AI and RPA: What's the Difference and which is Best for Your Organization?

AI technologies that mimic and augment human behavior and judgment adjunct Robotic Process Automation technologies that replicate rules-based human actions. Both AI and RPA have gathered a lot of hype in recent years for their ability to drive swift and exceptional productivity, efficiency, and customer satisfaction gains. 

As per the figures, the RPA market is expected to reach $25.56 billion by 2027, and the AI market is forecasted to reach $390.9 billion by 2025. 

Despite their achievements and capabilities, organizations are still confused over what differentiates them and how they can increasingly be able to work alongside each other.

Today's modern and manifold enterprises are a mix of both simple and complex processes that need a crackerjack to handle a full range of workflows. Together RPA and AI can drive operational efficiency and an important role in transforming the company's way of working entirely. 

RPA can thrive in systems with a clear step-by-step flow, whereas AI can augment and improve human decision making in complex processes. Not just it, there is more to understand about the two. Let's elaborate on them separately to comprehend in a better way. 

How Does AI differ from RPA?

AI is an adequate companion to RPA robots. RPA and alongside AI can expand automation into all sorts of new areas allowing you to automate more and complex tasks. 

AI, with its intelligence, can handle complex processes that previously were done by humans manually. Due to AI, where machines can make cognitive decisions using large sets of data to predict several possible outcomes. 

AI can go beyond execution to thinking, such as understanding documents, visualizing screens, comprehending conversations, discovering tasks and processes to automate, processing language, and handling semi-structured or unstructured data.

AI is a revolution that builds machine learning models for more efficient business and enhances the human experience, not replace it. This automation is a more practical application of AI in the workplace. 

RPA, on the other hand, is capable of handling repetitive rules-based tasks, but unlike AI, it doesn't learn anything further from it. For example, if something changes in an automated task the RPA bot won't be able to figure it out on its own. 

Understanding Intelligent Automation 

You may have heard of the term intelligent automation or will likely hear in the future. This intelligent automation can be seen in the form of a digital worker capable of assisting the organizations as an adept employee.

Sophisticated AI algorithms are efficient in transitioning software bots from automating specific processes to fully cognitive business assistants capable of handling all kinds of repetitive tasks in real-time and freeing humans from mundane work. This helps humans think more creatively and progress strategically as routine tasks will be handed to bots. 

So, what is RPA good at, and where can you use it within your organization?

RPA Services and Capabilities

Robotic Process Automation is a core automation technology that lets robots interact with the digital system to relieve human mundane, regular, and time-consuming tasks. 

RPA is best suited to handle rule-based processes where workflows don't change over time or require human intervention. RPA is proficient in handling some common processes such as logging into applications, connecting to system APIs, copying and pasting data, extracting and processing structured content from documents, opening emails and attachments, and scraping data from the web. 

By implementing RPA, companies can build an automation scaffold to support their full ecosystem of automation tools. As per Gartner, RPA paves the way to be among the top 10 strategic technology trends of 2020 inclusive of AI, machine learning, and process mining.

RPA Implementation Best Practices

The best practices to get the most out of integrating Robotic Process Automation are -

Focus on Outcomes 

Just following the technology trends as everyone is following will not work instead, you need to have a clear goal to implement the technology. 

With the help of effective governance, identifying the areas of deployment of the technology, and continuous monitoring will determine the effective outcome. 

Treating technologies like Digital Worker

Another way to deploy the latest or disruptive technologies is by considering them as digital workers. Holistically viewing RPA with different skill sets, much like humans. 

Ingraining RPA and considering it as a part of the organization helps translate into more work capacity for the organization and improved work/life balance of the employees. 

How AI and RPA can Work Together 

Whether or not you think of RPA as a part of AI or not together, they are going to revolutionize the way businesses run. Today the cloud-based APIs and common data formats had a major leap in enabling all kinds of services to talk to each other in automated workflows. 

Alongside AI, RPA is set to explore and boost new capabilities. The current characteristics of AI, Deep Neural Network, have combined tools with RPA toolbox in vision and language tasks. With this, RPA workflows can enable its capabilities at decision nodes where earlier it was difficult to do. Moreover, this ability will also enable documents and images viewing holistically with the help of algorithms and interpret the downstream logic and routing. 

So when you merge RPA with AI disciplines like natural learning processing or computer vision, the possibilities grow for efficient and effective automation. Other amalgamation benefits are automating more complex, end-to-end processes than ever before and integrate predictive modeling and insights into these processes to help humans work smarter and faster. 

When to Switch in AI and Roll Out RPA

Every organization will have to analyze and identify the process of handling the processes, whether it should be RPA or should it be AI. 

You can start by tackling the processes that can be mapped in your mind. When you analyze the processes that aren't successfully running under RPA, you will find that you can add AI to workflows for such complex processes. This addition to the Robotic Center of Excellence will give you a quick win in the transformation. Also, it will help build an automation foundation that you can scale with AI later. 

RPA is essential in your organization if you want to weave AI in your core processes seamlessly. RPA cleans the underlying process, which gives an integrated framework without any friction to ingrain and instigate AI on top of your existing digital systems. 

The next layer to simple processes to accommodate the deemed complex task, requiring AI candidates includes-

• Workflows whose goals or outcomes cannot be 100% forecasted relevant to time, such as loan defaults, inventory forecasts, and many more.
• Variable processes not relying on clear and set rules such as resume matching, purchase decisions, and language translations. 
• Processes relying on the unstructured data from images to videos to documents such as invoice extraction, email routing, and speech to text.

These technologies, as earlier mentioned, are revolutionary and, when uniquely positioned, can help achieve optimal, automation-enabled business that drives return on investment (ROI) like never before for a new future of business. 

Conclusion

In the real world, when we see AI is already supporting readmission prediction in healthcare, pricing optimization in retail, fraud detection in financial services, and deal guidance in process services. These are just a few, as we automate more & more operations, you will notice the need for higher-level judgment where you will need data science skills. To integrate AI, you will need RPA integration first, and for that, you must consult a Robotic Process Automation Company.