Use of robotic process automation (RPA) is increasing within industries, with the technology now commonly taking advantage of artificial intelligence (AI) capabilities to learn lists of actions to ultimately automate tasks.
In fact, in the industrial and manufacturing industries, RPA already works to streamline back-office and operational processes. Many businesses across the globe have now turned to the technology to handle the assembly of products, quality control measures and packaging processes.
RPA is based on the use of software robots, which when implemented correctly, make better use of employee time, reduce costs, increase efficiencies, and provide improved analytics. Utilised on their own however reveals visible limitations, and its true potential is yet to be realised. With this in mind, what do industries need to address in order to apply the technology effectively?
Expanding the capabilities of RPA
It’s important for organisations to begin with acknowledging the current limitations of RPA technology. Although it is highly effective at replacing repetitive tasks due to its understanding of structured data, at present, it struggles to process unstructured content and data. Moreover, the algorithms and data recognition patterns that are used by the majority of RPA tools are based on a fixed rules set and structured information. The lack of fixed patterns and the difficulty that comes with predicting the quality of unstructured data means that use cases of RPA have traditionally been restricted.
This is where AI comes in. AI makes the perfect partner to RPA by solving the puzzles associated with unstructured data. Due to this effective combination, more RPA providers are offering a mix of both technologies via intelligent RPA or cognitive RPA services, which can be applied across a wide range of applications in numerous industry sectors.
Exploring the use cases
To truly benefit from the technology, organisations that are considering a cognitive RPA integration need to closely assess where it should be implemented and whether suitable processes are already in place. This is where organisations should take advantage of process-mining tools or task-mining tools to gain transparency on how particular processes work, and then devise the best use cases and categorise these processes into structured and unstructured data.
The technology can be applied to a range of industries, from core uses in data validation, to marketing processes such as lead nurturing, invoice creation delivery and CRM updating in sales departments, and quote-to-cash and procure-to-pay processes in finance. When applied and used in applications in businesses that have procurement and logistics operations at their core, sectors such as these will benefit the most from integrating a cognitive RPA solution.
It’s clear that businesses that already have transparent processes and workflows in place, including a consolidated IT landscape, will benefit more than others when combining RPA and AI together. With organisations able to know where they can benefit from a cognitive RPA application, there are some key obstacles to overcome to ensure the performance of the technology can be fully realised. Tackling any fragmented processes and tasks, disjointed workflows, lack of data classifications and high upfront costs will go a long way in removing the hurdles that may stand before an effective RFA implementation. Processes mining tools/ task mining tools can be helpful in building processes and tasks into continuous workflows for organisations. The outcome of using these tools can be applied to carry out data classifications and improve data quality for RPA tools.
In order to scale integrated RPA systems effectively, organisations could also benefit from implementing automation platforms (IAPs) to bring infrastructure technologies together. These platforms could go a long way in tackling the rapidly changing regulatory requirements, complex licencing structures and high and low demand periods in industries such as manufacturing that can commonly stand in the way of efficient scaling of RPA.
The future role of cognitive RPA
Without a doubt cognitive RPA will have a vital role to play in many industries in the future, where the plethora of benefits such as transformed efficiencies and saved costs will be hard for businesses to ignore, especially as market competition increases and financial pressures make an impact in the coming years due to the COVID-19 pandemic.
It is however vital for organisations to take the first step of ensuring they identify the areas of where RPA will have the biggest impact, and then strategize the removal of any obstacles, particularly in the area of problematic data processes. IAPs, for example, are now maturing quickly and could make the difference in traversing scalability challenges in the future. Organisations can then be on track to reap the numerous rewards of the combination of RPA and AI, which is undoubtedly more effective than the use of RPA on its own.
About the Author
Arpit Oberoi is SAP Senior Lead Expert at delaware. delaware is a fast-growing, global company that delivers advanced solutions and services to organizations striving for a sustainable, competitive advantage. We guide our customers through their business transformation, applying the ecosystems of our main business partners, SAP and Microsoft.
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