As the digital workplace continues to rapidly expand, it shouldn’t come as a surprise that robotic process automation (RPA) solutions are following suit.
In fact, the RPA market was worth an estimated US$2.65 billion at the close of 2021, with a projected CAGR of 27.7% from now until 2030.
In other words, the RPA market is on track to hit US$23.9 billion by 2030, signifying a massive shift in the way humans and AI interact in the workplace.
Add this to the fact that up to 25% of employees spend time and resources on unnecessary manual tasks that are now fully automated, and it’s easy to see that RPA — and intelligent automation — provide robust benefits to businesses willing to implement digital strategies and AI solutions for the future.
Read on to learn the basics of RPA and some of the biggest benefits it has to offer.
In its most basic form, RPA refers to software designed to automate various rules-based business operations and repetitive manual tasks. It aims to boost efficiency, productivity, and accuracy across the business.
Whether your business leverages simple RPA or more intelligent automation like AI, IDP, or Digital Workers, there’s no denying what a business can accomplish with a unified digital and human workforce.
Today, RPA is becoming an integral component of the modern business landscape. The world’s leading brands use RPA processes to unlock future expansion in an era defined by an emerging digital workforce, AI, and cloud-based solutions.
Automation technologies offer significant benefits to virtually every industry, especially in departments with repetitive, manual tasks.
So, what are the biggest benefits RPA has to offer to businesses today?
Below we will outline ten benefits of robotic process automation and explain how companies around the globe use RPA software to upgrade business processes, enhance analytics, meet compliance, and manage human resources.
One of the most significant benefits of RPA is that it cuts down on time spent on manual processes meaning employees have more time to focus on creative tasks that add direct value to the company.
A Deloitte survey showed that 56% of its respondents have already implemented RPA technology, with expectations of a global takeover in as little as five years.
Bots are always working, and with much more accuracy, speed, and scale than humans can. As a result, tasks are completed far quicker and with fewer mistakes.
Using bots to automate tedious rule-based activities like data collection, reporting, and onboarding allows your business to streamline development and focus on enhancing the end-user experience.
RPA creates a way for businesses to merge digital workers with the human workforce. When unifying humans and AI, scalability is no longer an issue because automation provides seamless solutions to help you expand your business with fewer human resources.
This allows your human workers to focus on more strategic and creative tasks while leaving the more tedious and mundane processes to automated bots.
Implementing RPA processes improves productivity and efficiency by automating manual tasks for every aspect of your business.
According to Deloitte’s third annual RPA survey, “RPA continues to meet and exceed expectations across multiple dimensions including improved compliance (92%), improved quality/accuracy (90%), improved productivity (86%), cost reduction (59%).”
With more sophisticated automation solutions leveraging machine learning and AI, you can expect this trend to continue.
Businesses employing RPA technologies reported 10 to 40 percent measurable cost reductions during initial deployments.
There are also reports of companies achieving ROIs of up to 200% during the first twelve months, with bots accounting for a mean of 20% FTE capacity.
Automating manual tasks ensures your employees can stay focused on revenue-generating activities and providing the best customer experience possible.
RPA bots reduce the risk of manual errors associated with data management, authorization, and even help identify cyber-attacks that humans may overlook.
A reduction in data entry has far-reaching advantages, especially for financial services involving audits, taxes, and private customer data.
When automation is configured correctly, there is less risk associated with data being entered incorrectly throughout the data entry process.
Since RPA eliminates the risk of error, your overall data quality is better, too.
High-quality data translates to streamlined processes. It also provides you with detailed performance analytics that you can use to measure the effectiveness of any automation solution, create data benchmarks for various departments, and helps you identify new areas and processes that may be ideal candidates for automation.
It’s safe to say humans tend to be happier at their jobs when it includes tasks that allow them to learn new things and engage with activities outside of data collection.
By handing the monotonous jobs over to automated bots, your workforce is likely to see decreased turnover rates.
RPA offers a lot of value when it comes to streamlining compliance and combating fraud.Automating the processes that involve managing sensitive customer data reduces the risk of a human mishandling that critical data since automation handles the various processes related to collecting, extracting, and processing PII.
Whether you’re automating financial processes, data extraction, or something else, there’s no denying the immense benefits RPA and intelligent automation have to offer to businesses in all industries.
Are you looking to kickstart your robotic process automation journey? See how our TiA Tangentia digital workers can transform your internal processes. Book a demo today to get started.
Hyper intelligent automation, which is often referred to simply as hyper automation, is a general term that refers to the application of advanced and emerging technologies to automation.
Hyper Automation is rapidly transforming what’s possible with Robotic Process Automation (RPA). RPA was originally designed to repeatedly perform the same task accurately and it created the foundation for automation as we know it. It required incredibly complex code and it was not capable of adapting to new information.
However, advanced technologies like artificial intelligence and machine learning are now being applied to RPA, creating a more evolved version of RPA that actually can grow and adapt on its own. The application of advanced technologies to RPA and similar systems is the general idea behind hyper automation. A secondary goal of hyper automation is creating a unified understanding of all automated tasks and processes throughout the organization.
None of this is theoretical – hyper automation is currently transforming businesses around the world. Approximately 34% of organizations are adopting hyper automation with a focus on employee productivity. Enough organizations are aware of the benefits of hyper automation to make it an entirely new movement focused on evolving existing automated processes and understanding every automated process in the business.
