In short, these are the three challenges highlighted in the webinar: Key Challenges to AI Success Challenge #1: Data is the key to success, but difficult to harness The survey results found that 96% of companies cite data-related challenges with AI projects..These challenges have a direct impact on the amount of complexity in delivering an AI solution to production — with an AI project taking an average of 6 months to complete..Interestingly, more than half of that time is typically spent on data preparation activities..Challenge #2: Data science and engineering silos, resulting in poor collaboration Another contributor to AI projects getting stalled is the inherent silos between and among data science and engineering teams..80% of teams cite collaboration as a challenge with technology skills gaps and project management and oversight as the primary drivers of this divide — resulting in communication breakdowns and friction that slow productivity..Challenge #3: The explosion of ML frameworks and technologies adds complexity As AI continues to evolve, the number of frameworks and tools available are increasing at a rapid rate..Garofalo cites that organizations are using an average of 7 different tools across data processing, machine learning, data streaming, and deep learning..This fast-moving technology landscape is adding enormous complexity to the overall process, hindering companies who don’t have the expertise and resources to take advantage of the latest available frameworks and tools..Best Practices from Industry Leaders The webinar not only highlighted the drivers and challenges of AI adoption but also offered tips to overcome these challenges..Pat McDonough, VP of Customer Success at Databricks, shared stories and experiences from a number of our customers including Overstock.com, Regeneron, and Riot Games and the best practices they have employed to help them extract benefits from their work around AI..Here are some of the key best practices shared by industry-leading companies: Leverage the cloud to simplify infrastructure, reduce on-prem costs and provide the elastic scale your teams need to meet the demands of modern analytics workflows..Make data and the output of the data (e.g. models) available to all teams to foster transparency, collaboration and productivity.. More details