According to Grand View Research, the robotic process automation (RPA) market was valued at $1.4 billion in 2019. It’s expected to grow to $13.74 billion by 2028. This expected growth comes as more organizations use automation to achieve productive business outcomes, such as increased efficiency, productivity and revenue.
In today’s competitive business environment, your organization is constantly looking for ways to increase productivity and profitability. One of the most promising areas where you can focus on driving such business outcomes is through Ai decision engines. These are powerful tools used by a variety of organizations to accelerate RPA.
Leveraging Ai Decision Engines for Accelerated RPA
Artificial intelligence (Ai) and robotics process automation (RPA) are essential technologies for modern enterprises. Ai provides your organization with an unprecedented ability to scan data, instantly analyze it and make decisions based on the analysis.
Meanwhile, RPA technology automates the tasks of humans by running them through computers. When combined, Ai and RPA technologies have shown great potential in speeding up repetitive processes for enterprises.
During episode 10 of System Soft Technologies’ LinkedIn Live Series Ask an Expert, featured speakers James Duez, CEO at Rainbird Technologies, and Thomas Helfrich, Vice President of Intelligent Automation at System Soft Technologies, discussed and debated leveraging Ai decisions for RPA acceleration. They talked about why you need it and why now.
Ai decision engines can be a powerful way to accelerate your RPA projects. Learn how they work and the benefits they can offer your organization.
Here are 7 ways to use Ai decision engines for accelerated RPA.
1. Automated Data Analysis
A decision engine’s machine learning (ML) capabilities can help you automate tedious data analysis tasks humans often do manually. It frees up your human resources to focus on more valuable work.
In this example, a decision engine can analyze your data and answer a question without human intervention. An RPA bot can then immediately automate that task after receiving the results from Ai.
2. Process Automation
While Ai decision engines only provide the results of your automated data analysis, RPA bots allow for immediate process automation. Using a decision engine to automate and analyze your tasks can speed up business processes and free up your human resources typically required for this work.
Organizations may have ML and Ai technology to analyze data. But not RPA bots to automatically automate processes.
In this case, the decision engine will first process your data analysis before triggering a manual or more advanced automated system to step in and take over the task.
3. Elimination of Errors
Decision engines prevent errors that can harm your organization or customers. More complex Ai technologies like ML and deep learning help decision engines more precisely recognize patterns in your data. This results in fewer costly mistakes for your organization when using these systems.
By design, RPA bots must never make the same mistakes humans often do.
Automation of your tasks through RPA bots reduces the potential for human error, which is less likely to happen with a machine than it is with a person.
4. Reduced Time to Market
A decision engine can help your organization speed up time to market for products and services. It’s especially true when you use RPA bots. You can automate processes, without requiring human intervention or guidance after the initial analysis has been completed by a machine learning-enabled Ai system.
Once again, your organization may have RPA bots capable of performing tasks without human intervention. But without Ai technology, you can’t analyze data and make decisions. That’s where the decision engine will first perform its automated analysis before passing on any processing work to your RPA bot automation.
5. Reduced IT Support Requirements
Decision engines can reduce the need for human intervention. Your organization can reap significant cost savings of support costs by implementing Ai decision engines. These can automate your tasks with RAP bots, without requiring additional work from your staff.
Ai technologies like ML and deep learning require less hands-on maintenance than some older automated technologies. Typically, RPA bots need less maintenance than systems that require human intervention after being set up and deployed.
6. Dynamic Performance Capabilities
A decision engine can help RPA bots dynamically adapt to changes in your business environment. That’s particularly true when combined with Ai technologies that autonomously make decisions and continuously learn from their surroundings over time.
Some Ai technologies like ML allow for continuous learning, as they gather data about your business environment and make decisions.
Decision engines capable of performing automated tasks without human intervention can then pass those completed tasks to RPA bots for execution. These are directed by the decision engine’s Ai technology, such as cognitive ML or deep learning algorithms.
7. Expanded Decision-Making Capabilities
Decision engines allow RPA bots to make decisions that go beyond the capabilities of Ai technologies like ML and deep learning. It’s especially true when used with other types of automated systems, such as SaaS or cloud-based software, which offer more complex forms of interactivity.
Decision engines help organizations efficiently automate tasks that are based on a fixed set of outcomes.
They can also facilitate the development of RPA bots that act autonomously with little or no human intervention required after deployment.
This allows your enterprise teams to complete more work in less time. Ultimately, your organization saves money and improves overall productivity.
Ready to learn more about Ai decision engines for accelerated RPA? Then, it’s time to connect with Thomas Helfrich via LinkedIn or email. Thomas is standing by.
About the Author: Thomas Helfrich
Thomas Helfrich is Vice President of the Intelligent Automation Practice at System Soft Technologies. Tom uses Ai, ML and RPA to drive disruptive business performance and help organizations offset operational and financial challenges. His powerful knowledge of Blue Prism, Automation Anywhere, UiPath and Datamatics, combined with his expertise in the application of advanced technologies, spurs cost-effective business scale through automating processes and augmenting humans.