Note: This article is provided by Reshift Media, a Canadian-based digital marketing and development organization specializing in retail businesses. Artificial Intelligence (AI) is helping lead a digital transformation in the retail industry, with recent reports forecasting that global spending on AI systems will soar from $85.3 billion in 2021 to greater than $204 billion by 2025. These funds are allocated to a number of areas in AI development, but for most retailers, improving customer experience is at the forefront. With everything that the retail industry has faced throughout the pandemic, such as changes in consumer buying behaviour, the implementation of new technologies and BOPIS strategies, and the substantial rise of e-commerce, improving the customer experience has become essential. AI can help streamline that process for retailers in a variety of ways.

Nourish Bloom opened in January, introducing a high-tech experience that aims to make grocery shopping as simple as possible. We're all familiar with the traditional way of buying items at a grocery store where you check out with a cashier. The store's owners say it's the world's first Black-owned autonomous grocery store. "No one likes waiting in line, fumbling for their payment," said co-founder Jilea Hemmings. Jilea Hemmings and her husband Jamie opened Nourish Bloom in January, introducing a high-tech experience that aims to make grocery shopping as simple as possible.

Data Science is an exciting field for knowledge workers because it increasingly intersects with the future of how industries, society, governance and policy will function. While it's one of those vague terms thrown around a lot for students, it's actually fairly simple to define. Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data, and apply knowledge and actionable insights from data across a broad range of application domains. Data science is thus related to an explosion of Big Data and optimizing it for human progress, machine learning and AI systems. I'm not an expert in the field by any means, just a futurist analyst, and what I see is an explosion in data science jobs globally and new talent getting into the field, people who will build the companies of tomorrow. Many of those jobs will actually be in companies that do not exist yet in South and South-East Asia and China.

Though it seems like something out of a sci-fi movie, machine learning (ML) is part of our day-to-day lives. So often, in fact, that we may not always notice it. For example, social networks and mobile applications use ML to assess user patterns and interactions to deliver a more personalized experience. However, AWS services provide many options for the integration of ML. In this post, we will show you some use cases that can enhance your platforms and integrate ML into your production systems.

A DECADE AGO Amazon started to introduce robots into its "fulfilment centres", as online retailers call their giant distribution warehouses. Instead of having people wandering up and down rows of shelves picking goods to complete orders, the machines would lift and then carry the shelves to the pickers. That saved time and money. Amazon now has more than 350,000 robots of various sorts deployed worldwide. But it is not enough to secure its future.

Did you miss a session from GamesBeat's latest event? Inventory management is a growing challenge in retail and logistics, particularly as the pandemic places a strain on the supply chain. According to a 2018 survey by Coresight Research and Celect, inventory decisions -- including overbuying, buying the wrong type of products, and misallocating inventory -- account for an estimated 53% of unplanned markdown costs for retailers. Similarly, a Stitch Labs survey of warehouse operators found that human error was the top cause of inventory fulfillment issues for a majority of the respondents. Some problems stem from the barcode- and label-based systems that companies use to keep track of products, which can be susceptible to incorrect data, poor print quality, and other inconsistencies. Automation technologies have been proposed as a solution in light of the growing trend toward digitization in logistics.

When is it a bad idea to bet on the future? First, we're seeing massive shifts in customer behavior during the pandemic -- behaviors that look likely to last. Second, the emergence of Millennials and Gen Z as significant market segments suggest that those new behaviors are just the beginning. Third, and most importantly, the big guys of digital -- Apple, Facebook, Google, Amazon, and Microsoft -- are all placing big bets that threaten to reshape the landscape for every business in due time. So, maybe a better question is "How can you bet on the future to win?" We'll take a look at who's leading the way towards the future, some useful frameworks for how to think about betting on the future, and how to place smart bets for your business… bets that you can win. Here are the show notes for you. Here are the regular show notes detailing links and news related to this week's episode.

Retailers need more decision automation, faster coordination of supply chains, and faster interactions with consumers, which means they will increasingly rely on AI. Automated decisioning systems will soon be making fine-grained micro-decisions on the retailer's behalf, impacting customers, employees, partners, and suppliers. But these systems can't run autonomously -- they need human managers. Every system for making micro-decisions needs to be monitored. Monitoring ensures the decision-making is "good enough" while also creating the data needed to spot problems and systematically improve the decision-making over time.

Labor shortages have been a topic of conversation and concern as we continue to reel from the economic disruption of the pandemic and small business struggle to bounce back. Paired with this, an increase in customer demand for fast service makes it hard to keep up. Fast food restaurants will leverage AI to meet demands despite the labor shortages afflicting them. AI will automate order taking, NLP and recommendation systems will speed drive through times and improve recommendations. In large supermarkets and big box stores, retailers will use intelligent video analytics and computer vision to create autonomous, self-checkout systems and cashier-less shopping.

Machine learning has become ubiquitous in many industries. Online retailers use machine learning to predict what customers will buy, doctors use it to diagnose illnesses, and marketers use it for targeted advertising. These are just a few examples of how machine learning can be applied to improve our society. The types of machine learning are shown in Figure 1.