AI and machine learning touch every industry today, but it's sometimes unclear how they improve the bottom line. In addition to automating tedious processes or helping you discover trends, can this tech boost sales and margins? The answer is absolutely "yes." Here are three ways we're seeing businesses harness the best of AI to achieve significant gains. AI reduces the reliance on marketing personas - which aren't very good predictors of what customers will buy.

Knowing how much and which kind of inventory your brand needs involves a complex web of data that companies often keep up with via spreadsheets or legacy systems that don't provide a full picture of the business. Syrup Tech, now armed with $6.3 million in new funding, is feeding all that data, like transactions, marketing and inventory, and combining it with other data, like social media trends and even the weather, to spit out predictive inventory recommendations using artificial intelligence and machine learning. This way, merchandisers and planners have better information on what they need and can reduce some of the waste. "I was at McKinsey previously, and was shocked to see merchandisers spend hours on spreadsheets," James Theuerkauf, co-founder and CEO of Syrup Tech, told TechCrunch. "My thought was to let the AI do the number-crunching and let the merchandiser make the creative decisions using the AI as support."

Human engagement matters more than ever to consumers, who want intuitive experiences that reduce friction across all touchpoints of their customer experience. At the same time, businesses are looking to automated engagement tools to drive efficiencies and improve the customer experience in a way that still feels personal and connected. Technologies such as artificial intelligence (AI) and machine learning (ML), for example, offer the kind of intelligence that amplifies the creative work of expert human designers and merchandisers to instantly predict intent, freeing up time for creativity and amplifying human efforts. These technologies can quickly crunch a lot of data on past and present actions to get to the intent of the shopper as they search. Correlating shopping history, demographics, and personal preferences all lead to a better result, and a prediction on the next intended action.

I have a colleague who was refinancing his house a couple months ago. He was working with one of the top five U.S. banks that he's been banking with for his entire adult life. The mortgage branch of the bank required him to send documentation confirming that he was a customer of the bank. After decades of being a loyal customer, being asked to prove that you've been a loyal customer is frustrating enough to go bank elsewhere. If you define customer experience (CX) and employee experience (EX) as two distinct entities, you may think that the "mobile form downloads" that were available to my colleague was a great customer experience because he didn't have to go into the branch to submit the documents.

Next week, Adobe is rolling out'visual similarity recommendations' which offer AI-powered product suggestions based on what consumers are considering purchasing. And this on-the-fly use of visual interpretation and recommendation is just the start. Now that more people are shopping online during the pandemic, brands need to facilitate the myriad ways people hunt, browse and discover products. But it's not so easy to do that if a shopper doesn't quite know what she wants until she sees it. Enter AI and visual similarity.

Assortment planning, an important seasonal activity for any retailer, involves choosing the right subset of products to stock in each store.While existing approaches only maximize the expected revenue, we propose including the environmental impact too, through the Higg Material Sustainability Index. The trade-off between revenue and environmental impact is balanced through a multi-objective optimization approach, that yields a Pareto-front of optimal assortments for merchandisers to choose from. Using the proposed approach on a few product categories of a leading fashion retailer shows that choosing assortments with lower environmental impact with a minimal impact on revenue is possible.

The rise of artificial intelligence may be proving a boon to marketers, but the older consumers are, the less comfortable they feel about it, according to new research into online fashion buying. The survey, conducted by YouGov for Swedish eCommerce company Apptus, found that only a quarter (24%) of UK adults aged 55 and over would like to see online fashion retailers adopt online systems to tailor their shopping experience. In contrast, 56% of those aged between 18 and 24 - the first to have grown up with smartphones, social media and Google - report they would like to see fashion retailers adopt online systems to tailor their shopping experience. Apptus UK country manager Andrew Fowler said: "The older generation has grown up in a world where there were no computers on desks at their first place of employment, they have seen technology replace jobs and, culturally, films like 2001 A Space Odyssey and Terminator have shown artificial or synthetic intelligence in a worrying light as machines go'rogue'. "The digitally immersed Generation Z, on the other hand, has grown up with technology that, arguably, enhances their social lives, entertains them and is comfortingly omnipresent - try telling a Generation Z that there is no WiFi.

In a recent report, the National Retail Federation projected online sales in 2017 will grow three times faster than in-store sales. The report suggests 51% of Americans prefer to shop online rather than in stores--a figure that jumps to 67% for millennials and 56% for Gen Xers. With Amazon accounting for 43% of online sales in the US, only store sales will be disrupted more in the coming months. Additionally, eCommerce sales are also expected to reach $4 trillion by 2020 – making it 14.6% of total retail spending that year. So what does this mean for retailers?