The efficiency of the recruitment industry is set by the ability of employers and agents to match candidates to vacancies. With a typical hire taking up to six weeks and costing upwards of $4,000, more accurate matching can clearly lead to reduced waste of time and resources, and an impact on a business's bottom line. One of the inspirations behind Helena – an "AI head hunter" created by recruitment platform Woo – is the recommendation engines developed by online retailers like Amazon. Impressively, according to Woo CEO and founder LiranKotzer, early results show that employers are accepting for interview 52% of candidates put forward by Helena. This compares very favorably to performance of human recruitment agents, where the average figure is around 20%, and far better than relying on respondents to postings on job boards – where just 2.5% of applicants will be suitable for interview.

In Gartner's 2018 Market Guide for CRM in Tier 1 Retail, Kelsie Marian states, "The next phase of CRM includes leveraging advanced technologies to better understand and predict customer behavior across a variety of contextual settings." I was reading Gartner's report because Symphony RetailAI was listed as a representative vendor for our SR Personalized Marketing solution. I was struck by how Kelsie's statement is reflected back to me almost weekly. In my job, I'm fortunate to speak to executives both within my company and outside. I'd expect our execs to promote technologies that help retailers understand customers and predict their behavior – the essence of AI-enabled customer relationship management.

Most retailers are stuck in the past, using obsolete forecasting systems to make decisions. But the models of ten years ago won't get the job done today. If you want success in today's highly competitive retail market, you'll need to utilize the benefits of big data and machine learning. The problem is that most retailers aren't technically savvy enough to build these systems on their own. That's where Evo comes in.

This is a guest blog post by Andrea Tabacchi, the Solution Architects team lead at Memsource. Memsource is always looking out for exciting new integrations that enhance its cutting-edge translation solutions. With machine translation (MT) continuing to be a hot topic in the localization industry, Memsource is focusing on integrating with innovative MT engines that meet customers' growing MT needs. In particular, Memsource strives to offer neural machine translation (NMT) engines, such as Amazon Translate. NMT is proving to be a highly influential technology. The quality of NMT output continues to improve, making it a powerful productivity tool and therefore more in demand.

Robotic workers might sound like science fiction, but they're increasingly becoming science fact. Automation has already been blamed for taking jobs away from humans. And as technology such as autonomous vehicles and cashier-free convenience stores continues to improve, those fears are only going to get worse. Throwing humanoid robots into the mix could take those fears to a whole new level. And there are already some signs that robots are doing the jobs humans used to do.

Imagine meeting a personal stylist online who will make specific recommendations for clothing you need to purchase -- including matching items and accessories. Take that a step further, and your stylist will be able to parse recommendations based on your size and budget, as well as available inventory. Finally, you can easily get input from your trusted friends online, make a final decision, and then click once to purchase. With recent improvements to messaging, chatbots, and artificial intelligence technology, that scenario is entirely possible, and the stylist doesn't even need to be a real person. Artificial intelligence (AI) platforms can do much more than comb through product listings and return search results.

If you've been paying attention to the application of artificial intelligence in retail, you may feel like the buzz around the topic has gone from zero to "arrived" in less than a year. In retail time, even at the speed of the modern consumer, that is incredibly fast. Some of the hype has come from activity around specific use-cases for the application of AI in retail. Predictive analytics / forecasting – This is forecasting with an emphasis on either products, or customers. For products, retailers appear to be focusing on three main areas of opportunity.

It isn't easy for a business to stick around for 45 years and remain relevant in the digital age, even if your brand is as iconic as Roots Canada – but to avoid being left behind, the Toronto-based clothing retailer is using its website redesign to meld its brick and mortar store experience with its digital marketing strategy. In March the retailer finished giving its site, Roots.com, a complete facelift, using artificial intelligence (AI) to create a more modern, streamlined appearance from start to finish, including the homepage, shopping cart and end pages. Roots' biggest goal, said Jamie Garratt, CEO of Idea Rebel, the digital marketing agency that Roots hired to redesign the website, was to redesign not only the look but also the feel of the site. It was all about making a positive, user-centric experience, he told ITBusiness.ca. "You can't pull the wool over consumer's eyes anymore when it comes to building a website," said Garratt.

Digital transformation is driving a new breed of retail that will take consumers where they've never been before, writes Microsoft's Christi Olson. The following is a guest post by Christi Olson, head of evangelism for search at Microsoft. The views are the author's own. Like many industries today, retail is in an extreme state of disruption. In five years, 25% of malls will be gone.

Creative agency Redpepper made a AI-powered robot that can pinpoint Waldo in 4.45 seconds ("better than most five year olds," according to Redpepper). The robot is complete with a rubber hand that points to Waldo on the page. The agency used Google's AutoML Vision service to train AI on photos of Waldo. The drag-and-drop tool allows users to train AI tools without previous coding knowledge, and has been used to categorize anything from ramen based on the shops they came from to the types of attire carried in an online retailer. Matt Reed, a Creative Technologist at Redpepper who led the project, got 62 Waldo heads and 45 full-body Waldos from Google image search, then fed the data Google's AutoML Vision.