Realizing AI's full potential on a mass scale will require more than just executives' enthusiasm; becoming a truly AI-first enterprise will require a significant, sustained investment in cloud infrastructure and strategy. In 2024, the cloud has evolved beyond its initial purpose as a storage tool and cost saver to become a crucial driver of innovation, transformation, and disruption. Now, with AI in the mix, enterprises are looking to the cloud to support large language models (LLMs) to maximize R&D performance and prevent cybersecurity attacks, among other high-impact use cases. A 2023 report by Infosys looks at how prepared companies are to realize the combined potential of cloud and AI. To further assess this state of readiness, MIT Technology Review Insights and Infosys surveyed 500 business leaders across industries such as IT, manufacturing, financial services, and consumer goods about how their organizations are thinking about--and acting upon--an integrated cloud and AI strategy.

Today's modern enterprises are collecting data at exponential rates, and it's no mystery that effectively making use of that data has become a top priority for many. According to a recent survey of 2000 global enterprises by McKinsey & Company, 47% of organizations have embedded at least one AI capability in their standard business processes. This is up from 20% in 2017 and it's clear that this growth has created a global race to enabling the next important evolution of business as we know it: The AI-first enterprise. But what does this actually mean? With investment in AI technologies poised to reach $9.5 billion over the next three years, the imminent opportunity involves embedding data and machine learning intelligence across the business at scale -- predicting the next best move for growth, making every product a data product, or creating entirely new data-driven revenue streams.

How do market-leading organizations help data science teams do their best work within the constraints of the enterprise? Data science teams need fast access to business data and a diversity of tools for end-to-end machine learning that can make it challenging for IT to quickly enable them while maintaining data governance and controlling infrastructure costs. In this webinar, discover how Cloudera Machine Learning (CML), part of the new Cloudera Data Platform (CDP), brings the agility and economics of cloud to self-service data science on governed business data at scale. A consistent user-experience that doesn't change when the business moves data or infrastructure The webinar includes a live product demo that will highlight features for IT and end-users.