The journey toward successfully deployed machine learning operations (MLOps) starts with data, says global head of machine learning operations and platforms at Kraft Heinz Company, Jorge Balestra. Curating well-organized and accessible data means enterprises can leverage their data volumes to train and develop AI and machine learning models. A strong data strategy lays the foundation for these AI and machine learning tools to use data to detect supply chain disruptions, identify and address cost inefficiencies, and predict demand for products. "Never forget that data is the fuel, and data, it takes effort, it is a journey, it never ends, because that's what is really what I would call what differentiates a lot of successful efforts compared to unsuccessful ones," says Balestra. This is especially crucial but challenging within the CPG sector where data is often incomplete given the inconsistent methods for consumer habit tracking among different retailers.

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.

AI can analyze large amounts of data in order to identify patterns and hidden correlations, which would otherwise take humans considerably longer to comprehend and decipher. It can also be fed with a wide range of variables, which gives the engine more flexibility in its analysis. AI is revolutionising the overall healthcare landscape Automating healthcare is then, perhaps, a result of this changing healthcare landscape and the growing use and rapid adoption of technology. The healthcare sector is experiencing a dramatic shift in how goods and services are delivered. There are several driving factors in this transition, including a shift away from treating shorter episodes of illness towards a greater focus on long-term wellness and prevention. In addition consumers and patients alike show the deviation from and changing expectations.

There are many types of eCommerce AI chatbots available today that help with all different aspects of an eCommerce store. AI chatbots can boost sales and revenue by showing shoppers exactly the product they were looking for which was often hidden in complex menus, acting as a shopping assistant providing product recommendations, and aid customer support by immediately answering customer questions about a product, order status, and more. An AI chatbot built for eCommerce is not that expensive and has a high rate of return. The average increase in revenues when a shopper engages with the chatbot is 20% to 40%. There are also other benefits such as an average reduction in support tickets of 25% to 45%.

Over the years, the basic retail experience has remained more or less the same for the consumers. You go to a store, you look for the right product, and you make a purchase. But for the retailers, it is ever-changing. Analyzing consumer behavior is one of the biggest challenges that CPGs all around the world face. With increasing complexities, traditional auditing methods have proved inefficient.

Grand View Research estimates the global AI market will grow at a compound annual rate of 57% between 2017 and 2025, reaching $36 billion. Forrester predicts that 2020 is the year executives will focus on how to drive and measure the value of their investments in AI. A recent survey of healthcare executives conducted by Optum found that not only is use of AI on the rise, but also that most executives expect a faster return on their investments than first anticipated. What's missing from these lofty projections are more substantive discussions about what's required to ensure that AI can deliver on its promise, such as the importance of data governance and management. There are also fewer conversations about the role AI and machine learning can play in the healthcare supply chain, compared with other areas, such as improved disease diagnosis and drug development. But when you stop and think about how AI is being applied elsewhere in healthcare, you begin to see implications and opportunities for the supply chain.

Retail analytics is a process that helps to provide crucial data for businesses with regards to inventory, sales, supply chain activities, consumer demand, and more. By implementing an analytics-driven retail environment, organizations can make better decisions for procurement, marketing, merchandising, and other operational considerations. This, in turn, enables retailers to create a better buying experience and for identifying opportunities for organizational improvement. What are the benefits and importance of retail analytics? What are some current trends in retail analytics?

The 71-year-old fast-fashion chain is aiming to arrest a slump in same-store sales that has lasted 10 straight quarters as it faces problems bedeviling the industry: A spike in online shopping has led to fewer customers visiting stores, and digital startups are putting up fierce competition. H&M has repeatedly slashed prices to clear out $4 billion of unsold goods, and its shares are down 56% in the past three years. H&M, like most retailers, relies on a team of designers to figure out what shoppers want to buy. Now, it's using algorithms to analyze store receipts, returns and loyalty-card data to better align supply and demand, with the goal of reducing markdowns. As a result, some stores have started carrying more fashion and fewer basics such as T-shirts and leggings.

Machine Learning will come to change the way you work, earn a livelihood, purchase and consume goods and services. Nearly every working professional and entrepreneur who keeps a finger on the pulse of the market would have heard or read the words'machine learning' by now. For many of them, trailblazing pioneers at the forefront of new technologies, like Steve Jobs and Elon Musk, have become universal role models. What one needs to realise is that what the personal computer and the smartphone were to Steve Jobs, machine learning (ML) could be for you – but you need to start now. Technology is already entering the market at a rapid pace, and the opportunities in this field are growing at an exponential rate.

Jones was the founder of Travelocity.com This has helped to place Jones as a powerful voice in the world of entrepreneurship and matters relating to travel and transport. As CEO of Travelocity for seven years, Jones helped transform it into a public company with three billion dollars in travel sales. He left Travelocity to help found Kayak.com, a company that altered the way travel was purchased. Today Jones is the chairman of Wayblazer and managing principal of ON Inc, a consultancy he founded to help companies in their transition to the digital economy.