RPA Meets AI: A Synergy Revolutionising Business Automation

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Although RPA has been in use for decades, the recent advancements in AI could see the pair create a synergy that brings in untold levels of efficiency

With speed and efficiency becoming more integral to an enterprise’s ability to compete,  organisations are in an eternal search to find ways to do things quicker and better. 

Often, this is in the form of menial tasks that, although not overly complicated - like filing in an order form - are vital to a business's operations. 

This could be done with staff, yet the cost of doing that does not always match the returns of the output. Plus, should there be growth, the chance is that these time-consuming tasks grow too. 

From data entry and invoice processing to customer service inquiries and compliance reporting, these routine activities often become bottlenecks, hindering productivity and innovation. Therefore, businesses battling to find growth may be between a rock and a hard place.

Enter Robotic Process Automation (RPA), a transformative technology that promises to revolutionise how businesses handle repetitive tasks and complex processes. By leveraging software robots capable of mimicking human actions, RPA offers a powerful solution to the challenges faced by such scenarios. 

Examining RPA and AI’s dynamics

Although RPA and AI are often discussed together in today's business landscape, RPA actually predates the widespread adoption of AI in business processes. 

RPA emerged as a distinct technology in the early 2000s, focusing on automating repetitive, rule-based tasks without the need for complex programming. These early RPA systems were primarily used to mimic human actions in interacting with digital systems, such as data entry, form filling, and basic data processing.

Yet it is due to the current wave of AI that we see enterprises reexamining the decades-old system.

"AI and RPA are already playing a pivotal role in transforming business processes by enhancing automation with intelligence and adaptability,” says Michael McLaughlin, VP Cloud Alliances & Channel at SS&C Blue Prism. “This integration is enabling AI to empower RPA to handle more complex, data-driven tasks and move beyond simple, repetitive processes."

This technological synergy is not just changing how businesses operate; it's reshaping entire industries and redefining the future of work. The AI-induced evolution of RPA from a tool for automating routine tasks to an intelligent system capable of handling complex operations marks a significant shift in business process management. 

"This has led to smarter, end-to-end automation, reducing manual intervention and increased accuracy," Michael continues.

The financial sector has been quick to adopt AI-augmented RPA, leveraging its capabilities to enhance accuracy, ensure compliance and improve speed in critical areas such as fraud detection and claims processing. 

Similarly, the healthcare industry has embraced this technology to manage patient records, streamline billing processes and automate diagnostic procedures, thereby improving efficiency and reducing errors.

Beyond these back-of-house process automations, the synergy of RPA and AI has meant that RPA can now have a customer-facing role.

 "By integrating AI-driven sentiment analysis with RPA, we've been able to automate customer query handling and the review of customer interactions for coaching and training with a high degree of accuracy,” says Tim Peters, Chief Marketing Officer at Enghouse Systems. 

“The system not only processes routine inquiries but also gauges the emotional tone of customer interactions, enabling it to prioritise and escalate issues that require human intervention."

The journey towards synergy

However, the journey towards fully integrated AI and RPA solutions is not without challenges.

Many issues that are present with other uses of AI are present with this RPA version too, such as internal resistance to change and a skills gaps. 

"The primary challenge in integrating AI with RPA lies not in the technology itself – since both exist under a single platform – but in overcoming organisational limitations or frictions. Many organisations struggle with securing c-suite-level buy-in and scaling automation efforts successfully," explains Michael. 

Some issues are more technical in nature, but both concern data. “Data management and quality are absolutely critical in the integration of AI with RPA,” explains Tim.

AI systems rely heavily on large datasets to make accurate predictions and decisions. If the data fed into an RPA system is inconsistent or of poor quality, it can lead to errors that undermine the effectiveness of the automation.

This, when used in a healthcare setting, for instance, could have the potential to lead to disastrous results. 

Not as dangerous but equally as damaging are the security concerns. As RPA and AI technologies become more integrated and sophisticated, they gain access to vast amounts of sensitive data, potentially increasing what is exposed if a data breach occurs. 

"Organisations are often tentative in rolling out technologies like Gen AI due to compliance and security concerns, particularly when managing sensitive data,” says Micheal.

Combine that with the challenge of integrating legacy systems with modern AI and RPA technologies and many organisations may struggle to retrofit their outdated infrastructure to support these advanced technologies, or deem the task of digital transformation too large to tackle. 

Although the task is big, Peter explains how it is not insurmountable, saying: “Organisations should start by investing in robust data management practices. This includes data cleansing, normalisation and governance, to ensure that the AI models have access to high-quality, consistent data.”

Additionally, adopting a phased approach to integration, starting with pilot projects that target specific processes before scaling up can give companies clear goals, minimise disruption and ensure the proper foundations are set before building anything on top of.

Finally, partnering with experienced vendors which offer comprehensive support and integration services can help organisations navigate the complexities of combining AI and RPA.

“This collaboration can simplify the integration process by providing valuable expertise and proven methodologies, such as the ROM2, that address both technical and organisational challenges,” explains Michael.

RPA & AI’s next iteration

As businesses navigate these challenges, new trends are emerging in the realm of intelligent automation. 

The continual integration between AI, RPA and other advanced technologies will converge to automate complex, end-to-end business processes. 

"While still adopted by relatively few organisations, hyper automation is gaining traction as these technologies mature and organisations recognise its potential for driving efficiency and innovation," Michael explains.

Tim echoes this sentiment, predicting that hyper-automation will push the boundaries of what automation can achieve, enabling businesses to automate not just repetitive tasks but also complex decision-making processes.

Looking ahead, the vision for RPA and AI integration is ambitious. A fully autonomous enterprise where manual intervention is minimal and processes are continuously optimised through AI-driven insights is the promise a fully realised synergy between the two can offer.

Yet, to usher in this vision, organisations must invest in scalable technologies, prioritise data quality and foster a culture of innovation.

"Organisations should focus on strategic alignment, ensuring that AI and RPA initiatives support long-term business goals,” says Michael. “Continuous upskilling of the workforce and collaboration with technology partners are crucial for staying ahead of technological advancements and ultimately achieving improved resilience, agility and a competitive edge within the industry."

From enhancing customer experiences to optimising internal processes, the integration of these technologies promises to redefine how organisations operate in an increasingly digital world.

Whilst challenges persist, including data quality concerns, security issues and the need for organisational adaptation, the potential benefits far outweigh these hurdles, and in today’s business landscape, businesses not taking on the challenge may be left behind. 

“By taking a proactive approach to AI and RPA integration, organisations can ensure that they remain competitive in an increasingly automated world,” Tim concludes. 

To read the full story in the magazine, click HERE.

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Michael McLaughlin
Tim Peters
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