What to do when Robotic Process Automation is not enough

With robotic process automation (RPA) tools, companies can automate human tasks with software, as a means to capture greater operating efficiencies and realize cost savings. In the financial services industry, this might mean using software to process loan applications, or to approve and fulfill a credit card request within a much shorter time frame, increasing customer retention and satisfaction. 

However, without a means for interfacing with underlying platforms, nor as part of a business process automation strategy, the immediate and more durable benefits of RPA cannot be extended. How can companies overcome the current limitations of RPA and find scalability? In this post we’ll walk through a little background on RPA and where to turn when it’s not enough.

What RPA can do, and what it can’t

When highly repetitive tasks are part of the enterprise operational environment, they often compete with and direct attention from other, important problem-solving and customer-centric work—thus the benefit of automating them. RPA can automatically input (or extract) pertinent data for usage in other applications or systems, defined by the patterns that (recorded) manual tasks perform.In addition to potential financial savings from automating manual tasks, RPA can also help increase precision by limiting human error. 

But, is it enough? 

While gains can be obtained by automating repetitive processes and tasks,  RPA approaches constraints because it’s purely task oriented (which is especially illustrated when it tries to coordinate among processes that are complex or have dependencies). 

However bright their initial promise, standalone RPA implementations will exhaust the scope of benefits when not taken into consideration with the underlying platform or a true automation mindset.. 

An extended implementation and vision

In order to realize maximal advantages, operational enhancements should go beyond surface-level implementation and be used across the underlying architectures, facilitating more complex, conditional actions and enabling multiple outcome scenarios as part of strategic decision-making processes. RPA functionality wasi initially limited to desktop-based tasks, given its principally designed function to mimic and reduce manually generated keystrokes rather than true end-end process enhancement. 

As the process discovery has matured, RPA’s limitation  as part of a larger business process automation strategy has more clearly been brought to light. It is ill-designed to be the means to realize broader business automation improvement, especially as transactional volumes scale to the operational demands which are present within current - and future - global financial institutions . 

Building process automation on a cloud-native foundation

To attain that level of outcome, automation tools should be able to interact with cloud-based enterprise data—event monitoring, gathering information, and automating tasks and entire workflows, indeed even interacting with applications using APIs. 

Ultimately, platform automation can be realized with layered artificial intelligence and machine learning (AI/ML) monitoring and functionality, which can even help discover inefficiencies for automation in addition to performing tasks with little human interaction, capturing greater reliability, speed, and cost savings over legacy workflows.  

This is especially true in the financial services industry, where improved operational efficiencies must help mitigate the expense of multiple levels of unique regulatory oversight in addition to the ever-present technology challenges which are universal to other sectors as well.

However tempting, it would be unwise to rely on processes of the past and legacy operating procedures in a complex digital environment of applications, data repositories, and integration components. Even with some ability to extend infrastructure utility, the true issues might be centered on an aging deployment near the limit of viability, so it is wise to avoid merely re-engineering existing processes and workflows . 

Organizations must ensure the underlying infrastructures are both robust and adaptable to keep pace with present and future business demands—by directing focus on building an open cloud-native foundation first.  With business needs continuously morphing, migration issues and integration lock-ins can be especially cumbersome and unadaptable. The open source environment lends itself to the flexibility in vendor selection and accommodation as demands evolve. As organizations focus efforts to deploy innovative technologies across the enterprise rather than focusing on tasks where RPA will continue to have serviceable elements, solutions like Red Hat Process Automation Manager can help. 

Process automation efforts, which surpass RPA limitations—especially with reduced monitoring requirements and functionality beyond keystroke substitution—can achieve magnification of utility, and potentially fulfill a higher level of effectiveness and productivity enterprise-wide. These process automation investments are of high value and should be prioritized accordingly. 

Additionally, since the financial services industry is one which is especially reliant on the establishment and maintenance of  trusted relationships, the development of open source (and inherently flexible) supporting elements as described, contributes directly to business resiliency and reliability factors. 

Red Hat Process Automation Manager

The potential to evolve and adapt as business demands change is a direct function of the robustness, interoperability, and adaptability of the underlying enterprise platform.Red Hat Process Automation is a platform for automating business decisions and processes—which include Process Automation Manager, Decision Manager, Business Resource Optimization and Complex Event Processing (CEP) technologies. An approach involving containerization, Process Automation Manager can assist enterprises towards modernization of legacy applications, creating and migrating them to cloud-native applications.

With an ability to develop and run them more consistently, and at scale, they can gain the agility and flexible management which is optimized in a cloud strategy.  With these components, backed by the flexibility of open source platform elements, end-to-end transformation in workflows are possible. In addition, in business rule management, Red Hat Decision Manager (also part of Process Automation Manager), can help with managing procedures on a rule basis and automating deficiencies in data entry items and correlation checks.

Conclusion

However promising the  benefits in automating human centric tasks, challenges with RPA’s scalability and potential as a tool to contribute across an enterprise-wide interoperable platform are evident, especially as other promising, cloud native technologies emerge. 

With automation initiatives and their integration forming increasingly important underpinnings in  highly streamlined organizations, cloud-native, open source infrastructures can lay the foundation for successful process automation. 

Learn more about how Red Hat Process Automation Manager has helped other organizations realize efficiency gains as described here.  

Further exploration about Red Hat Process Automation offerings is provided here.