In the digital age, companies are always looking for new ways to deliver better services at lower costs. The uncertainties of COVID-19 have only accelerated this need, as automation is no longer an innovative pursuit, but a requirement to hedge against disruptions to business operations.
AI-infused automation can help shorten the time between identifying and responding to issues leading to better customer experiences. To make this happen, there is no hotter trend in automation than Robotic Process Automation (RPA).
What is RPA?
RPA is / are programmable software bot(s) that automate business tasks by interacting with a set of business logic and structured inputs.
Using RPA technologies, companies can build robots to automate repetitive human tasks such as manipulating files, processing transactions, or even working between multiple business systems.
Although that sounds magical, RPA is not a silver bullet. As it exists today, there are some limitations; so, RPA best-suited to tackle use cases that fit the following descriptions:
1. Manual and Repetitive
Today, many employees still bear the burden of manual and repetitive work.
Think of an HR professional onboarding information for new hires, or an insurance agent processing a new claim. They often have to transfer data from one source to another.
After repeating these tasks multiple times, letters and numbers become jumbled, and it’s easy to see how error-prone the process could be due to human error, fatigue, or context-switching.
RPA software bots can perform this work any number of times without the same limitations.
2. High Volume and Low Skill
Work such as those described above also tend to come in at high volumes and require a low amount of skill to complete.
These tasks are often done by knowledge workers, who end up devoting a significant portion of time to perform these low-value obligations over tasks that require domain expertise and a higher degree of thought.
The HR professional mentioned above could have more time to resolve other employee issues that require a human touch or sensitivity. The insurance agent could be solving more complex cases.
To scale up these processes and handle more work, organizations would traditionally have to increase operational costs.
Conversely, scaling RPA bots to assist with these tasks provides huge cost savings.
3. Structured Data and Well-Defined Rules
Again, using the above examples, it’s easy to see how the above tasks could follow a set structure around the type of data received.
The HR professional and insurance agent receive information in a specific, well-known layout and type based on the document the information is on.
It is then easy to identify the required information (name, claim type, claim amount) and apply a ruleset around it to make that information accessible to the internal system and actionable for these professionals.
RPA bots thrive in these scenarios where the input is structured and there are clear parameters for what the bot should do with the information it receives.
Organizations today have plenty of use cases that are high volume and repetitive (1) as well as manual and low skill (2), yet often times struggle with having perfectly structured data with well-defined rules (3).
Although frustrating, this struggle makes sense. Information coming from customers to power business processes arrive from different channels such as email, fax, etc. and in different shapes, sizes, and form types. Given that, how can companies scale their RPA investments to address these complex business cases?
How to make the most of RPA
The key is to normalize the variation of incoming business documents into well-defined structured data that RPA bots can efficiently process. Document Capture tools, such as IBM Datacap, help organizations streamline the capture, recognition and classification of business documents and extract important information for those bots to act on.
Datacap supports multiple-channel capture by processing documents on scanners, mobile devices, fax, emails, etc. Additionally, its natural language processing, text analytics and machine learning technologies automatically identify, classify and extract content from unstructured or variable documents and transforms it perfectly for the bots to operate on.
With the recent acquisition of WDG Automation, a Brazilian software provider of RPA, IBM has extended its existing AI-infused automation capabilities giving enterprises broader access to intelligent automation through software robots.
IBM Datacap and IBM RPA together can enable organizations to achieve tremendous returns on investment, reduce human error, improve quality and increase throughput.
Watch this video to see how IBM’s RPA extends Datacap’s document capture workflow to provide end to end automation