The Ultimate Power of Cooperation from Combining AI and RPA Together
JULY 25, 2019
The modern world economy is moving at a faster pace due to the goodness of digital transformation. The main agenda behind the development of robotics process automation is to automate the manual tasks which take time to execute. In the current business scenario it helps to boost productivity and cut off any human error. The only drawback you will find with RPA is its inability to be intelligent. This is where artificial intelligence gets involved with the process.
The combination of RPA and AI is bringing about tremendous changes at the business level by offering it amazing competitive edge in the market. Again this can only become possible when the powerful tools are used in the right manner. They have the capability to build together complementary cooperation of greater effect.
Most of the time RPA and AI are talked about together, but it is a fact that they both are very different processes.
The focus of RPA development is to take care of repetitive manual tasks thereby release human resources to take care of other important tasks. By using the automated system as this, you will be able to easily take care of tasks like entering information in the field forms or take care of the tasks that have more chance to be erroneous. One of the studies predicts that by 2025, the compliance and accuracy of RPA will offer the market a growth of $3 billion.
On the other hand, AI is focused on decision making, intelligence which includes image and speech recognition and prediction methods. By keeping the demand of technology in mind, McKinsey predicts that artificial intelligence is powerful enough to deliver around $5.8 trillion a year.
As per data scientists the programs which run on AI are more of independent in terms of learning from data-based algorithms and patterns to turn themselves smarter.
If the software rules are developed by data scientists in complex manner, then AI is capable to handle it and break down its complexity. It is capable to reach a different level of intelligence while carrying out this process. On the other hand, you can have RPA used to collect large amount of critical data which AI will need to work.
When it comes to building effective AI programs, gathering large amount of data has always been the challenge faced. This is where RPA takes up the limelight. RPA is capable to easily collect large amount of data as much as you need all the while helping to speed up the development of AI. This way the repetitive tasks will be taken care off thus releasing the scientists to take care of crucial tasks like coming up with algorithms which are needed for apps based on AI.
If we are taking a look at the collaboration formed by the two technologies then we can for sure say that they go hand in hand as far as business is concerned. When AI and RPA come together they get tremendous power to complete toughest tasks with utmost efficiency.
By working in collaboration with AI, RPA tends to keep on improving. It gathers information and share it with systems in order to make better decisions. In order to understand the foremost proposal of a business, RPA programs can make use of the information from AI-program.
- For software engineers, developing applications in combination of AI and RPA can bring up several issues.
- There is a need of joint effort from more than one team to take care of AI and RPA functionality.
- The teams working on the project need to have good coordination.
- The working teams needs to have right level of planning and communication.
- In order to convey the information of development process to others, there should be a record of actions.
- Developers and other professionals should be more attentive as RPA can churn in more information.
- You will end up in great trouble just through a single course of error.
If it is possible for the business to overcome the above mentioned challenges and get ahead with the application which is a true combination of AI and RPA, then for sure they will be able to taste success. Such kind of applications can help the business to take care of customer interactions well and for better customer-service relation, predict the behavior of customers. This also helps the data scientists to collect as much amount of data as needed to build applications which are more complex and comes with higher development cycles. Moreover, for software engineer working with multiple teams on the same project opens up more opportunities.