Robotic Process Automation (RPA) is a great way to capture and interpret data in order to automate processes, data manipulation and communications across multiple systems.
Ultimately, it’s a great way to replicate a repetitive, time-consuming process with a software robot or ‘bot’, thus allowing employees to work on other more valuable tasks. Unfortunately, a high proportion of RPA projects fail. These failed projects often come down to a few key issues from unrealistic expectations through to the lack of a holistic platform.
In this case, unrealistic expectations is a really interesting reason for failure and it could even go right back to how RPA is sold: the promise of complete automation, no human-interaction needed and a massive return on investment…
The above expectations are possible, but it’s unlikely to be the case with RPA alone - after all, RPA is, as mentioned, just the replication of the process in an automated digital format - essentially a very clever workflow (although this might be oversimplifying it…). Where it falls down is when something unexpected happens, for example, a new type of unstructured data that the workflow hasn’t been programmed to deal with.
This unexpected data means the workflow stops and someone needs to check the process, rectify the issue and get things back on track until the next thing throws it off course again.
Enter AI and machine learning.
Although true artificial intelligence is still very much in the works, machine learning is often thought of as part of AI, as once taught a set of rules, it can learn on the go and find ways around issues without needing to consult a human every time. It’s machine learning that can really take RPA from being great automation to amazing automation and it’s where the biggest benefits start to come in.
Here are 3 reasons why your RPA needs AI and machine learning:
1) Reduce human interaction with processes
Without any form of machine learning, your RPA processes can only enact the exact process it’s built to do. This means that “documents, emails and other unstructured data commonly create roadblocks” and human interaction is needed to get things back on track, whereby an employee needs to “analyze, understand and make a decision based on the information”. (RPA and Cognitive Document Automation—How AI Can Bridge Process Automation Gaps, Kofax)
Each time a human has to step in, the efficiency of the process decreases and operational costs are increased.
Machine learning, and more specifically, Cognitive Document Automation (CDA), can help to get around these roadblocks and reduce the human interaction needed.
2) Automate processes with unstructured data
Cognitive Document Automation (CDA) automates the processing of unstructured data contained in documents and emails and not only understands the data, but extracts information and sends it to the right place.
Unstructured data is the key here as it can be the factor that stops RPA working as you might expect it to. With CDA, you gain two crucial benefits that mean processes can continue to be automated:
- Automatically classify and separate documents to determine what they are and where they should go
- Automatically OCR, extract and validate data from documents, automating manual data entry tasks – including support for machine print, handprint, cursive, barcodes, bubbles, checkboxes, tables and mailing addresses
3) Reduced operating costs and increased productivity
Working together, RPA and CDA (which makes the most of machine learning capabilities) can automate processes that involve both electronic data capture and document capture.
In turn, this can reduce the overall operating costs of these processes and helps to increase productivity as employees can trigger a workflow with an action like scanning a document, and then leave the automated process to do the rest while they move on to something else.
How to implement AI and machine learning to enhance RPA
There are different approaches to adding machine learning capabilities to Robotic Process Automation, and one step that we always recommend is making sure you have a platform that allows for more than just RPA, with options to add more intelligent automation and scale as needed.
Cognitive Document Automation can be an important part of this platform, bringing in new features and opportunities to automate more complex processes without the need for human interaction.
Here are a few things to look out for when it comes to CDA:
- Ability to create digital images from multiple sources i.e. mobile, email, MFPs, web services, scanners etc.
- Ability to capture documents from back office data, front office, remote workspaces and directly from customers
- Ability to intelligently automate any document type (i.e., invoices, orders, enrollment and claim forms, mailroom documents, mortgages, contracts, correspondence and bank checks)
- Ability to leverage AI-based machine learning technologies to save time in initial configuration and ongoing maintenance
In all honesty, when it comes to document processes, there shouldn’t be much that RPA and CDA combined can’t handle, and it’s this level of robots that will bring a real return on investment and allow employees to step back and let the software robot do the job!