Global Artificial Intelligence in Retail Market was valued US$993.6 Mn in 2017 and is expected to reach US$8314 Mn by 2026, at a CAGR of 30.41% during a forecast period. The report is majorly segmented into types, technologies, solutions, services, deployment modes, applications, and region. Further, Artificial Intelligence in a retail market based on type includes online and offline retail. Technology segment is sub-segmented into machine learning and deep learning, Natural Language Processing, and others. Solution segment in the report comprises product recommendation & planning, customer relationship management, visual search, virtual assistant, price optimization, payment services management, supply chain management & demand planning, and others which include website and content optimization, space planning, and fraud detection.

An automatic-speech-recognition system -- such as Alexa's -- converts speech into text, and one of its key components is its language model. Given a sequence of words, the language model computes the probability that any given word is the next one. For instance, a language model would predict that a sentence that begins "Toni Morrison won the Nobel" is more likely to conclude "Prize" than "dries". Language models can thus help decide between competing interpretations of the same acoustic information. Conventional language models are n-gram based, meaning that they model the probability of the next word given the past n-1 words.

This chart shows AI use by retail organizations worldwide in 2018, by operating model. When it comes to artificial intelligence, machine learning is retailers go-to AI use across all business types, according to Capgemini. Artificial intelligence is an umbrella term, encompassing a wide variety of automated technologies. Machine learning refers to the process of building a system where a user can feed it new information and it can process that information based on previous data the machine has received, making decisions and taking actions without being explicitly programmed to do so. The study also found that retailers are primarily using AI for consumer-facing projects.

We propose a novel learning framework to answer questions such as "if a user is purchasing a shirt, what other items will (s)he need with the shirt?" Our framework learns distributed representations for items from available textual data, with the learned representations representing items in a latent space expressing functional complementarity as well similarity. In particular, our framework places functionally similar items close together in the latent space, while also placing complementary items closer than non-complementary items, but farther away than similar items. In this study, we introduce a new dataset of similar, complementary, and negative items derived from the Amazon co-purchase dataset. For evaluation purposes, we focus our approach on clothing and fashion verticals. As per our knowledge, this is the first attempt to learn similar and complementary relationships simultaneously through just textual title metadata. Our framework is applicable across a broad set of items in the product catalog and can generate quality complementary item recommendations at scale.

According to a Deloitte report, "A strong customer experience can not only lead to stronger financial performance but also form the basis for competitive differentiation. Successfully differentiating the brand both in terms of products and the experience can have a positive impact on a company's bottom line with higher conversion rates and increased customer loyalty." Not surprisingly, Gartner has predicted that over 50% of organizations will redirect their investments to CX (customer experience) innovations in 2019. But where should you start -- is that the question echoing in your mind as you read these words on your screen? Well, if you ask us, investing in customer support technology can give you great results in terms of improving your CX. A report indicates that "62% of organizations view customer experience provided through contact centers as a competitive differentiator."

When it comes to artificial intelligence, machine learning is retailers go-to AI use across all business types, according to Capgemini. Artificial intelligence is an umbrella term, encompassing a wide variety of automated technologies. Machine learning refers to the process of building a system where a user can feed it new information and it can process that information based on previous data the machine has received, making decisions and taking actions without being explicitly programmed to do so. The study also found that retailers are primarily using AI for consumer-facing projects. Seventy-four percent of AI use cases are for customer-facing projects, while only 16 percent are dedicated to operations.

In December, Amazon launched a crowdsourced Q&A platform into beta with the goal of improving Alexa's ability to answer questions. That feature, Alexa Answers, is now live to all. Amazon says the feature was well-received by the early community of invite-only participants, who have since contributed hundreds of thousands of answers that have been shared with Alexa customers millions of times. To differentiate these answers from other Alexa responses, they're attributed to "an Amazon customer." As the company explained at launch, there are thousands of answers that had previously stumped Alexa, like "Where was Barbara Bush buried?," "Who wrote the score for Lord of the Rings?," "What's cork made out of?," and "Where do bats go in the winter?"

Manufacturing robots are breaking free of their cages. Sensor technology and artificial intelligence have now progressed to the stage where collaborative robots (co-bots) can work safely alongside humans in a wide variety of applications, including supermarkets, farms, and hospitals. But manufacturing is where co-bots will really help to boost productivity far beyond that of humans and robots working separately. As the market expands, however, businesses must prepare appropriately to get the most out of co-bots and avoid costly, if not dangerous mistakes. Bringing robots into close quarters with humans is a huge advance in our technological progress, and will allow us to achieve new levels of intricacy and minimize risk of injury, most notably in manufacturing.

Artificial intelligence (AI) and machine learning (ML) are starting to play a bigger role in retail, foreshadowing what's to come in the new decade of the 2020s. Walmart, for instance, hopes to reduce checkout theft by turning to cameras powered by AI, with deployments underway in some 1,000 stores. "The retailer began investing in the surveillance program, dubbed Missed Scan Detection, several years ago in an effort to combat shrinkage – loss due to several causes including theft, scanning errors, waste and fraud," the report stated. "The AI-powered cameras were rolled out to more than 1,000 stores about two years ago, and the retail giant has seen positive results since then, according to [Walmart spokeswoman LeMia] Jenkins, who said shrinkage has reduced in stores where the cameras have been added." By incorporating visual recognition technology and artificial intelligence into their business models, retailers such as Neiman Marcus, IKEA, H&M and west elm are leveraging mobile devices and AI to provide advanced customer services.