Businesses around the world are dealing with daily financial and political uncertainty, against the backdrop of recovering from the pandemic.
Optimizing processes and systems using advanced automation has the potential to improve efficiencies and support businesses as they look to not only survive but thrive in a volatile environment. Machine learning (ML), artificial intelligence (AI), and robotic process automation (RPA) are all terms we hear often when discussing the value of advanced automation. However, machine vision is an essential part of unlocking the full potential of these technologies – yet it is rarely included in the automation conversation. Operations managers need to be aware of the role automation plays in order to get the most out of the automation of front and back office operations.
Machine vision is a class of technologies that process information from visual inputs such as images, documents, computer screens, videos, and more. Its value in automation lies in its ability to quickly and efficiently capture and process large amounts of documents, images, and videos in quantities and speeds far beyond human capacity. Machine vision typically works with other advanced technologies, including natural language processing, RPA technology, artificial intelligence, and machine learning, to deliver the impact of automation on business processes. Machine vision is the eyes of automation, AI and machine learning are the brains, and RPA is the backbone you hang these technologies on to take advantage of in automation.
Take advantage of business opportunities
adopt automation accelerated In recent years, it has become imperative for companies to remain competitive across industries. While organizations prioritize these investments, they also face increasing cost pressures, with the aftermath of the pandemic, supply chain disruptions and geopolitical events. Rising prices. Documents, images, and information based on a computer screen are mandatory items that business organizations have to do. For this reason, the use of computer vision has spread because an important parent of front and back office operations involves dealing with visual information whether it is documents, video, or objects such as text boxes, scroll bars or buttons on screens. In many companies, if you want to automate on a large scale, you will probably have to process the image data more or less on a large scale as well.
Document processing is one of the most common uses of machine vision in automation. Machine vision combined with machine learning are the active components of what is referred to as Intelligent document processing: Automatically process and classify documents, extract printed or handwritten data and then decode the content for further automated processing.
IDP is particularly useful when automating document quality at scale. For example, technology is transforming traditionally process-dependent paper-based sectors, such as the financial services industry – by reducing the need for people to engage in certain processes that would normally require data mining from large numbers of documents. Even during the height of the pandemic in 2020, when most were relying on screens and working from home, it is estimated 2.8 trillion pages of paper have been printed. Companies also spend collectively billions On annual wages for data entry.
Machine vision automation isn’t just about scale – it’s about accuracy and improving the work people do, too. Boring repetition of these tasks contributes to significant error rates and results in low satisfaction and high levels of employee turnover, particularly when dealing with handwritten documents that can be processed with IDP.
Insurance officials no longer need to devote their days to manually digitizing paper applications; Bank clerks do not have to manually enter customer information or spreadsheet data into databases; Brokers can avoid the extra work that arises from errors that can occur when processing large volumes of transactions under strict daily deadlines. By filtering machine vision of data input extracted through machine learning and AI-based techniques, the speed, accuracy, and organization of processing needed to embrace automation technologies can be achieved.
The evolution of how to apply computer vision to autonomy is not limited to document processing. Video-based facial recognition in security operations, checkout supermarkets and remote equipment identification via drones for inventory management are examples of how computer vision can be leveraged for automation.
Technologies based on machine vision have become central to the creation of automation itself. For example, instead of relying on human workers to describe the processes that are automated when automation is designed, recordings of the process to be automated are created and then machine vision software is used, along with other technologies, to pick up The end-to-end process then provides the input to automate much of the work required to program digital workers (robots).
Ensure accuracy and leave collaboration with humans in the loop
Accuracy and bias standards It is a concern mentioned by organizations when it comes to relying on artificial solutions to perform certain operations. This is why it is important to have the correct processes in place for each application to ensure the best result. For automated document processing procedures, repeated processing procedures for human workers when doubts arise are common. Just as some supervision is required for the humans performing the operations, this diligence should be applied to digital workers as well.
Conversely, machine vision and artificial intelligence are also used in human-based quality assurance processes. In health care, automated second opinions are increasingly being used for radiology-based diagnoses. This is partly because it reduces the time and cost of processing second opinions but also because in an increasing number of fields, machine vision / AI-based processing of radiographs is more accurate than that of humans.
Human in the loop (or automation in the loop) avoids the issue of relying solely on technology or humans in areas with critical consequences, while allowing humans to use the most efficient and accurate statistical capabilities of automation techniques. Human healthcare workers can then effectively attend more resources to more patients by reaping the benefits of human digital collaboration. This is the real driver of automation in healthcare – realizing that every cost saved in clinical management and operations is a cost that can be dedicated to improving patient care. It goes without saying that healthcare is one of the most enthusiastic about automation today.
The future of work is flexible and machine vision makes it easy, adding even more intelligence to intelligent automation. This technology allows digital workers to interact with screens, documents, and videos just like humans do, which is quite an achievement. Ultimately, a more satisfied and satisfied workforce is achieved, along with a more competitive and profitable business.
Endless possibilities and opportunities
Machine vision is integral to increasing the impact of advanced automation technologies on business processes and paving the way for increased capabilities in the field of automation. Self-driving cars aren’t too far behind and show how machine vision is being pushed to the extreme.
We talk a lot about empowering employees to do more satisfying work; As we go forward, it will be about giving people the opportunity to live a more fulfilling life inside and outside of work. Not only can machine vision open up more opportunities for people to thrive, it can also enable businesses to successfully navigate an evolving landscape, lowering costs and increasing efficiencies – no matter what challenges and uncertainties lie ahead.
About the author
Tony McCandless is Chief Technology Officer at SS & C . blue prism. SS&C Blue Prism provides leading enterprise intelligent automation technology worldwide. We empower clients to reimagine how work gets done with a smart, secure, and scalable digital workforce. A digital workforce increases efficiency, reduces operating costs, and returns millions of hours to employees to focus on the things that matter most.