Staying in tune with consumer demands can be challenging, especially when these demands change almost daily. Tracking consumption patterns to predict demand is a monumental task. Previously, solving this problem involved spreadsheets or legacy statistical methods. But with automation and machine learning, artificial intelligence can help organizations slice through mounds of data to get more accurate and timely insights on consumer demand . Machine learning can build forecasts for hundreds of thousands of items and consider all of the minute details for each one of them, from seasonality to sales history. For example, automated machine learning can easily create separate models on clusters of products, using unsupervised ML techniques, to make predictions even more granular such as by store, week, and SKU.

The advantages of automated machine learning in the front office, the importance of problem framing, and how automated machine learning techniques expand the problem space that can be investigated. Plus, why the choice of machine learning algorithm should be seen as just another parameter to search on.

Introducing DataRobot Pathfinder The potential impact AI will have on the global economy has been estimated to be huge -- about $13 trillion by 2030 (McKinsey). However, even with all the research and media around AI, many executives are still perplexed as to how AI can improve the profitability of their individual organizations. From the difficulties of choosing the right use case to the intricacies of adopting predictions into existing workflows, AI comes with a wide array of challenges that prevent organizations from maximizing the returns on their AI investments.