How AI, ML can Enhance RPA in Companies?

Robotic Process Automation (RPA)

The world around us is rapidly changing and evolving through digital transformation. It is witnessing some of the most fantastic use cases of the technology. When robotics have become the talk of the town, AI and Machine learning are also further helping with the RPA phase. With the objective to automate the most mundane, repetitive, and time-costly tasks, this sector has seen significant growth over the past few years. And as these disruptive technologies mature over time, when unified, they shall mark an age of having intelligent, more resourceful robotics models.

 

Symbiotic Relation with AI, ML

 

Before further illustrating how these technologies can propel innovation and productivity, let us decipher these buzzwords. Robotic Process Automation allows for the configuration of computer software or a bot to replicate human actions and tasks, especially those that are repetitive in nature, only done substantially better and lesser error margin. AI refers to training computers to behave in a way similar to humans. Lastly, machine learning which is mostly heard in conjunction with AI empowers machines or computer models with the ability to ‘learn’. While RPA provides a very quick non-intrusive means of automation, it has its limitations. This is because its’ automatable part runs on pre-defined algorithms, which do not make it ‘smart’. When one uses AI, decision making augments RPA. And ML helps build a knowledge base based on historical data (supervised or semi-supervised cases) and uses it for decision making and prediction. In other words, it activates an AI system’s ability to detect and analyze data patterns on its own, and to ‘learn’ from those patterns.

 

So when combined, these three technologies can give an intelligent and efficient driver to push digital transformation in enterprises by increasing productivity, reducing cost, and eventually leading to revenue and business growth. Therefore, they must be intelligently orchestrated as tools for business process automation and education to occur. The main thing companies’ IT team must do is identify areas that need to be automated and which areas need AI and ML decision support.

 

Challenges

 

Since the enterprises are becoming more open, allowing their products and technologies to be better integrated and share data, RPA is exposed to more data. Although AI and ML help to make sense of this data by analyzing, comprehension and drawing conclusions from data formats, there are certain hurdles faced too. These include setting unrealistic expectations coupled with failing to train the staff with the knowledge of operating these tools to optimize their work streamlined with the goals. The relative immaturity of the automation market makes it hard for companies, to learn about, and to adopt RPA at scale. 

 

Even though the support for these technologies exists in C-level, for assuring long term backing and financial investment, the IT and Data science departments must produce successful implementations of these technologies that return tangible business benefits. Along with it, they must educate non-technical C-level management on the differences between RPA, AI, and ML tools and how all of these tools come together in a business process or operation. Other than that for successful usage of the above technologies companies must aim for co-operation among the vendors for using their tools, foster integration, and thereby scale solutions that cater to the business goals.

 

Outlook

 

As it becomes more evident that RPA holds the potential to transform our society. If implemented correctly, with AI and ML, it shall unlock the ability to empower organizations to modernize the way they function. It’s just a matter of time before machines start developing compelling insights from everyday business processes.