Key Capabilities Strategy and Sponsorship as a reaction to a need. They’re first- with a partner who offers solutions- keep data clean and trustworthy, and movers instead of fast followers in as-a-service, vs. purchase “off-the- to support decision making at greater 1. Senior Sponsorship: Organizations terms of applying AI for business value. shelf” AI solutions with little-to-no speed and scale. have an AI strategy that is developed customization. by the Chief Analytics Officer, Chief 4. Readily Available AI and ML tools: 9. Data Management and Governance: Data Officer, Chief Digital Officer Organizations work with an ecosystem 7. Platform and Technology: Organizations scale their data or an equivalent. The CEO and the of technology partners to access Organizations apply the necessary management and governance Board actively sponsor and share machine learning models and tools cloud, data and AI infrastructure, practices to increase data quality, accountability for the strategy and to help innovate new products software, self-serve capabilities and trust and ethics across entities associated AI initiatives. and services. industry best practices, and they adopt —e.g. by implementing master data the latest tools available from platform management and ensuring security, 2. AI Strategy: Organizations not only 5. Readily Available Developer and technology partners. compliance and interoperability. have a core AI strategy aligned to the Networks: Organizations tap into an overall business strategy, but they ecosystem of technology partners 8. Experimentation Data—Change: 10. Data Management and Governance— also dedicate tools and tactics to to access developer networks that Organizations improved their use Change: Organizations improved their execute it and continuously track their support the development of new of experimentation data between data management and governance performance against that strategy. products and services. 2018 and 2021, effectively translating practices between 2018 and 2021, into a higher data and AI maturity. effectively translating into a higher data 3. Proactive vs. Reactive: Organizations Experimentation data is the use of and AI maturity. have the resources (such as Data and AI Core internal and external data to design technology, talent and patents) to new models and generate new proactively define and demonstrate 6. Build vs. Buy: Organizations develop insights. To do that, organizations use how AI can create value vs. apply AI custom-built AI applications or work enterprise-grade cloud platforms to The art of AI maturity—Advancing from practice to performance 37