With technologies like machine learning appearing as a sort of “magic wand” for business innovation, it’s prompted an unprecedented wave of speculation and investment in artificial intelligence (AI) across several different industries. According to the digital media firm GlobalData, AI has a relatively simple concept at play—exploring and implementing different ways to perform well-understood, common tasks in a more efficient manner. The firm’s Disruptor Tech Database indicates that entire AI practitioners primarily see very particular benefits within problems directly impacting the bottom line. Here are four major ways (and examples) AI is impacting these businesses and industries.
This seems surprising, yet an idea I’m sure many innovators and technologists are kicking themselves for not thinking of themselves. Motorcycle manufacturer Harley Davidson, for example, began utilizing AI to automatically craft proper digital marketing and advertising campaigns on a customer-by-customer basis. The technology seeks existing customer data from Harley Davidson’s customer relationship management (CRM) system, and analyzes users’ past online and offline purchasing behavior to bolster marketing campaigns across channels. Just three months after deploying AI, dealership leads grew almost 3,000 percent, while overall sales increased by 40 percent.
Some specific situational use cases for AI lie in improving operating efficiency. Utility company General Electric (GE) tapped into millions in cost-savings thanks to this innovative platform. Having often faced challenges in reaching conclusions on its overall expenditure, GE has an extensive operating record across multiple sectors and enterprise resources. AI enabled GE to integrate all 270 of its separate ERP systems onto a single platform, which made the company discover over $100 million in return on investment opportunities that included optimizing sourcing strategies, renegotiating contract terms, cross-selling opportunities, and reducing product landing costs.
AI has been largely revered by enterprises as a key to customer personalization—something this field has used for many years. Music streaming companies Spotify and Pandora, for example, utilized this technology for enabling customers to have a totally personalized listening experience. While major entities in music streaming like Apple offer their own customizable playlists, they don’t pander to an individual listener’s tastes over time. Spotify’s “Discover Weekly” feature, for example, was organized algorithmically, and became sensationalized for people wanting to more accurately personalize their music playlists.
Another aspect where AI has become a substantial game-changer is the different ways businesses classify and respond to risk management. Money transfer and payment company PayPal, for example, shifted to using neural networks instead of linear models via deep learning, for the purpose of analyzing money transactions in real time. The innovative platform assists in the formation of scenarios related to positive and negative user behavior, along with helping improve the accuracy of fraud detection over time. Since deploying AI, PayPal has reportedly reduced its fraud rate to .32 percent of its total revenue, compared to many of its competitors that see rates around 1.32 percent in this category.
Filed Under: AI • machine learning, M2M (machine to machine)