This is an edited version of the AI section in Econsultancy's Future of Marketing report. The future of AI, whatever industry you want to focus on, is a strange discussion for the layman because the technology in its current state of maturity seems a mix of stunning triumph and inarguable work-in-progress. I can speak to my phone, say "Okay, Google, show me photos of Ted" and my Pixel will quickly display images of my oldest son that it has accurately categorised in my Google Photos app. There are cars that can drive themselves, to a large extent. And yet, current AI systems have difficulty with causality and don't seem to demonstrate reasoning.

We've reached the end of January – hooray! To celebrate, there's a myriad of stats to get your teeth into in this week's roundup, including data on customer recommendations, digital out-of-home (DOOH) and financial results for some big companies. Before we get started, subscribers can take a look at our Digital Statistics Index for more? A survey conducted by Vitreous World and Phrasee has found that 68% of global marketers feel they don't understand what AI is and think it's simply an overused buzzword. Additionally, a worrying 67% of those surveyed say they don't know how to implement AI to effectively deliver results. This shows a large gap in knowledge when it comes to one of the biggest trends shaping the world of marketing today and the near future – and it is thought to be costing companies millions in revenue.

From visual search to computer vision, natural language processing to predictive modelling, machine learning underpins all kinds of innovations that are levelling the playing field by giving retailers of all sizes access to the same tools as behemoths like Amazon – and allowing them to develop cutting-edge online and in-store experiences. This in-depth briefing will look closely at a number of different applications for machine learning in retail, accompanied by examples of how retail brands are putting them into practice and how they translate to improvements in sales, processes, customer engagement, and the customer journey. It will examine both ecommerce and bricks-and-mortar retail, noting the differences in how machine learning is used in digital versus offline environments, before finally considering how this usage might evolve in the future.

Valuable marketing insights are hard to find in the mountains of data many marketers are now faced with – resurfacing them requires more sophistication than manual methods can deliver. Machine learning and AI has come to the fore in this regard. So much so that 92% of companies have increased their investment in AI and big data this year. But things are rarely simple when it comes to data analytics. Even early adopters admit that they are yet to become fully'data-driven': In most cases, the lack of a proper AI adoption strategy is to blame.

You've got them written up on the wall, on your mousepad, emblazoned across your screen or maybe even in your reception. They are, of course, your brand values, and they provide the basis for your marketing efforts – hence their importance. Brand values and personality define what your brand stands for and the response you want it to evoke. Communications from your brand might not always convey the intended personality or be consistent across even a small selection of brand touchpoints. We're often asked to test communications and assess brand perceptions, addressing things like their relevance, distinctiveness, alignment and consistency across different touchpoints like websites, promotions, ads, products and services.

Artificial Intelligence enables marketers to understand sales cycles better, correlating their strategies and spending to sales results. AI-driven insights are also helping to break down data silos so marketing and sales can collaborate more on deals. Marketing is more analytics and quant-driven than ever before with the best CMOs knowing which metrics and KPIs to track and why they fluctuate. The bottom line is that machine learning and AI are the technologies CMOs and their teams need to excel today. The best CMOs balance the quant-intensive nature of running marketing with qualitative factors that make a company's brand and customer experience unique.

The Barbican's latest exhibition explores the rise of artificial intelligence and the increasingly complex relationship between humans and technology. Visitors to'AI: More than Human' are able to delve into cutting-edge research projects by MIT, DeepMind, IBM and Google, among others, and get a glimpse of not only what is in store for AI, but its roots and its evolution. As Assistant Curator Anna Holsgrove tells Econsultancy: "One of the key messages is that although technology is developing, the desire to create intelligence and give it a physical form is an idea that dates back centuries and crosses cultures." The exhibition delves into everything from ethics to the future of our species, touching on several important themes. But what are the key learnings for marketers?

Potential applications of machine learning are very broad indeed. But, although AI conjures up images of robot butlers and promises big changes to customer experience, marketing teams who are already making use of AI-powered tech are doing so to sell. Machine learning refers to statistical approaches to train models which incrementally improve the output of a system. This sort of predictive modelling is used to increase the likelihood that a customer will take a particular action – this could be opening an email, clicking an ad or viewing a recommended product. Marketers are therefore mostly using machine learning in their push for personalisation – fuelled by a desire to improve sales.

Artificial intelligence and machine learning are an increasingly integral part of many industries, including marketing. But while we often talk about using or incorporating AI in marketing, what do we really mean by that? What does it look like in practice? Here are 15 examples of AI and machine learning in action in the marketing industry (P.S. remember to check out Econsultancy's Marketer's Guide to Machine Learning and AI). The practice of clustering customer behaviours to predict future behaviours began way back in 1998, with a report on'digital bookshelves' by Jussi Karlgren, a Swedish computational linguist at Columbia University. In the same year, Amazon began using "collaborative filtering" to enable recommendations for millions of customers. Fast forward to 2019, and some of the most successful digital companies have built their product offerings around the ability to provide highly relevant and personalised product or content recommendations – including Amazon, Netflix and Spotify.

With multiple facets to optimise for higher conversions, ecommerce brands often treat on-site search as an afterthought. By failing to address the search experience, you are losing customers, especially when you consider that shoppers who use the on-site search bar reportedly have a higher conversion rate and average order value. Machine learning may be hyped technology but it's already making a major difference to the on-site product search experience. Here are three examples demonstrating how progressive ecommerce brands are succeeding with it. Commonly misspelt words are a lost revenue opportunity for ecommerce companies.