5 of the best artificial intelligence use cases

“The number of systems will continue to increase at an even larger rate as corporations acquire more AX expertise and feel more comfortable about its application. More systems which capture scarce expertise and make it available throughout the organization will be created. Systems that enhance our problem solving by making better decisions more quickly will be created.”

Those were some of the conclusions from a report entitled Industrial Applications of Artificial Intelligence (AI)—written in 1985 by Mark Fox, at the time a professor and founding member of Carnegie Mellon University’s Robotics Institute.

Clearly, AI has come a long way from the era when hulking mainframes crunched numbers like a sloth approaches a lettuce leaf. AI’s upgraded algorithms produce predictive analytics, parse data, and help people and machines make smarter decisions from the boardroom all the way down to the factory floor.

So, what are some of the most compelling use cases for AI? 

Retrofitting factory floors with AI in manufacturing

The main challenge with implementing AI in manufacturing is that many systems still predate the internet age, from vegetable canning lines little changed since WWII to boxboard factories still maintaining 40 year old processes. While automation exists in these factories, the true test is how to make legacy systems compatible with new AI technology.

That’s why the two most popular ways AI has integrated into manufacturing are predictive maintenance and robotics that leverage image recognition and smart sensors alongside existing machinery. In fact, AI’s predictive analytics in smart factories can help eliminate downtime, improve worker productivity and prevent supply chain chokepoints. For instance, Chinese mobile phone manufacturer Changying has deployed AI-powered robots seamlessly onto the factory floor. Working side by side with humans, their predictive ability to anticipate problems has increased productivity by 250% while reducing production floor errors by 80%.

According to research, AI-enhanced predictive maintenance of industrial equipment will generate a 10% reduction in annual maintenance costs, up to a 20% downtime reduction and 25% reduction in inspection costs. That makes major dollars and sense for businesses trying to jump in on the wave, as Trendforce estimates that the market potential for global smart manufacturing valuation will reach $320 billion by 2020.  

Moving from reactive to proactive health care

AI use cases span past the hospital bedside and now meet patients where they are. Smart pacemakers, heart monitors, oxygen tanks and blood glucose monitors are a few of the vast array of AI-based products that will get to know the unique rhythms and functions of your body and then warn you if something seems to be trending ominously or abnormally.

Health wearables have long been seen as a link between healthier lifestyles and the medical ecosystem as a whole, but AI-driven IoT is truly the bridge that connects the two. A 2016 study by Frost & Sullivan outlines that AI in healthcare can improve health outcomes 30 to 40% while slashing costs in half.

Products like the Owlet, a wearable sock for babies, provide real-time data about your baby’s vitals as they sleep. Data is compared with a baseline and emits a shrill chirp if oxygen levels hit a dangerous threshold.

The evolution of transportation in commercial applications

AI is revolutionizing transportation from commuter cars to commercial fleets. For daily drivers, smart sensors are playing a growing role in regulating everything from traffic to mission critical behind-the-wheel functions like braking and accelerating.

However, the real market opportunity lies with business transportation. The driver shortage in the trucking industry is so acute—with no real relief in sight—that some industry executives are waiting for the day when autonomous trucks roam the roads. Hundreds, if not thousands, of semi trucks, aren’t being ordered because there aren’t drivers to fill the cabs.  A recent report by the International Transport Forum shows that without autonomous transport, cargo will continue to idle at docks and terminals throughout the U.S. and Europe where 6.4 million drivers will be needed by 2030, but only 5.4 are estimated to be available.

AI will also have an increasing role in regulating the complex transportation and supply chain. The issue here is you have a patchwork of systems being used to move goods from outdated software to human dispatcher. As AI is integrated into the system it can help make smarter decisions from the best routes to choosing the best subcontractors to move goods. Large companies that have complex delivery networks, like UPS and FedEx, will initially see the most ROI in their AI deployments, according to industry publication FreightWaves.

The buzzworthy fintech space gets an AI boost

Bain estimates the banking industry will save a trillion dollars in operating expenses by implementing AI, from the notoriously labor-intensive underwriting process to back-office users that will integrate individualized consumer psychographic pattern profiles with real-time data. Fraud will become easier to detect and to prevent.

The arrival of AI has created a new class of businesses—AI fintech—that are all scrambling to cater to financial institutions who are eager to invest in the technology. In fact, the most recent PwC Global Fintech Report shows 34% of banks are investing in AI and we expect that number to increase.

AI is being used in banking to better analyze credit scores and use predictive analytics based on consumer behavior to minimize risk in underwriting loans. Upstart companies like ZestFinance, which applies its AI-based credit-decisioning technology platform to help lenders increase revenue, reduce risk and ensure compliance, are appearing on the banking stage.

Boosting waning retail margins with AI

McKinsey has found that early adopters of AI in retail settings have enjoyed a profit margin increase of 5%—a significant number in a business where margins can be tight.

One of the easiest retail problems AI can address is inventory. Holiday sales abound when businesses order in expectation of what might sell over the holidays, only to be left with a glut of panini makers that didn’t move. AI-driven real-time data allows retailers to instantly see what is selling and where, making inventory stocking decisions far more precise than the traditional annual guessing game where a wrong guess can lead to full warehouses and wrecked balance sheets after Christmas.

According to Retailtouchpoints, merchants want to allocate lower amounts of inventory initially, process the demand signals they receive so they can understand where products are selling, and then replenish locations based on demand-sensing signals.

There are consumer-centric applications that bring with them big business benefits.  The Twyst bag is an example of a retail AI-innovation that has the potential to transform the shopping experience by reducing “abandoned cart” and check-out line chokepoints.  A study by Ayden shows that long lines cost businesses.  A customer who finds the perfect sweater for their Aunt Sally sees the massive line snaking into the baby clothes department, gets discouraged, drops the sweater and goes elsewhere. That abandonment, according to Ayden, costs $37.7 billion annually in lost sales from discouraged customers, a loss innovation like Twyst eliminates.

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