5 Practical Applications of AI
Why AI Is More Accessible Than Ever Before
The hype around modern artificial intelligence techniques is enormous. Every other day you read headlines that tout self-driving cars and mind-reading devices. It’s no secret companies innovate incredible products. But the media’s emphasis on cutting-edge technology can make AI appear out of reach for everyday folks and businesses.
The less advertised side of AI shows its actually becoming more accessible to businesses of varying industries, stages, and sizes. In a small business survey, Capterra found 1 in 5 small business leaders in the U.S. are using AI and 47% are considering adding it to their strategic plans.
While still in its early stages, more and more people are using AI without even realizing it. This movement isn’t limited to the tech industry either. Businesses in fashion, e-commerce, and even sports are leveraging these powerful techniques to make life better for their consumers and teams.
Let’s look at a few examples. Here are 5 practical (and cool) applications of AI you may not have thought about before.
1 | Chatbots- A New Kind of Conversation
Many businesses are utilizing these little guys to automate customer service.
Chatbots imitate human conversation, either through text or voice. Voice assistants like Siri or Alexa are popular examples. Many websites also use text-based chatbots.
Intelligent chatbots incorporate two AI techniques: machine learning and natural language processing (NLP).
The ML component use algorithms that allow the chatbot to receive input, like a customer question, analyze it and form context, and produce an appropriate output. Developers feed the algorithms lots of data to train them to produce the correct outputs from a variety of inputs.
The NLP component gives the chatbot the illusion of human-to-human conversation. The technique allows the bot to recognize and mimic human language.
Gartner predicts that chatbots will power 85 percent of all customer service interactions by the year 2020. No matter your organization’s size, you could consider implementing a chatbot to create a more user-friendly website or boost customer service efficiencies.
2 | AI in Sports Betting
Fantasy sports is $13.9 billion industry in the U.S. and is expected to reach $33.2 billion by the end of 2025. Sports have always produced huge amounts of data and stats and innovative tech has supported data collection on at huge scales. Combine it with competitive nature of sports and you have the perfect recipe for gambling.
Sound complex? It’s not. Sports gambling is a great example of an industry leveraging practical AI. You don’t need to be a high-tech analytics startup to gain an advantage in sports betting. Several fantasy leaguers have successfully run predictive algorithms and sports data to predict accurate outcomes. Then they employed machine learning techniques to complement the algorithm so that it can continually learn and improve as more data is collected over time.
3 | Recommendation Engines for a Personal Touch
Netflix anyone? How about some Spotify music recommendations for your long work day?
These user-driven platforms generate self-learning algorithms to offer personalized recommendations to you, the user. Mostly commonly, recommendation engines utilize collaborative filtering, content filtering, or a hybrid model.
A content-based recommendation engine generates preferences by combining the profiles of items in a user’s history and measuring the distance between a user’s profile and the items your organization would like to suggest.
A collaborative filtering model leverages similarities between users for recommendations rather than focusing on concrete details about particular items.
Some engines use a hybrid model of the filtering techniques, utilizing product details and similarities between users to offer suggestions. Personalized recommendations have proven to increase customer satisfaction, drive up revenues, and produce useful insights to companies across many industries.
How could a recommendation engine help you personalize your customer’s experience?
4 | Data-Driven Styling in Fashion Retail
StichFix, the personalized styling service, brought AI to fashion. The subscription box offers unique and personal selections by combining data and machine learning with expert human judgment.
The company has made machine learning and data science a priority from the start but it also prioritizes its people- the stylists. By incorporating AI, their stylists get to spend more time creating strong relationships with customers and focusing on the unique tastes that the machines can’t find.
StichFix incorporates AI into several areas of its business like styling, operations, and customer service. Contrary to popular belief, there is room for data-driven approaches in highly subjective industries like fashion, without impeding on human creativity.
5 | Combating Counterfeit Items in E-Commerce
In the booming age of e-commerce, counterfeit items are a very real and expensive problem. Apple found that 90% of “official” chargers sold on Amazon to be fake. Counterfeit goods, software piracy and the theft of trade secrets cost the American economy as much as $600 billion a year.
In an effort to fight counterfeit goods, online retailers are turning to AI-powered images and text recognition to detect minute image variations and suspicious product descriptions.
According to a recent Forbes article, “this unique approach enables addressing the issue of counterfeits at a massive scale by covering any number of online marketplaces at once -- a huge leap forward compared to the current hit and miss method followed by brands.”
As we continue to see more applications of AI emerging across all types of industries, consider how such techniques could be used to enhance your product, team, and bottom-line.
Thinking about jumping in but not sure where to start? Seek help from experts outside your organization to help you strategically plan and execute the best AI techniques for your products.
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