What You Should Know About Robotic Process Automation and Artificial Intelligence2019-04-24T11:38:25.000Z 2019-04-24T11:38:25.000Z Robotic Process Automation (RPA) and Artificial Intelligence (AI) kindle interest lately due to being a tangible asset for the enterprises that move towards full-scale digital transformation. What are the benefits of utilizing these technologies in your product?
From the very first jump, Robotic Process Automation was intended to automate time-sapping and laborious operations performed manually by human personnel, however, these days it enables the companies decreasing human faults to amount and elevating efficiency according to multiple business parameters as soon as financial loss and management risks are mitigated. Nevertheless, RPA has one disadvantage which is a disability to cognize data patterns and trends being processed automatically. And this is where Artificial Intelligence comes to aid.
Nowadays RPA and AI kindle huge interest being a tangible asset for those enterprises realizing the value of full-scale digital transformation. While both of them are cutting-edge techniques, they can also cooperate effectively delivering outstanding synergetic benefits.
In order to leverage the collaboration of RPA and AI, it is worthwhile to perceive the difference between them, and, in turn, the difference between those advantages they can deliver. Although RPA is usually considered to be AI sub-set, they are not the same. Robotic Process Automation was meant to automate iterative operations executed manually, for example, handling blanks, reports, bank statements, etc. In this context, RPA has to recognize data in hard copies and to convert it into electronic format. Document turnover automation allows reducing workload on human personnel and allocating efforts to more creative tasks accomplishment.
Indeed, some features of RPA give the possibility also to perform slight corrections in data being virtualized, for instance, when medical records digitization, still this function scope is very limited. Somehow businesses drift toward RPA since this technology gives a possibility to unlock more human workforce potential and to make human error occurrence less frequent as the automated system is not inclined to shift away from technical specification requirements. The digital experts predict that the RPA market volume will exceed $ 3 billion by 2025.
Meanwhile, Artificial Intelligence maintains operations at higher cognition level, such as speech recognition, image analysis, and behavioral pattern forecast. AI-based solutions are so popular that McKinsey expects market growth to be $ 3.5 to 5.8 trillion per year. The developers create algorithms based on the vast volume of data to train AI applications, and in turn, the applications study it by seeking relevant trends. As long as the algorithms are becoming more sophisticated, AI applications become more complex and functional.
RPA can facilitate AI training process
AI-based applications can perform complex operations and possess cognitive capabilities only thanks to the developers coding sophisticated algorithms into the software. At the same time, RPA can be employed for those data collection required for AI learning. For instance, RPA can perform screen monitoring to gather data from various websites automatically, like all legal updates for a certain period of time, and this data can be utilized to develop algorithms for further AI application training.
As long as the development of productive Artificial Intelligence algorithms requires a huge amount of data, by gathering it, Robotic Process Automation solutions will facilitate AI progress and free up additional human efforts for algorithms generation and application training instead of data aggregation.
Artificial Intelligence can bring cognition to RPA
Yet, Artificial Intelligence can be combined with Robotic Process Automation to deliver better performance. RPA will progress while gathering data and distributing it with other elements, in particular, AI. Besides, collection of information, deployment of algorithms for decision-making, requesting Artificial Intelligence systems to define a sequence of operations are also helpful in RPA development facilitation. For example, if you call a courier service company for the purpose of tracking parcel delivery status, RPA can give you this information. However, when RPA is joined with AI system, Artificial Intelligence can provide voice responding service with the ability to perform more creative actions, for example, if understanding that the customer wants to speed up parcel delivery, RPA can offer him an express delivery service, explain its benefits, price and shipment terms. So, next time given individual is likely to use this option to order the goods via an online marketplace.
Application development concerns
As long as the business and innovative benefits of pairing RPA and AI while application development is clear, production of software for given applications is quite challenging for software developers. The main difficulty is that combined development needs several teams focused on RPA and AI operability, and the effective communication between these teams has to be established. Thus, scheduling and interaction are crucial, all steps have to be recorded and displayed to all team members in order to avoid miscommunication. Moreover, software development and QA specialists should focus on RPA and handle it thoroughly because of its high data processing velocity. If a mistake occurs in a piece of RPA algorithm, it will bring lots of troubles within an extremely short period of time.
The advantages of application development pairing RPA and AI are quite convincing since both technologies complement each other productively. This combination enables enterprises to leverage more advanced RPA solutions which are capable of maintaining client cooperation and forecasting future trends based on existing behavioral patterns. Data experts can also benefit from the deployment of RPA and AI combined products as long as they can find huge volumes of relevant data to train Artificial Intelligence modules for more complex tasks accomplishment within condensed timeframes and for development lifecycles facilitation. In fact, since the consolidated development approach calls for tighter collaboration between several teams, it also unlocks broad potential for innovative software delivery.