A strategic guide to operationalize and scale enterprise AI solutions
Recent progress in artificial intelligence may represent the most significant technological advancement in a generation, but progress is uneven.
Our recent industry survey confirms that most enterprise organizations still have not graduated beyond their first AI experiments and pilot projects.
Progress is slow at most enterprises because implementing AI depends on technical as well as organizational factors—and few resources exist to help leaders plan and strengthen their organizational foundations for AI.
In this document, we present a comprehensive AI Maturity Framework to close that gap. The AI Maturity Framework is designed to help leaders understand and prioritize the actions that will have the greatest impact on AI in their unique context. It catalogs five key dimensions that must be aligned to create and scale business impact with AI: Strategy, Data, Technology, People and Governance. It also explains how these dimensions define an organization’s maturity across five stages: Exploring, Experimenting, Formalizing, Optimizing and Transforming.
We also address how the AI maturity journey is unfolding across industries today. Throughout the document, we share the firsthand experience of our AI Advisory and Enablement practice as well as provide insights from an industry survey conducted with senior decision-makers between October 2019 and January 2020.
At a macro level, our survey confirms that fewer than one in ten organizations (7%) are mature enough to operationalize and scale AI. About twice as many (14%) are aligning Strategy, Data, Technology, People and Governance to join this vanguard. Another 52% are working through experiments to validate specific business cases for AI.
Our framework, cases and survey data help explain these statistics. We show how mature organizations tend to emphasize Strategy for AI, securing executive sponsorship and clarifying organizational roadmaps early. Many organizations are behind on Governance for AI and still need to set policies and practices for managing new risks. In early stages of maturity, organizations tend to invest in Data for AI before defining data requirements with AI use cases.
Using the framework, and guided by insights from our cases and survey, business leaders can learn how the five organizational dimensions need to evolve in the age of AI, and quickly assess their own progress in each dimension. Then, they can target the best next steps for impact.