{"id":2559,"date":"2018-06-28T00:00:00","date_gmt":"2018-06-28T00:00:00","guid":{"rendered":"https:\/\/azure.microsoft.com\/blog\/reasons-why-retail-and-consumer-goods-brands-should-address-artificial-intelligence-today"},"modified":"2023-05-11T15:38:52","modified_gmt":"2023-05-11T22:38:52","slug":"reasons-why-retail-and-consumer-goods-brands-should-address-artificial-intelligence-today","status":"publish","type":"post","link":"https:\/\/azure.microsoft.com\/en-us\/blog\/reasons-why-retail-and-consumer-goods-brands-should-address-artificial-intelligence-today\/","title":{"rendered":"Reasons why retail and consumer goods brands should address Artificial Intelligence today"},"content":{"rendered":"

The digital experience (DX) era is here, and AI is one of the primary technologies to fuel productivity and innovation in the retail and consumer goods industry. Brands that take a wait and see approach may find themselves quickly outpaced by their competitors. And by competitors, I mean not just born-in-the-cloud e-commerce players, but forward-thinking omnichannel retailers who focus on winning customers and evolving retail at scale.<\/p>\n

Within the spectrum of digital transformation, AI is not a new technology. It is moving from its research roots to entering the mass market. This is made possible by the growth of cloud computing, availability of big data, and years of improved algorithms developed by researchers. At its core, AI gives computers decision-making capabilities to solve problems in a more natural and responsive way, as compared to the practice of pre-programmed computer routines of today. AI will be an imperative for optimization, automation, scale and most importantly, (gulp) survival.<\/p>\n

Cloud computing and big data are accelerating AI technology<\/h2>\n

According to Accenture<\/a>, unlimited access to computing power and the growth in big data are creating the right environment for AI. Analyzing data requires massive compute and storage; the cloud provides an efficient way to run AI systems. A general consensus is that public cloud spending is increasing at a growing rate every year. For example, Forbes<\/a> states that the \u201ctotal public cloud market will be $178 billion in 2018, up from $146 billion in 2017, growing at a 22 percent compound annual growth rate.\u201d Further, the digitization of everyday life is enabled by the proliferation of devices and the cost effectiveness of sensors. That in turn has created massive amounts of data available for computers to learn from. Deep learning and computer vision will thrive on the accessible data. By 2025, big data is estimated to grow to 180 zettabytes, up from less than 10 zettabytes of data in 2015.<\/p>\n

The industry is placing bets on AI<\/h2>\n

A key factor in this discussion is the value creation attributed to AI technology. According to a McKinsey study<\/a>, it is estimated that AI could potentially generate $4 billion to $8 billion in annual value for the global retail economy, and $2 billion to $5 billion in consumer goods. According to a study on Forbes<\/a>, 81 percent of IT leaders are currently testing, or planning to invest, in AI.<\/p>\n

According to angel.co, since July 2017, there are 4,701 AI startups, up from 2,200 AI startups. Machine Learning, Natural Language Processing, and Computer Vision startups make up the top three categories within AI technologies. This signals the anticipation of demand, and the opportunity to transform.<\/p>\n

Customer-centric technology that responds using natural language and behavior<\/h2>\n

The technologies that define AI fall into four categories, sense, learn, reason and act. According to the Microsoft e-book, The Future Computed<\/a>, these capabilities will enable computers to more naturally interact in the world around them through technologies that enable vision, speech, language and knowledge.<\/p>\n

The power of leveraging these capabilities in combination can make an experience with a customer more natural and seamless. On a macro level, AI technology lends itself to functions within an organization with massive amounts of structured and unstructured marketing, sales, logistics, operations, finance, and call center data. If you go a few clicks deeper, use case domain examples include: data-driven accounting, data-driven hiring, lead generation, fraud and debt analysis, inventory optimization, pricing and promotion, allocation, demand forecasting, personalized recommendations, search, and more. These examples illustrate opportunities to use AI to improve existing analytics use cases. Per McKinsey research<\/a>, potential for incremental value from AI over other analytics techniques is 87 percent and 55 percent for retail and consumer goods, respectively.<\/p>\n

I\u2019d be overzealous if I didn\u2019t acknowledge that there are pre-requisites to leveraging AI in today\u2019s organizations. Obstacles include data silos and legacy technology. To produce value, and leverage AI\u2019s full potential, you must have data to process. The state is not as important as long as it can be processed and remain within regulatory compliance in the cloud. In other cases, data may not be captured even though it is generated, so again, the pre-requisite is having available data to process. Now is the time to get an organization\u2019s data estate in order. We\u2019ve seen this taking shape with efforts such as Customer 360, Unified Commerce, Order Management, etc. where data is the backbone of the effort.<\/p>\n

The democratization of AI is underway<\/h2>\n

Microsoft\u2019s AI platform provides core capabilities through a common set of APIs, and tools for practitioners who want to be hands-on in the creation of their own custom AI models. They can do so leveraging an AI foundation built on ethical principles to ensure AI helps all people.<\/p>\n

For organizations looking to get started, Microsoft offers pre-trained AI services with pre-built AI models. These include Azure-powered Cognitive Services<\/a> to infuse apps with intelligent algorithms to see, hear, speak, understand and interpret your user needs through natural methods of communication. Conversation AI integrates naturally fluid conversation into the consumer experience. Azure Bot Service<\/a> and LUIS<\/a> can aid in creation, deployment, and management of a digital assistant that allows consumers access to your brand 24\/7.\u00a0<\/p>\n

AI services and tools are also integrated into the Azure Portal and Visual Studio<\/a>. There are tools for developers to train and deploy their own models such as TensorFlow, and Cognitive Toolkit<\/a> to name a few. Lastly, Microsoft is invested in solutions where AI can address industry-specific use cases. There are options from build to buy, first-party or partner-led to engage with Microsoft\u2019s AI technology.<\/p>\n

Where to start<\/h2>\n

Learn best practices for managing retail data by reading this overview on Data Management<\/a>. Visit Azure AI<\/a> to learn more about Microsoft\u2019s AI tools and services, including the opportunity to try for free Cognitive Services<\/a> that infuse your customer touch points with natural methods of communication.<\/p>\n

What other opportunities and challenges do you see with AI and cloud computing? This is just the tip of the iceberg for retail and consumer goods. I welcome your feedback and questions.<\/p>\n","protected":false},"excerpt":{"rendered":"

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