As the pandemic recovers, businesses around the world are facing financial and political uncertainty. Optimizing processes and systems with advanced automation has the potential to improve efficiency and support companies as they look to not only survive but thrive in a volatile environment. Machine learning (ML), artificial intelligence (AI), robotic process automation (RPA) are terms we often hear when discussing automation. However, machine vision is a key part of unlocking the full potential of these technologies - but it is rarely included in the automation conversation. Operations managers need to be aware of its role in automation to get the most out of automating their front- and back-end processes.

Machine vision is a type of technology that processes information from visual inputs such as images, documents, videos, etc. Its value in automation lies in its ability to capture and process large amounts of documents, images, and videos quickly and efficiently at a rate and speed that far exceeds human capabilities.

Machine vision often works with other advanced technologies, including natural language processing, RPA, AI, and machine learning, to deliver the impact of automation on business operations. Machine vision is the eye of automation, AI and machine learning are the brain, and RPA is the backbone that you attach these technologies to to leverage them in automation.

Capitalizing on Business Opportunities

The adoption of automation has accelerated in recent years, becoming essential for businesses to remain competitive across industries. While organizations are prioritizing these investments, they are also facing increasing cost pressures, with the aftershocks of the pandemic, supply chain disruptions, and political events all causing prices of essential materials, products, and services to skyrocket.

Documents, images, and computer screen-based information are essential elements of the work organizations need to do. As a result, the use of computer vision has exploded because a significant percentage of front- and back-office processes involve processing visual information, whether it is documents, videos, or objects such as text boxes, scroll bars, or buttons on a screen. In many businesses, if you want to automate at scale, you will probably also need to process image data from one form or another at scale.

One of the most common applications of machine vision in automation is document processing. Machine vision combined with machine learning are the active components of what is known as intelligent document processing (IDP): automatically processing and classifying documents, extracting printed or handwritten data, and then decoding the content for further automated processing.

IDP is particularly useful when automating document quality at scale. For example, technology is transforming traditionally paper-intensive and process-oriented sectors, such as financial services, by reducing the need for humans to be involved in certain processes that may require extracting data from large volumes of documents. Even at the height of the pandemic in 2020, when most were screen-based and working from home, an estimated 2.8 trillion pages of paper were printed. Companies are also spending billions of dollars in annual salaries on data entry.

However, machine vision automation is not just about scale – it is also about accuracy and improving the work that humans do. The tedious repetition of these tasks contributes to significant error rates and leads to low satisfaction and high turnover, especially when dealing with handwritten documents that can be processed with IDP.

Insurance administrators no longer need to spend days manually digitizing paper applications; bank employees do not have to manually enter customer information or spreadsheet data into databases; brokers can avoid the additional work that can arise from errors that can occur when processing large volumes of transactions under strict daily deadlines. By filtering machine-extracted data inputs through machine learning and AI-based technology, the speed, accuracy, and organization of processing required to apply automation technologies can be achieved.

The sophistication of computer vision applied to automation is not limited to document processing. Video-based facial recognition in security processes, checkout-free supermarkets, and remote device identification via drones for inventory management are examples of how computer vision is being leveraged in automation.

Machine vision-based technologies are even becoming central to the creation of automation devices. For example, instead of relying on workers to describe the processes being automated when designing the automation, records of the processes to be automated are created and then machine vision software, combined with other technologies, is used to capture the process from start to finish, and then provide input to automate much of the work required to program digital workers (bots).

Ensuring Accuracy and Leaving Humans in the Loop

Accuracy and bias standards are concerns raised by organizations when relying on artificial solutions to perform certain processes. This is why it is important to have the right processes in place for each application to ensure the best results. For automated document processing solutions that are often repetitive for workers, uncertainty is common. Just as some oversight is required for humans performing processes, that same diligence should be applied to digital workers.

Conversely, machine vision and AI are also used to QA (Quality Assurance) human-based processes. In the healthcare sector, automated processes for radiology-based diagnostics are increasingly being used. This is partly because it reduces processing time and costs, but also because increasingly in some areas, machine vision/AI-based radiology image processing is more accurate than humans.

Humans in the loop (or automation in the loop) avoids the problem of relying solely on technology or humans in areas with serious consequences, while allowing humans to use the more efficient and accurate statistical capabilities of automation technologies. Healthcare workers can then provide more efficient resources to more patients by reaping the benefits of human-digital collaboration. This is the real driver of automation in healthcare – the realization that any cost saved in clinical and administrative processes is a cost that can be allocated to improving patient care. It goes without saying that healthcare is one of the most enthusiastic adopters of automation 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 videos in the same way as humans, which is a huge breakthrough. The end result is a more fulfilled and satisfied workforce, along with a more competitive and profitable business.

Endless possibilities and opportunities

Machine vision is integral 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 are not too far away and demonstrate how machine vision is being pushed to its furthest limits.

We talk a lot about empowering employees to do better work; as we move forward, it will provide people with the opportunity to live more fulfilling lives inside and outside of work. Not only can machine vision open up more opportunities for people to thrive, but it can also enable businesses to successfully navigate the evolving landscape, reduce costs, and increase efficiency – no matter what challenges and uncertainties lie ahead.