Automation has been a cornerstone of business innovation for centuries, evolving from early mechanisms such as the waterwheel and windmill to the revolutionary impact of the steam engine and electricity. However, the automation process was never designed to be hands-off, such as leave it running and walk away. Monitoring, management and upgrades remain part of the process, including artificial intelligence (AI). As with all automation throughout history, AI has set out to improve efficiency, productivity and convenience, but it still requires human partnership to ensure stability.
CTO and co-founder of Reveille Software.
Robot process (automation?)
Robotic Process Automation (RPA) has entered today’s business world to perform routine and mundane tasks. It serves the same purpose that automation always has: saving time and resources while increasing accuracy and productivity. In the case of RPA, it performs the routine tasks necessary to access the Intranet, Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), Accounts Paid/Payable (A/P) and Human Resource (HR) applications. ). .
RPA now handles the processing of invoices, payroll and claims. These tasks include extracting information from digital content stored in document repositories or during content capture processing. The goal is process efficiency, better data accuracy and lower costs.
However, users do not need to have low-level programming or coding skills to take advantage of RPA. This lack of coding knowledge does not mean it is a hands-free operation; monitoring and upgrades are necessary. With this in mind, we explore the management, operational and governance gaps an organization can have when running a digital workforce.
Map the gaps
To use RPA effectively and appropriately, a comprehensive framework for managing the RPA platform is needed. This management includes knowing the roles, responsibilities, and other processes associated with the RPA operation. For RPA to function properly, it is necessary to close so-called ‘platform management gaps’ for actively managing changes, incident management and Service Level Agreements (SLAs). For these reasons, it is necessary to ensure that management provides insight into:
Performance statistics: Performance metrics include robot usage, throughput, cycle time, error rates and cost savings.
Robot target applications: Information about the service level for the applications that the robots automate, such as application availability, performance and service levels.
Operational insight: Understanding how the RPA platform works, such as the uptime of the RPA platform, the health of the RPA database and the number of system errors.
IT professionals want to complete tasks without the guesswork. Therefore, insight is critical to effectively managing and optimizing RPA program performance and effectiveness. The set of rules, processes and controls established to manage and oversee the implementation, operation and maintenance of RPA is referred to as ‘governance and oversight’.
Governance and oversight relate to RPA scalability, or RPA’s ability to handle an increasing number of processes, transactions, and users. Before scaling RPA operations, IT staff must carefully consider infrastructure, resources, capacity, and more. Here is the essential checklist to follow before any expansion takes place:
- Is the scalability of RPA hindered by the support of the RPA infrastructure?
- Is RPA scalability hindered by the way your RPA platforms are currently managed?
- Do some automated RPA processes require additional resources at a level of complexity?
- Does your RPA integrate with legacy systems with limited capacity?
- How do you know who has changed or updated the RPA environment?
It is important to note that data privacy, security, and compliance risks will all increase as the RPA program grows. Make sure you know who has the administrative-level control to manage and change RPA configuration and access.
While RPA platforms can have a positive impact, they require time to adapt to changes in target applications. Updates, new versions or replaced applications that are crucial for robot processes can cause malfunctions and incorrect data. Error management in RPA automation is usually a major challenge. Therefore, an effective governance and oversight framework for RPA programs is critical.
As the RPA program grows, software tools will help close these potential gaps in the management and governance of RPA platforms. Third-party tools provide the insight, control, and oversight needed to mitigate risk and ensure the highest ROI from the RPA investment. Some of these added tools provide agentless management capabilities to help ensure the continued health and productivity of RPA platforms. These capabilities ultimately provide organizations with a comprehensive framework for actively managing roles, responsibilities, and processes associated with RPA. In addition, they also provide the ability to manage robots at scale, know their performance and receive automated notifications when problems arise.
Know your basic stats
In terms of RPA, benchmarking evaluates and compares the performance, efficiency and effectiveness of an organization’s system against established standards, best practices or competitors. It leads to informed decisions before adding capacity for upgrades, new deployments and migrations. To establish a practical and useful benchmark, it is crucial to have access to RPA metrics such as:
- User levels
- Record processing levels
- Repository activity
- Transaction performance levels
- Database activity
- Application server activity
- Workflow processing volumes
- Transformation processing volumes
- Service levels of the ECM platform
- Service levels of the RPA platform
- Service levels for application integration
IT applications are constantly changing. As an organization grows and evolves, evaluating the service levels, performance, and baselines of RPA systems becomes essential.
Conclusion
There is no doubt that RPA will benefit your organization in the same fundamental way that the water wheel increases the efficiency of grain milling. In the case of RPA, however, organizations can’t just plug it in and let go, even though “automation” is part of the name. Users need to close any possible management, operational, and governance gaps with added software tools, understanding roles and responsibilities, and benchmarking. This process ensures that you get the best ROI as the system functions and grows. Rule of thumb: you can automate, but you still need to participate.
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