Straight talk on automation: Getting the right stuff done with AI, ML, and RPA
AI, machine learning (ML),and robotic process automation (RPA) are becoming increasingly pervasive in the modern workplace. To some, they are tools that drive digital transformation and modernize how organizations do business. For others, they are over-hyped "shiny new objects" whose real-world value has yet to be proven. So which viewpoint is correct?
If you peel away the acronyms, the underlying technologies all focus on automating mundane and repetitive tasks. For example, AI, ML, and RPA can enable automated data extraction from any document, video, chat message, and contract. But it is in more complex business processes where enterprises derive real value because these technologies seamlessly drive data in the right systems, processes, and people—while also automating business-critical processes from end to end (instead of simply digitizing individual and unrelated tasks).
Intelligently automating manual processes
AI, RPA, and ML are all branches on the intelligent automation tree. However, each one has unique characteristics, and as a result, they should all be used in specific scenarios to address an organization’s unique needs and requirements.
RPA uses software "robots" to automate standard, repeatable business processes such as copying a loan account balance into a report or the movement of files from one folder to another. It’s programmed to complete repetitive tasks the same way every time and plays a beneficial role in back-office automation for many enterprises. RPA does a great job at completing well-defined tasks, but it does require a level of human support to set up and modify.
However, where RPA requires a human touch, AI and ML do not. AI and ML technologies can replace human intervention in manual processes altogether because these tools learn independently and develop their own logic and approaches to solving problems.
Both AI and ML can transform unstructured data (such as documents, spreadsheets, videos, and social media feeds) into structured data that the business can use to better support decision making, mitigate risks, and improve customer experiences. These technologies learn by example—independently identifying patterns and characteristics within datasets.
Document-driven use cases—reimagined
Intelligent automation—driven by RPA, AI, and ML—is modernizing multiple back-office processes.
A classic use case is accounts payable (AP) and invoicing processing. This key business function is complex and process-heavy, and many enterprises still struggle to find tools that enable them to automate core workflows beyond a few siloed instances.
RPA, AI, and ML are flipping this script by delivering end-to-end AP automation that identifies errors, detects fraud, and automatically codes and approves invoices. This new approach reduces the need for human intervention to only the most obtuse exceptions.
Automation is also an increasingly common feature in expense reporting systems. Expense management is a painful process, with humans submitting receipts as part of a batch for reimbursement. RPA frequently replaces the previously manual method of checking each receipt, matching it to the report, and approving payment to the employee.
Any process with a certain amount of structure is ready for automation. For example, employee onboarding, new account activation, and the processing of customer orders are all areas where RPA, AI, and ML tools can be applied to add value. The age of intelligent automation takes the limitations of traditional process automation and blows them away.
Satisfaction with intelligent automation
AI, ML, and RPA are all essential weapons in the battle against mundane, inefficient back-office processes. Intelligent use of these tools increases productivity, eliminates errors, and cuts costs. And perhaps most importantly, intelligent automation offers the potential for employees to truly find their work more satisfying, stimulating, and productive.