data analysis. Hyperautomation offers a much wider and long-term perspective on the issue.
The Role of Artificial Intelligence
Artificial intelligence plays a significant role in hyperautomation by enabling systems to analyse data, identify patterns, and support decision-making. Artificial intelligence chatbots can help answer employees’ questions, whereas machine learning systems can detect potential inefficiencies and provide relevant solutions.
In financial shared services, AI can detect any inconsistencies with expenses that may indicate violations of regulations. In Human Resources functions, AI-powered systems can help evaluate applications, schedule meetings, and analyse employee engagement.
Process Mining and Workflow Optimization
Process mining is yet another significant aspect of hyperautomation technology. By using data, process mining enables organisations to visualise, analyse, and optimise workflows using process data. The benefit of process mining is that it provides a data-driven view of how processes actually operate rather than relying on assumptions.
In shared services centers, process mining increases visibility and enables management to allocate resources optimally. Once process mining is integrated with automated workflows, organizations can make approvals easy and fast, ensuring that consistent service is provided throughout the business units.
Enhancing Employee Experience
Hyperautomation is intended to support employees by reducing repetitive work and enabling them to focus on higher-value activities. Automation makes work easier for employees, enabling them to engage in more complex tasks and activities. For example, shared services employees can dedicate time to critical activities such as problem solving, interacting with clients, and developing plans.
Automated self-service portals, AI-driven support systems, and intelligent workflows also improve employee satisfaction by providing faster responses and smoother operational experiences.
Data-Driven Decision Making
One advantage linked to hyperautomation is the capability to make use of data in generating insight. Through analytics, it becomes possible for organizations to collect vast amounts of data and extract valuable insights that could influence decision making.
This can help organisations assess shared services performance, anticipate future demand, and identify areas for improvement. This leads to improved sustainability and growth.
Conclusion
In summary, hyperautomation is becoming an increasingly important part of the evolution of shared services. While RPA has been very instrumental in the development of shared services, there is a need to develop even smarter and interconnected processes within organizations. Through integration of AI, machine learning, process mining, and analytics, hyperautomation can significantly transform how shared services are delivered and managed.
FAQs
Q1. What is hyperautomation and why is it gaining popularity?
Hyperautomation combines AI, machine learning, analytics, and automation technologies to streamline complex business processes and improve operational efficiency.
Q2. How is hyperautomation different from traditional RPA?
While RPA automates repetitive rule-based tasks, hyperautomation can combine AI, analytics, process mining, and automation to handle more complex processes and support decision-making and process improvement
Q3. Which business functions benefit most from hyperautomation?
Finance, HR, procurement, customer service, IT support, and shared services operations can significantly benefit from hyperautomation.
Q4. Can hyperautomation improve employee productivity?
Yes. By automating repetitive tasks, employees can focus on strategic work, problem-solving, and customer engagement.
Q5. How does AI contribute to hyperautomation?
AI helps systems understand patterns, make intelligent decisions, identify anomalies, and provide predictive insights.
