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Businesses around the world are dealing with financial and political uncertainty every day, against the backdrop of recovery from the pandemic. Augmenting processes and systems with advanced automation not only improves efficiency and supports companies as they seek to thrive in a changing environment. Machine learning (ML), artificial intelligence (AI), and robotic process automation (RPA) are terms we hear when discussing the value of advanced automation. Machine vision, however, is a key component to unlocking the full potential of these technologies – but one that is rarely included in the automation conversation. Operations managers need to understand the role of automation in order to get the most out of automating their front and back office processes.
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 capture and process large volumes of documents, images and videos quickly and efficiently at a scale and speed far beyond human capabilities. Machine vision automation works with other advanced technologies including natural language processing, RPA, AI and machine learning to deliver impact on business operations. Machine vision is the eye of automation, AI and machine learning are the brains, and RPA is the backbone on which you mount these technologies for automation.
Capitalizing on business opportunities
Automation adoption has accelerated in recent years, making it essential for businesses to remain competitive across industries. While organizations are prioritizing these investments, they are facing increasing cost pressures in the wake of the pandemic, supply chain disruptions and geopolitical events on the prices of basic materials, products and services. Documents, images, and computer screen-based information are imperative elements that organizations must work with. As a result, the use of computer vision has exploded because a significant percentage of front- and back-office processes involve processing visual information from documents, video, or objects such as text boxes, scroll bars, or buttons on screens. In many businesses, if you want to automate measurement, you’ll probably need to process some image data to some extent.
One of the most common uses of machine vision in automation is in document processing. Machine vision combined with machine learning are the active ingredients in what is called intelligent document processing (IDP): automatically processing and identifying documents, extracting typed or handwritten information, and then identifying the content for further automated processing.
IDP is especially useful when scaling document qualities. For example, the technology is changing paper-heavy and process-driven sectors like the financial industry – reducing the need for people to participate in certain processes where information is required to extract information from multiple documents. In the year Even at the height of the pandemic in 2020, with many relying on screens and working from home, an estimated 2.8 trillion pages of paper have been printed. Companies are spending billions in annual salaries for data entry.
Machine vision automation isn’t just about scale, it’s about accuracy and improving what humans do. The tedious repetition of these tasks contributes to high error rates and leads to low satisfaction and high turnover rates, especially when dealing with handwritten documents that can work with IDP.
Insurance administrators no longer need to devote their days to manually digitizing paper applications. Bank clerks should not manually enter customer information or spreadsheet data into databases; Brokers can avoid additional work that may arise from errors that may occur when carrying out a large number of transactions within a strict daily time frame. The speed, accuracy, and organization needed to adopt automation technologies can be realized by filtering data generated by machine vision through machine learning and AI-based technologies.
The complexity of how computer vision is implemented in autonomy is not limited to document processing. Video-based facial recognition in security processes, checkout-free supermarkets and remote equipment via drones for inventory management are examples of how computer vision can be used in automation.
Machine vision-based technologies are becoming central to creating automations themselves. For example, when designing automation, instead of relying on human workers to automate the processes being automated, automated recordings of the process are created and then machine vision software combined with other technologies is used to capture the end-to-end process. Finally, provide resources to automate many of the tasks required to program digital workers (bots).
Ensuring accuracy and giving up cooperation with people
Accuracy levels and bias are concerns cited by organizations when it comes to relying on human solutions to perform certain procedures. This is why it is important to develop the right procedures for each application to ensure the best results. It is common for automated document processing steps to slip over human staff when doubts arise. Just as some oversight is needed for people processes, due diligence must also be applied to digital workers.
Conversely, machine vision and AI are also used to QA human-oriented processes. In healthcare, radiology-based diagnoses with automated secondary opinions are increasing in use. This is partly because it reduces the time and cost of processing second opinions, but also because machine vision/AI-based processing of radiological images is more accurate than humans in an increasing number of areas.
Humans in the loop (or automation in the loop) eliminates the issue of relying solely on technology or humans, allowing humans to leverage the capabilities of automation technologies that are statistically more efficient and accurate in areas of critical consequence. By reaping the benefits of human-digital collaboration, healthcare workers can attend to more patients more efficiently. This is the true driver of automation in healthcare – the realization that every cost saved in administrative and clinical processes is a cost that can be allocated to improving patient care. It goes without saying that healthcare is one of the most enthusiastic automatons today.
The future of work is agile and machine vision facilitates this, adding more intelligence to intelligent automation. This technology allows digital workers to interact with screens, documents and video just like humans, which is a breakthrough. Ultimately, a more fulfilled and satisfied workforce results in a more competitive and profitable business.
Endless opportunities and possibilities
Machine vision is critical to maximizing the impact of advanced automation technologies on business operations and paving the way for increased capabilities in the automation space. Self-driving cars aren’t too far off and show how machine vision can be pushed to its very potential.
We talk a lot about empowering employees to do more fulfilling work; Moving forward, it will be about giving people the opportunity to live more fulfilling lives, both at work and outside. Machine vision not only opens up more opportunities for people to thrive, but also enables businesses to successfully navigate the evolving landscape, reduce costs and increase efficiency – no matter what challenges and uncertainties arise.
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