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Is AI just a fairy tale? Not in these successful use cases

Passing the artificial intelligence fairy tale stage is a challenge for some organizations. Here are two examples of artificial intelligence that fulfill the wishes of its users.

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Even with technology, we sometimes believe in fairy tales. A fairy tale is a story with “a fantastic and magical setting or magical effects within a story”. Recently, I didn’t think much about fairy tales until I started studying the number of online case studies for artificial intelligence (AI) in companies.

In most of these case studies, the result was that an AI solution was successfully implemented. However, the results were not there when I looked through the stories for business results or results. Instead, the stories ended with what companies hoped to get from their AI investments. They hoped their fairy tale projects would really come true.

AI is in its infancy in most companies, and there are legitimate reasons why many tangible business results from AI are still invisible.

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Still, it’s not too early for CIOs and others responsible for AI projects to start worrying: Because it won’t be long before boards and stakeholders start questioning business results.

Since most of us have already heard of these issues, I won’t be able to examine why AI projects can fail, or what companies need to do to tighten their AI applications. Instead, it’s time to take a look at some of the AI ​​projects that started out as fairy tales but have actually come true and are generating huge profits for their company.

Here are two examples.

GE lowers operating costs with artificial intelligence

Machine downtime costs companies $ 260,000 per hour, according to Aberdeen Group. When General Electric decided to target machine downtime as a category that bleeds the bottom line, it looked at the combination of AI and the Internet of Things (IoT) as a way to bring in significant operational savings.

GE has added IoT sensors to all of its machines, from power turbines to hospital scanners and aircraft engines. Sensors transmitted data in real time that reported how the machines were operating. The sensors also measured the effects of fuel level changes and temperature fluctuations.

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“Each of the company’s 22,000 wind turbines is constantly streaming operational data into the cloud.” Intelligent learning algorithms allow each turbine to adapt its behavior to mimic other nearby turbines that are working more efficiently, “says futurist Bernard Marr.

In GE’s data center, this machine-generated data is embellished with data from third-party weather, geopolitical and demographic sources. Data is processed by the Hadoop-based industrial data lake service used by GE and its customers, along with a variety of tools that assist GE and its customers in interpreting data.

GE saves money because its AI predicts potential machine downtime situations. Management can intervene before the outage occurs, hopefully halting or shortening downtime. So are the customers who are expected to save an average of $ 8 million a year from reduction in machine downtime.

This is a use case of artificial intelligence that not only lowers operating costs but also transfers these benefits to customers, thus increasing the likelihood of new revenue opportunities.

University of Iowa uses artificial intelligence to combat blindness

Diabetic retinopathy (DR) is an eye disease caused by diabetes, which over time endangers the small blood vessels at the back of the eye and blocks blood flow. As this happens, patients begin to experience visual symptoms such as dark spots and floaters, which ultimately leads to blindness.

Because DR can lead to blindness if left untreated, it is very important to detect the condition early so it can be treated and managed successfully.

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It sounds simple, but it’s not. Patients often have limited access to caregivers who can diagnose the condition, and those living in urban areas may find it difficult to make appointments with overburdened professionals’ offices.

Dr. Michael Abramoff is a professor of ophthalmology and visual sciences and electrical and computer engineering at the University of Iowa. He was determined to solve the problem with artificial intelligence and an autonomous system capable of making medical decisions. The IDx-DR system uses a low-power microscope attached to the camera to capture images behind the eye. The AI ​​then evaluates the images for DR biomarkers and reports the findings to the patient’s ophthalmologist. The procedure only takes a few minutes and makes it easier for doctors to see more patients at risk. If DR is present, the provider can immediately refer the patient to a specialist. The time saved can mean the difference between eye health management and blindness.

This is a life-changing and game-changing AI use case.

Why is AI success important?

Many companies are still working on AI projects in fairy tale mode, but we can all be encouraged by the AI ​​success stories that make fairy tales come true. These AI use cases targeted specific business problems and produced significant and measurable results.

More of these effective results are likely to come as AI continues to revolutionize the way we solve problems and push our thinking in new directions.

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