According to a recent study by 451 Research, part of S&P Global Market Intelligence, "more than 90% of organizations that have adopted AI began development on their first AI project within the past five years." Though nascent, AI-enabled solutions are on the rise all around us. However, many of these initiatives still aren't meeting expectations--if they even make it to deployment. To succeed, leaders should select and manage AI projects with a thoughtful strategy driven by clear expectations, alignment to business goals, and iteration. Let's look at common hurdles organizations face when designing successful end-to-end AI projects, and how to overcome them.

With the acceleration of digital transformation in business, most CTOs, CIOs, and even middle management or analysts are now asking, "What's next with data?" and what ongoing role will technology play in both digital and data transformations. With this in mind, plus observations and discussions with many Tableau customers and partners, it seems that today's circumstances, behaviors, and needs make it the right time for predictive data analytics to help businesses and their people solve problems effectively. With growing, diverse data sets being collected, the analytics use cases to transform data into valuable insights are growing just as fast. Today, a wide range of tools and focused teams specialize in uncovering data insights to inform decision-making, but where organizations struggle is striking the right balance between activating highly technical data experts and business teams with deep domain experience. Until now, using artificial intelligence (AI), machine learning (ML), and other statistical methods to solve business problems was mostly the domain of data scientists.