One of the biggest fears of artificial intelligence is that it’s a job-killer. It’s understandable – AI is often positioned as the silver bullet that will automate our lives, and one day enable machines to take over.
I’m happy to report that this will not be our reality anytime soon. While AI is widespread and thriving in ways consumers might not even realize, it’s still just a good supplement for companies.
From healthcare to retail to real estate, the power, magic, and effectiveness of AI in any industry is when it is augmenting, not automating, systems and processes.
Instead of sitting around and waiting for that “AI doomsday,” businesses should embrace today’s reality of AI and use it to grow engagement, traffic, and more. Not sure where to start? Here are a few examples of how you can tap AI to augment existing systems and deliver bottom-line impact.
Recommender engines are an excellent way for AI to support existing applications and improve the user experience. For example, recently, my wife and I learned that one of our favorite restaurants had integrated a recommender engine that saved our payment information and eating habits. By remembering what we ordered last time, the next time we dined there, the system was able to quickly recommend our favorite appetizer and handle our bill before I even took out my credit card. We were impressed by how personalized our dinner felt––almost as if we were eating in our own home.
Leveraging recommender engines is an effective way to personalize user experiences by working with the user data you already have. Netflix’s recommender engine is a great example of this principle. Their engine relies on data from more than 100 million users, such as the type of content they watch and how frequently they watch it, and an AI application called machine learning that is trained on this data to make smart suggestions. In the end, 80 percent of the TV shows that viewers watch on Netflix are discovered through the platform’s recommendation engine, personalizing the video streaming experience like never before.
The best part is that recommender engines are one of the easiest implementations of AI tools and can be applied to a wide variety of industries, such as e-commerce, real estate, and healthcare. Check out how we built our recommender engine at Trulia.
Virtual customer service
Businesses that are tasked with handling large volumes of customer inquiries should consider using AI to augment their customer service teams. Take the airline industry as an example. According to a 2016 report by MileCards, flights in December are cancelled five times as often as those around Thanksgiving. The surge in travelers coupled with canceled flights due to bad weather means airlines are likely dealing with massive amounts of inquiries from flyers each day. Managing this volume of data requires incredible scale, accuracy, and a personalized level of engagement, making it so that even the customer support team needs support. This is where AI can step in and augment existing applications.
Chatbots, or virtual assistants powered by AI, could easily handle a large portion of these interactions by providing basic information like real-time flight updates and refund policies, saving time for both customers and airline employees. Chatbots and assistants also have the potential to enhance customer relations even further when trained on enough data. For example, combining customer data from previous interactions with purchasing behavior can enable a chatbot to make specific suggestions to a customer, such as recommending the best times to book flights for their frequently-visited destinations.
When chatbots are able to efficiently take care of basic requests, human representatives can dedicate their time to more complicated cases. Adopting this kind of AI can not only improve the customer experience, but also maximize employees’ full potential.
Visual search engines
Photos are the new way to share information. More businesses are processing visual data, so leaning on tools like computer vision is key.
A useful and powerful application of computer vision is visual search. For example, Pinterest developed a visual search tool that lets you zoom in on a specific object in a Pin’s image and discover visually similar objects. Pinterest relied on deep learning and computer vision to analyze powerful image features by utilizing annotated datasets of billions of Pins curated by users. We do something similar at Trulia with real estate photos. Our systems can identify objects within a photo – like hardwood floors or granite countertops – and then organize those photos into collections of homes for visual browsing.
Building visual search engines involves developing natural language descriptions of image content that will allow users to search in a more intuitive way. Following traditional text search, the ability to search by image is not a new concept, though it will likely become the new norm thanks to recent advancements in hardware.
In the end, the best way to approach AI integration is to evaluate the practices within your own business that can be improved upon and seek out different applications of AI that best suit your business and customer needs.
This piece originally appeared on CIO Applications.