Today we’re going to examine how hyper intelligent automation is already evolving RPA, along with a deeper dive into what hyper automation actually means.
Hyper automation is not a specific technology or tool, but rather an umbrella term that refers to a wide range of tools and technologies that are used in advanced automation. Additionally, the term can also be used to refer to more sophisticated automation processes that go beyond the current definition of automation.
Every new and emerging technology that aims to make computers smarter plays a part in hyper automation. Each of the following technologies can be used to evolve RPA into something vastly more powerful:
Engineers and developers will use any of the above technologies (plus any that are not mentioned) to create advanced versions of the existing RPA processes.
RPA was revolutionary for organizations across all industries and still is. However, it was originally designed for mundane, repetitive tasks such as data entry or reconciling invoices.
There are three problems with RPA that hyper automation aims to solve:
Hyper intelligent automation aims to address each of these problems. By applying advanced technologies, typically AI and ML, RPA systems can become evolved versions of themselves that are aware of resource consumption and can react to unforeseen challenges in their tasks.
A real-world example of hyper automation evolving an RPA process comes from a developer from the United States government. The developer applied AI to the existing RPA that was used to help determine eligibility for the Affordable Care Act applications. The evolved RPA allowed the agency to scale without needing more human employees and serve more users.
Essentially, hyper automation creates better RPA bots that truly unlock the scalability that has been the goal of the technology since the beginning.
There are numerous capabilities that come with an IDP solution. These include:
So far we’ve been focusing on how specific RPA tasks and bots can grow and evolve with hyper automation. However, hyper automation not only applies advanced technology to the given process but also creates cross-functional collaboration across different processes and robots.
Ultimately, the goal of hyper automation is not to have dozens of siloed processes that are more advanced but to have an entire company-wide system of automated processes that work together to accomplish required tasks in the most intelligent way possible. Evolved RPA systems will be able to decide the best way to accomplish the task when armed with data from the entire company.
Organizations across all industries are undergoing digital transformations in an effort to remain competitive. Hyper automation is yet another way for companies to embrace a true digital transformation by creating an over-arching system of automated processes that create a holistic view of the entire organization. Additionally, each individual process will be approached intelligently, rather than strictly programmatically as with early RPA bots.
However, it’s worth mentioning that the promises of hyper automation discussed above will only be reaped by organizations with the right team of developers and engineers to apply advanced technologies to RPA bots.
Are you looking to evolve your RPA bots with advanced capabilities along with creating an all-encompassing vantage point of all automated processes in your business, but unsure where to start? Contact Tangentia today to see how we can help evolve your RPA bots into hyper-intelligent automated digital workers that will help your company reach new heights.
Intelligent Document Processing (IDP) is a method of automating the collection of structured, semi-structured, and unstructured data from a variety of sources and organizing it into a usable format. IDP is the most advanced form of extracting data from documents.
Keep in mind that IDP is not the same as Optical Character Recognition (OCR), despite the fact that the two terms are often used interchangeably. Instead, IDP was developed to enhance the capabilities of OCR, as well as to incorporate other technologies, such as data capture and Natural Language Processing (NLP).
With this in mind, this blog post covers how IDP works. But before we get into that, let’s talk about the benefits of IDP.
With the ability to eliminate the need to collect unstructured and semi-structured data manually, IDP delivers a number of critical benefits for a wide range of organizations. These include:
These benefits are significant, particularly in a world where organizations increasingly have to do more with less. With this in mind, let’s take a look at the IDP workflow and how it helps organizations reap these benefits.
The IDP workflow is used to scan hard-copy documents and files, capture the information in them, and store that information in a digital format. Types of documents that can be scanned include PDF files, emails, text messages, medical imaging, forms, and other types of documents in digital and paper-based formats. With this in mind, the IDP workflow consists of five steps, which are as follows:
1. Preprocessing of the document
Documents must be preprocessed to ensure that OCR can effectively distinguish the characters and words from the background. For this reason, the following techniques are used to prepare the document for OR:
2. Classification of the document
Classification of the document is a three-part process that determines the following:
3. Extraction of data
There are two ways to extract data from a document. These include:
4. Validation of data
The validation of extracted data is done to determine whether it contains any inaccuracies. This is done by applying data validation rules to the document, ensuring that any inaccuracies that are present are detected and flagged so they can be corrected.
5. Review by a human
The IDP workflow would not be complete without the human component. All flagged documents are reviewed by a human to confirm and correct inaccuracies. This is particularly useful during the supervised learning of learning-based extraction.
Once the IDP workflow has been completed, the resulting data can then be entered into a database or exported to any one of a number of file formats, such as PDF or XML.
There are numerous capabilities that come with an IDP solution. These include:
The above capabilities are applicable to a wide range of industries. The following are specific use cases for IDP:
The key to taking full advantage of IDP is to take a strategic approach to implementation. This includes implementing process intelligence, which is used to examine your processes and determine where IDP implementation will be most effective, as well as identifying process inefficiencies that could interfere with the implementation of IDP.
At Tangentia, we have an experienced team who can work with you to determine your IDP needs and develop a solution that will enhance your document processing capabilities.
For more information, reach out to a Tangentia ream member today.