Automatic image processing for many people is the technology that makes you look ten years younger on your Instagram photos. Yes, that is somewhat true, but it’s only a part of the picture.
Image processing is used mainly for two purposes: image quality enhancement, similar to the Instagram use case, and intelligent image recognition - the analysis and understanding of what is pictured on those images. Artificial intelligence (AI) helps understand images, find and classify objects on images, assess the quality and completeness of objects by comparing them with etalons - all those capabilities bring unique opportunities to drive business value. That makes image processing a precious automation technology for business.
As you can see from the following use cases, automatic image processing technology creates business value in a number of ways by streamlining the existing and enabling new, previously unimaginable business processes:
There is a powerful story of Airbnb, which went almost bankrupt in the early days due to low rental rates until they realized that the reason was - poor quality, unattractive images. They hired professional photographers to help landlords in New York city take and post high-quality pictures of their properties. That solved the problem and saved the company.
Control of image quality is a critical task for every eCommerce website allowing 3rd party postings. Now technology can help to process images, assess their quality, and provide recommendations for improvements. This can happen automatically, right when pictures are uploaded, so sellers can make timely updates before finalizing the posting. That improves the conversion rates and makes both sellers and website owners more successful.
Automatic image processing technology also helps traditional retailers and FMCG companies to improve operations and increase sales per square foot - a critical success metric. Continuous monitoring of the shelves with automatic recognition of low stock items allows timely replenishment and just-in-time processes across the supply chain.
The image processing technology can also create an opportunity for merchandisers to gain a competitive advantage. One of our FMCG customers equipped its field merchandisers with a mobile app to order replacement products instantly when they find an empty shelf. That helps keep their shelves packed by timely refilling the inventory but also helps take over space from less agile competitors. Retailers are never excited by the empty shelves. If the incumbent products are not available in time, there is an opportunity for competitors to come in.
Organizing digital images is never easy, especially if they are critical for your business and you have many of them. You may do it by the file name, creation date, file format, and other metadata parameters. However, those are not always meaningful. Even the file names do not reflect the content of those images. For example, photos receive names as sequential numbers at the time of capture.
Sorting through the pictures to determine which categories they belong to is made easy with automatic image processing. AI technology recognizes the content of the images and can tag, group, and rename the files and folders, creating a new, meaningful structure.
With automatic image processing, you can quickly create custom product offerings specific to your customer’s needs. Think about cosmetics, clothes, and glasses. Different styles and colors fit different people depending on their body shape, facial features, hairstyle, skin color, eyes, and other unique parameters. Before, you had to consult professional stylists to find the best products for you; now, AI technology can help. Pre-trained AI models can automatically suggest the right products, and automatic image processing allows you “to try” them by applying them to your photo. This opens new opportunities for selling more products online and providing better customer service.
The unique benefit of automated image processing technology is that it can learn to become an expert in visual recognition. Think about how Facebook can find all your friends in the photo. In a somewhat similar way, the trained machine learning technology can identify visual anomalies in the shape and color of buildings, equipment, and products, detecting potential damages and quality issues. While before, companies had to leverage highly trained human experts to control the quality of the output from the production line - now it could be done instantly and automatically by taking pictures of each product and using automatic image processing to validate its quality.
Non-medical insurance fraud is a $40 billion dollars issue, according to NAIC. The average American family pays an additional $400-$700 a year in insurance premiums to cover these costs. The accurate assessment of property damage requires trusted and expensive expertise, which creates a challenging dilemma for insurance companies - when those costs are justifiable. Automatic image processing uses AI technology to reduce the cost of claims processing and validation. Intelligent image analysis can identify and assess damages on submitted photos and use trained AI models to estimate the potential cost of repair. Such an automated system ensures the processing of 100% of claims, leaving much fewer opportunities for potential fraud.
Those are just some of the most common use cases for automatic image processing. What images are you dealing with? What are your automation opportunities?
Book a personalized demo today to see what Super.AI’s flexible, work-ready, AI-driven automatic image processing technology can do for your company.
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