If you’ve ever browsed through the vast selection of items Amazon offers on their website then you’ve most likely had an interaction with their advanced AI algorithms. Beginning with product recommendations, Amazon started using machine learning algorithms as part of their core offerings, and over time they have quietly built strong AI and ML capabilities broadly across the whole organization. There is no single AI group at Amazon. Rather, every team is responsible for finding ways to utilize AI and ML in their work. At the company’s recent re:MARS show in June 2019, Amazon showcased its wide footprint on use of AI & ML. At the event,
the AI Today podcast interviewed three executives across various Amazon groups
to hear how each group is utilizing AI.
Alexa Shopping
Chuck Moore, Vice President of Alexa Shopping shared insights into how the Alexa Shopping group is currently applying AI across both the Alexa device as well as mobile apps, website, and backend operations. In 2018, Cognilytica produced a Voice Assistant Benchmark in 2018 as a way of measuring performance of various voice assistants. While these assistants are continually improving, and gaining more intelligence through their conversational interfaces, they have a long way to go before becoming as “smart” and useful as users would like them to be.
Amazon sees conversational commerce evolving significantly in the coming years. Alexa has only been around for five years, and as Alexa continues to mature, one area that Amazon wants to explore is the idea of predictive commerce. This is where Amazon can plan ahead and take care of the needs of customers before they are even aware of the need. The company aims to help customers with the things they don’t necessarily enjoy shopping for and figuring out how would you actually do that on a predictive basis to just make that super convenient and reduce the amount of time they spend on this.
Additionally, Amazon aims to reduce friction with conversational device and shopping interaction. Currently, when trying to book movie tickets, search for restaurants, and book a cab you need to open a separate skill for each of these tasks. The idea is to make these multi turn conversations better for the user. At the Re:Mars conference, Amazon announced Alexa Conversations, which enables a conversational thread across multiple skills, all in one coherent conversation. The “night out” conversation lets a user purchase movie tickets, make dinner reservations and request an Uber ride all in one conversation without having to wait and open additional skills to proceed.
Voice assistants, and therefore conducting commerce with voice, is still very new there are some learning curves to just how customers and brands will use this platform. As consumers start to see AI as a normal part of everyday life the role of voice assistants and the evolution of conversational commerce will only increase. While voice assistants still have a way to go the future looks promising.
Continuing investment in robotics
On the podcast, Roger Barga, General Manager at AWS Robotics shared insight into Amazon’s use and development of robots. Amazon has been heavily investing in robotics for almost a decade. For many years now Amazon has been using robots in their fulfillment centers to help with a wide variety of tasks. They have been innovating with robotics working alongside humans to deliver products to customers and currently have roughly two hundred thousand robots in use helping deliver products to our customers. These robots help with many of the sorting, picking, and packing tasks necessary to get packages out to the customer as quickly as Amazon is able to. Robots provide incredible efficiencies for Amazon’s operations.
For companies who aren’t as big as Amazon and might be working on a tight budget, building and deploying robotics into their organization hasn’t always been possible. Traditionally, building robots from scratch has been a challenge. Amazon recently released AWS RoboMaker service to make it much easier to develop, test, and deploy intelligent robotics applications at scale. Amazon found that customers were spending 80%-90% of their time writing code that was necessary but not providing much value add. AWS RoboMaker provides the tools to make building intelligent robotics applications more accessible and removes much of the heavy lifting of robotics development.
Intelligent search
Another Amazon executive, Srikanth Thirumalai, Vice President, Search at Amazon, shared how AI is being used to improve customer experience. You might not necessarily think of Amazon as a search engine but with hundreds of thousands of products available for purchase, they have a very robust search engine powering their site with both text and visual search. Machine learning is used throughout the entire customer’s search journey on the site through features such as predictive typing, optimising page layouts, or recommendations and suggested products.
In fact, without machine-learning powered search, hyperpersonalized offers are not feasible. Hyperpersonalization is the AI-enabled concept of treating each person as an individual and not generally bucketing them into certain groups. Using machine learning, companies can develop a unique profile of each individual, and have that profile learn and adapt over time for a wide variety of purposes including displaying relevant content, recommending relevant products, website layout optimization and more. Amazon was one of the very early adopters of this pattern applying personalization and making recommendations across Amazon for the last twenty years. AI-enabled systems are increasingly blurring the lines of search and recommendation systems. By knowing which customer is searching for what product, AI will now make it possible to bring up very relevant results and personalized recommendations.
For many decades now Amazon has pioneered the use of AI and machine learning in many parts of their business. As AI continues to become more widely adopted by organizations of all types you can expect similar stories from large organizations spreading the use of machine learning throughout the enterprise.