As more businesses embark on digital transformations and begin exploring ways AI can be utilized, combining AI with robotic process automation (RPA) stands out as a way to unlock a massive return on investment.
AI compliments RPA, which helps eliminate repetitive processes from a worker’s day-to-day experience. With RPA or software robots handling the more mundane tasks, employees have more time to tackle bigger and more complex problems. Combining AI and RPA ups the ante, allowing software robots to tackle even bigger tasks, which unlocks even more time for employees to focus on higher-level work.
When people think of RPA, they often think of rules-based models, but combining AI with RPA allows software robots to do significantly more than execute on a pre-set series of tasks. Thanks to the cognitive boost offered by AI, software robots have the power to make decisions and interpret information from data.
Some of the most common use cases for jointly using AI and RPA are back-office processes. When AI and RPA are combined, software robots can detect evidence of possible fraud in data being processed and intelligently manage accounts payable and receivable work. Software robots powered by AI and RPA can also identify backlogs in business processes by tracking the transactional logs of the various applications and systems with which they interface.
AI and RPA can help a software robot extract and transform data from a document and even process natural language. As AI continues to mature, companies can now more easily process documents like receipts, invoices, purchase orders, and utility bills.
As AI capabilities continue to scale, companies will turn to semantic automation to handle truly complex tasks. Semantic automation deals with a software robot’s understanding of the world in which it operates. Just like people borrow from past experiences to better understand new things, so too will software robots with semantic automation capabilities.
Instead of just seeing the layout and contents of a document that is being processed, software robots powered by semantic automation also understand the larger business context of the document. In short, this adds an understanding of “why” to the existing automation processes. This new layer of understanding leads to less work for developers because as ease-of-use increases, development becomes increasingly democratized, empowering more people to use software robots and make their lives easier.
Knowing the significant value add that exists when RPA and AI are combined, it is important to be able to identify when it is time to utilize these powerful tools. The question of using AI with RPA is really centered on the type of data being analyzed. The first step in the decision-making process is identifying if data is organized, semi-organized, or unorganized. Organized data refers to information being pulled from spreadsheets or other centralized, searchable files. Semi-organized and unorganized forms of data run the gamut from emails and word documents to videos, photographs, and natural language text. It is also important to consider if the data in question is standard or non-standard. Data organized in the columns and rows of a spreadsheet is considered standard. Non-standard data includes many forms of semi-organized and unorganized data, like images and natural language text. Any time work is being done with a data set that is semi-organized unorganized, or non-standard, consider combining AI with RPA for the best results.
Embarking on a digital transformation journey and utilizing automation and AI is imperative for businesses, and beginning the journey with an automation platform can be easier than business leaders realize. As more businesses combine AI with automation it will become even more ingrained in how we live and work, saving valuable time and creating new opportunities for creative expression and problem-solving.