\ Leading Use Case of Artificial Intelligence
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Leading Use Case of Artificial Intelligence

By Patrick Schwerdtfeger

Everybody's talking about artificial intelligence, but few understand where its impact will be felt in the near future. Here are five emerging use cases for machine learning that every business executive should be familiar with.

1. Sensors and the Internet of Things (IoT)

The explosion of sensors in recent years is resulting in a corresponding explosion of data, and machine learning algorithms are churning through that data, looking for patterns that provide value. Predictive analytics is a perfect example. Platforms aggregating data from heat and vibration sensors, for example, are comparing that data to equipment performance metrics, learning to anticipate failures before they happen. It's much cheaper to conduct proactive maintenance work than react to an unexpected failure.

Executives should look at the equipment operating within their organizations and ask themselves if unexpected failures have happened in the past. If so, they should immediately begin experimenting with sensors to anticipate future failures. Predictive maintenance is one of the highest ROI (return on investment) applications of big data technologies coupled with machine learning algorithms.

2. Digital Transformation

Enterprise software solutions like ERP (enterprise resource planning) and CRM (customer relationship management) platforms are increasingly handling deliverables traditionally handled by white-collar human talent. A 2014 report by the Hackett Group revealed that the number of finance employees per billion dollars in revenue (within Fortune 500 companies) has already dropped by 40% in the past 10 years (between 2004 and 2014). Artificial intelligence technologies are facilitating these increasing capabilities and will increasingly displace white-collar jobs in the years to come.

Executives should identify all of the repetitive tasks undertaken by their people, particularly those in expensive management capacities. Then, sort those tasks by their level of complexity. Start with the least complex, and look for software solutions that can automate those tasks. Over time, these tasks will almost all be automated, and a proactive approach will accelerate the transition, yielding efficiencies and profits along the way.

3. Chatbots and Socialbots

For the purposes of this article, chatbots refer to a computer's ability to communicate with humans via text, and socialbots refer to their ability to communicate with spoken language. Siri and Amazon's Alexa are good examples of socialbot technology. Chatbot and socialbot capabilities are improving quickly right now and it's inevitable that they will soon displace customer service call centers and support staff.

We've all heard examples of this when calling banks or credit card companies. The automated phone system offers simple tasks that can be handled without a human customer service representative. These options remain clumsy today, but they're improving along an exponential curve, and many people, especially younger people, will soon prefer automated attendants over their human equivalents. Over time, that preference will spread throughout the population.

Enormous efficiencies will result from these technologies. Not only can businesses avoid eliminate salaries from their cost structure, but they can increase consistency and accuracy of their customer service delivery. Executives need to explore these technologies and look for opportunities to test them within their respective organizations.

4. Object and Image Recognition

One area of significant development is the use of machine learning to help platforms recognize images or objects in photo or video content. Machines can recognize cats or trees or cars. These capabilities are expanding quickly, including an increasingly diverse array of objects. Applications of this technology are playing a major role in autonomous driving platforms, and other applications are only limited by the imagination.

Executives should evaluate their operations and brainstorm applications of object or image recognition technologies within their businesses. Invite younger workers to participate in the brainstorming session. They're often more creative than their older counterparts. Don't just look for things you are already doing, except with humans instead of machines. Also look for things you are not currently doing. Look for ways to expand your product or service offering. Look for ways to surprise and delight your customers.

5. Autonomous Self-Driving Vehicles

Autonomous driving technology is evolving quickly, but it will still be a few years before we see "level 5 autonomous driving" on city streets. The technology will roll out first in more predictable settings. Long-haul highway driving has far more predictable than city driving, for example, but there's an even more predictable environment than that. Autonomous tractors driving on agricultural land is perhaps the most predictable application of all.

A number of companies have already introduced autonomous tractors. Case IH has a particular appealing prototype in development. The appeal of these tractors is clear. They can activate themselves in the middle of the night, servicing the fields during the cooler night temperatures when stress on the plants is minimal. They can be programed on an inch-by-inch basis, optimizing fuel usage without missing a single plant. Of course, there are labor implications as well. Grower/shippers and harvesting companies should test these technologies as soon as possible.

Long-haul truck driving will be the next industry to be disrupted. Today, there's a shortage of truck drivers, but that will soon change. The use of autonomous driving technology will initially focus on the long-haul highway driving. The "last mile" driving will still be done by human drivers for a few more years. In fact, before those city miles are automated entirely, they'll be handled by "drivers" sitting in cubicles outfitted with TV screens, steering wheels, and accelerator and brake pedals, despite being located in remote office buildings. The trucks will be outfitted with multiple video cameras, allowing remote "drone" drivers to see the truck's surroundings from hundreds of miles away.

The cost of trucking will drop dramatically when driver salaries can be eliminated. And once one company deploys the technology, the cost advantage will mandate that all providers adopt the technology quickly or be left behind. Tesla's new Semi already comes equipped with autonomous driving hardware. As soon as regulations are in place, we can expect deployment to soon follow. Executives should look at their current trucking needs. Also, they should consider what would be possible if trucking cost a fraction of current rates. Could you expand your service offering?

In all cases, there are two implications to consider. First, how can you deploy the technology to drive efficiency and reduce costs? Second, what would be possible if the cost structure dropped by 25%, 50% or 75%? Could you offer new and better services if it was cheaper to do so? The trend is towards higher and higher levels of service. Artificial intelligence, analytics and algorithms will facilitate those levels of service. Business executives who embrace these technologies expand their competitive advantage over time.

Patrick Schwerdtfeger is a business futurist who specializes in technology trends including big data, artificial intelligence and blockchain. He is an award-winning author and full-time professional speaker, and has spoke at hundreds of conferences all around the world. His new book is Anarchy, Inc: Profiting in a Decentralized World with Artificial Intelligence and Blockchain. www.patrickschwerdtfeger.com  

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