Picture this: you turn up the volume in your favourite music streaming service and set off on your daily drive to work using your favourite web mapping service. All while enjoying a playlist that is auto-curated for you and without having to manually enter where you want to go. These services just seem to know what you would like and where you are going. You probably don't think about the complex machine learning and analytics that are combining in the background to give you a pleasant commute. Trends in historical drives and your recently played tunes power artificial intelligence (AI) predictions about which song to listen to and which turn to take next.

Maybe we are interested in knowing what's likely to happen in each game they play. In this case, knowing the total number of home runs hit over the course of the season isn't going to be quite as helpful – to make an accurate forecast about the next game, we need to have game-level data. Is the game being played at home or away? The answers to these questions are all crucial to generating an accurate prediction of the Giants' next game. This type of forecast is called a'bottom-up' or'rollup'-based forecast because predictions are made for each game based on the Giants' probability of winning each matchup.

Nearly half of companies now say that they're using some form of artificial intelligence, according to McKinsey research that Salesforce shared at Dreamforce 2019, yet seven out of ten companies report they're seeing little to no value from their AI projects so far. This gap exists, Salesforce executives asserted, because too many companies are struggling to overcome data-management and data-science technical hurdles, so they have yet to put AI into production at scale. Salesforce's alternative is Einstein and Einstein Analytics, both of which offer prebuilt, CRM-embedded predictions and recommendations along with the ability to build AI apps and answer company-specific business questions without coding or data science expertise. There's no question that AI is of interest to organizations and that the Einstein and Einstein Analytics promise of pre-built capabilities and declarative, no-code/low-code app and model development is compelling. Yet Salesforce executives frankly acknowledged that Einstein and Einstein Analytics have only scratched the surface of potential adoption, with Sales Cloud use cases leading the way.

Salesforce Einstein and Einstein Analytics have footholds in sales, but these 15 #DF19 announcements add artificial intelligence use cases across the enterprise. Nearly half of companies now say that they're using some form of artificial intelligence, according to McKinsey research that Salesforce shared at Dreamforce 2019, yet seven out of ten companies report they're seeing little to no value from their AI projects so far. This gap exists, Salesforce executives asserted, because too many companies are struggling to overcome data-management and data-science technical hurdles, so they have yet to put AI into production at scale. Salesforce's alternative is Einstein and Einstein Analytics, both of which offer prebuilt, CRM-embedded predictions and recommendations along with the ability to build AI apps and answer company-specific business questions without coding or data science expertise. There's no question that AI is of interest to organizations and that the Einstein and Einstein Analytics promise of pre-built capabilities and declarative, no-code/low-code app and model development is compelling.

AI for BI is a key tenet of the third generation of analytics. Sometime in the middle of the current decade, features such as augmented intelligence, machine learning and natural language processing started to become key parts of business intelligence platforms. In the years since, although analytics platforms have progressed, AI for BI still hasn't matured to the point where analytics tools can truly free up humans from the mundane tasks associated with data analysis, to the point where data analysis is part of everyday applications rather than a stand-alone application unto itself, or to the point at which BI platforms can predict for humans a likely outcome before they even request it. And it hasn't gotten to the point where it's accessible to everyone. In September, Constellation Research released a report entitled "Augmented Analytics: How Smart Features Are Changing Business Intelligence."

Salesforce has revealed the launch of its most advanced CRM analytics platform yet. Called Einstein Analytics, this platform leverages artificial intelligence to the analytics workflow, allowing users to automatically create CRM insights. The AI will also be able to recommend actions which improve customer service and boost sales, as well as helping optimise marketing campaigns. Salesforce says that the platform will come with role-specific KPIs, providing users with an "intuitive, self-service way" to gain access to newest data from any device. One of the tool's main features is called Einstein Discovery – which conducts thousands of statistical checks to check the validity of trends and offer'intelligent explanations' to the patterns it identifies.