Computer vision’s huge return on investment potential

computer vision object detection
Image credit: Depositphotos

Investments in artificial intelligence promise to deliver exponential returns, especially when  human processes are automated to reduce the time and expense of man-hours. The investment in machine learning models, training and integration can be recouped for a high ROI because of a massive productivity gain that can be realized.

The full potential of this impending boom has not been realized as the adoption of AI is still in its infancy. Just 20 percent of AI-aware firms say they are adopters or companies that say they use AI as part of their current processes, according to McKinsey. Yet, we are on the cusp of a coming wave of technology integration – and this isn’t hype. “The findings are startling: Fiscal 2019 will see a 3X upgrade in AI deployment,” writes Jon Markman in Forbes. In fact, according to MIT researcher and WSJ columnist Irving Wladawsky-Berger, “AI could lead to a gross GDP growth of around 26 percent or $22 trillion by 2030.”

Studies have proven that AI integrations can deliver both higher profit margins and exponential performance improvements, but only if the integrations are combined with proactive strategies to create true solutions. For artificial intelligence applications to be profitable instead of simply experimental skunkworks programs, they must show value, such as acceleration of a critical process or slashing an operational cost. The bottom line is that these applications must be able to drive higher productivity using AI.

Robotics, speech recognition, virtual agents, and autonomous vehicles are some of the well-known applications of AI technology, but companies that are specifically focusing on computer vision with technology that incorporates visual AI  are finding powerful productivity gains in automating processes and reducing human workloads.

How computer vision with AI boosts productivity

Computer vision has given machines the ability to identify objects, faces, actions, and concepts. Organizations can create different applications designed to exploit these functions and to automate processes like counting, tagging, authorizing, recording actions and so on. This drastically reduces costs, time to execute processes, and radically increases both throughput and accuracy.

This technology amounts to cloning the visual capability of humans with machine learning. For instance, airplane inspections can be automated with a machine learning model and will result in more accurate, lower cost, faster inspections. Likewise, today many scientists and researchers count things—plants, animals, samples—for many types of analysis. Offloading the task of counting to AI will save time, costs, increase throughput and free up human capital.

Computer vision AI in retail

AI is not just for customer-facing programs in retail. Brick and mortar retailers can also use computer vision for better planogramming. A planogram is a diagram of retail products showing how they should be placed on the shelves to increase customer purchases. Usually, companies hire a professional to do this and have them regularly check the arrangement for efficiency.

The average cost for manually generating a planogram report is $72, but with the help of AI and computer vision, this can be reduced to as low as $8 per a report, a 9x savings. Computer vision planograms are also more accurate than human-generated data.

Retail owners are expected to spend around $34 billion on AI technology by 2025. Of this amount, 29 percent may go to computer vision due to its ability to facilitate inventory reports, contribute to customer analytics, and empower checkout-free shopping models.

The application of computer vision technology in retail outlets can provide incredible insight into foot traffic, customer reactions, promotion efficiency, and purchasing behavior. ABI stated in a recent report, “brick and mortar retailers applying AI technology to their processes have a better chance of remaining competitive in the industry if they focus on long-term ROI over short-term goals.”

Computer vision AI and digital images

Another example of users opting into visual AI services is the tagging of photo agency images so they can be resold as photojournalism.

A photo news agency that shoots celebrity and sporting events uses a service to tag photos with detailed information about products, styles, celebrities’ names, and so on. These tags are often more detailed and have more information than most humans can possibly know about the contents of a photo. Imagine a celebrity of a foreign film wearing high fashion that most people would never be able to name.

Industry rates suggest that the average cost for manually tagging an image is $0.80. Using computer vision technology, however, can bring the cost of automated image tagging down to $0.01 per image – that’s 80x cheaper compared to traditional methods.

Computer vision AI in equipment maintenance

Computer vision is currently being used in the manufacturing industry for conducting quality assurance tests and maintenance checks. As mentioned above, the job of an aircraft mechanic is vital in ensuring the safety and reliability of a plane before it’s used—but computer vision and AI can automate this.

A single mechanic may take up to 10 hours to inspect the whole aircraft for damages and possible areas of failure. This number comes from the estimated time it will take an expert to inspect a single part and decide whether it needs to be repaired or not based on its aesthetic appearance. If they’re paid $100 per hour, this task will cost the company $1,000 per person.

In contrast, cameras connected to a machine with computer vision can inspect an aircraft part in seconds and virtually no cost. Productivity is increased by a significant amount, enabling organizations to add more tasks to the queue or redirect resources to other projects for growth and expansion.

Closing thoughts

In adopting AI, organizations should set the right environment for a digital transformation by focusing on AI productivity gains.

The math adds up, but the challenge may come from revising existing workflow processes, technical capacities, and company culture. Computer vision massively multiplies the ability of a human. The possibilities of their use are endless, and the technology is ready for implementation in many uses cases.


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