Tonight is your friend’s birthday party. You’ve forgotten to get a gift, and you have to work late.
The solution is in the palm of your hand. With your smartphone, you can ask MyKai, a mobile tool that connects to your bank account, how much you can spend. You can then send a quick text message to 1-800 Flowers for a bouquet of flowers. Then, you can tap on your friend’s address in Facebook messenger to summon an Uber driver to take you to the party.
Seamless consumer experiences such as that are becoming the new norm. Products and services custom-tailored to individual needs are no longer a luxury; they’re inexpensive, easy-to-use, and readily available to anyone with a mobile device and Internet access.
But, today’s developments are just a glimpse of what we’ll enjoy tomorrow. Thanks to rapid innovations in artificial intelligence (AI), we’ll soon have an army of robotic personal assistants at our beck and call. Our fridges will stock themselves, our home entertainment systems will read our minds, and our digital helpers will be skilled conversationalists—remembering context clues and earlier chats.
While AI has been around since a conference at Dartmouth College in 1956, in recent years, advances have made it possible so that soon it’s about to make our lives a lot easier and more affordable.
Take the clothing industry. Up until recently, only celebrities and industry titans could afford a bespoke suit. Now, modern technology has made individualized apparel production at a massive scale not just possible, but profitable. At Acustom Apparel, customers are ushered into a room decked out with high-definition sensors; within seconds, the sensors record every contour of the customer’s body. The resulting 3D model is used to make customized shirts, suits, and more—for a fraction of the price of traditional tailoring.
In the future, mainstream retailers will join the trend, bringing custom-made suits to the masses. The Brooks Brothers website already allows shoppers to digitally render their own made-to-order look, customizing everything from the fabric to the collar style.
There’s a similar trend in transportation. A decade ago, private limousine services cost hundreds of dollars per hour. Today, people can use car-sharing apps like Uber and Lyft to get chauffeured around their city for just a few dollars. The airline industry will soon have the digital infrastructure to automatically rebook seats or reserve hotel rooms for travelers in the event of a canceled flight.
Machine-learning, which employs advanced data science to program adaptable machines that “learn” from new data to improve on past computations, is only accelerating the trend toward personalization. Better than a personal secretary, the technology solves human problems without ever being asked. And it’s already starting to take over consumer services.
Consider Sephora. The cosmetic giant runs a huge “skin tone library,” offering 100+ shades and undertones. Internal research showed that customers often found the process of navigating different skin tones overwhelming and time-consuming; on average, people had to try on seven different foundations before finding the right shade.
Sephora harnessed machine-learning to fix the problem, creating the in-store device ColorIQ, instantly analyzing skin tones and offering product recommendations. Over time, as customers make purchases and rate products, ColorIQ’s algorithm fine-tunes its results to better reflect the customer’s tastes and preferences.
Fueled by machine-learning, AI can even housekeep. The Nest thermostat learns users’ schedules and temperature preferences and automatically adjusts its settings accordingly. Intelligent personal assistants like Cortana keep users on schedule with automatic wake-up calls listing the day’s appointments, weather forecast, and traffic information.
Machines are even getting chattier. Chatbots use complex formulas to converse directly with shoppers via text, and they are transforming consumer convenience. HealthTap’s chatbot, for example, responds to patient medical questions by drawing from a database of relevant physician responses; and Pizza Hut’s chatbot consults customers’ order histories to suggest personalized menu options.
It’s easy to question whether this level of convenience is too good to be true. And, yes, there are downsides to machine-learning and AI.
Many people already perceive the world today as formulaic. Take TV shows and box-office hit movies. How often can you predict the ending of a movie? Critics scoff at the lack of imagination, yet these patterns continue to exist because they are successful in creating a “comfort zone” where we find ourselves.
It’s hard to discuss AI without speaking to the risk associated with reducing customer experience to a formula. No doubt, it can lead to generic interactions that ultimately reduce novelty, individualization, and innovation.
But keep in mind that technology amplifies what already exists in human behavior and experience. The same stands true with AI. As much as AI can open doors to fundamentally new experiences, it will be used to enhance both the “good” and “bad” sides of our everyday experience, unless those issues are addressed head on.
The power to enhance the consumer experience using this rapidly emerging technology—such as AI—is in the hands of marketers. If they can strike the right balance between satisfying and capitalizing on consumers’ demands while exposing them to new experiences, personal convenience will reshape our lives for the good, and companies will profit substantially.