Robotic Process Automation (RPA) technology is well-regarded for its capacity to simplify repetitive tasks, minimize human errors, and lower operational costs. Many companies from different sectors have experienced early successes by using RPA to automate their processes.
Limitations of RPA in GBS
However, we may be witnessing a shift in priorities. According to a High Radius' report, "Will Robotic Process Automation (RPA) in GBS Be Dead in a Decade?", we can clearly identify and agree that the key limitation of RPA is its reliance on standardized data and uniform processes. Even a slight deviation in data format or process flow can cause an RPA bot to fail, requiring human intervention to reformat the data or an extensive IT effort to reprogram the bot for adjustments.
While GBS divisions may be realizing impactful benefits from the processes they have automated, many still struggle to achieve the full potential of their investments. The three biggest cost components eating away at their ROI is:
Total cost of ownership
Cost of RPA development and deployment
RPA maintenance and support
Thus, the technology is not yet capable of functioning independently or replicating the higher-level thinking and actions that humans can perform.
The Need for a Holistic Approach
To fully leverage the power of technology, it's essential to implement a cohesive digital strategy that combines RPA with other technologies. Intelligent automation encompasses the extensive use of artificial intelligence (AI) When RPA is integrated into a thoughtfully designed intelligent automation framework, business rules and logic can be seamlessly incorporated into workflows to tackle more complex challenges.
IBM has noted that for RPA tools in the market to stay competitive, they must evolve beyond simple task automation and broaden their capabilities to include intelligent automation (IA). This form of automation builds on RPA's functionality by integrating sub-disciplines of artificial intelligence (AI), such as machine learning (ML), natural language processing (NLP), and computer vision.
Most organizations stand to gain from technologies that can automate digital work tasks while also making intelligent decisions within those tasks. Consultants and system integrators are increasingly providing support for these combined solutions and often collaborate in partnerships.
A compelling case study demonstrating the successful integration of RPA and AI is Maybank’s digital transformation during Malaysia’s Movement Control Order (MCO). With a surge in digital transactions and users, Maybank Group had to rapidly deploy advanced technologies to maintain business continuity and enhance customer service. The bank prioritized automating its back-office operations by implementing machine learning (ML) for credit applications, branch operations, remittances, and trade services. Additionally, it revamped its technology stack, adopting robotic process automation (RPA), intelligent character recognition (ICR)/optical character recognition (OCR), and application integration to optimize operations.
Thus, Intelligent Automation and RPA are not competing technologies but complementary forces in digital transformation. AI enhances RPA by enabling it to automate tasks more fully and handle more complex scenarios, while RPA ensures AI insights are implemented quickly, rather than being delayed by manual intervention.
Conclusion
While Robotic Process Automation (RPA) has revolutionized business operations, it is not a standalone solution for achieving a complete digital transformation given how quickly technology is advancing. To unlock the full potential, organizations must adopt an integrated digital strategy. The future of automation lies in intelligent automation (IA), where RPA and AI work in synergy to address more complex challenges. By embracing an integrated approach to automation, GBS can drive innovation, improve operational resilience, and position themselves for sustained success in an increasingly digital world.
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