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  "documentTitle": "The art of AI maturity Advancing from practice to performance North America",
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      "text": "1. Senior Sponsorship: Organizations have an AI strategy that is developed by the Chief Analytics Officer, Chief Data Officer, Chief Digital Officer or an equivalent. The CEO and the Board actively sponsor and share accountability for the strategy and associated AI initiatives.\n2. AI Strategy: Organizations not only have a core AI strategy aligned to the overall business strategy, but they also dedicate tools and tactics to execute it and continuously track their performance against that strategy.\n3. Proactive vs. Reactive: Organizations have the resources (such as technology, talent and patents) to proactively define and demonstrate how AI can create value vs. apply AI as a reaction to a need. They're first-movers instead of fast followers in terms of applying AI for business value.\n4. Readily Available AI and ML tools: Organizations work with an ecosystem of technology partners to access machine learning models and tools to help innovate new products and services.\n5. Readily Available Developer Networks: Organizations tap into an ecosystem of technology partners to access developer networks that support the development of new products and services.\n6. Build vs. Buy: Organizations develop custom-built AI applications or work with a partner who offers solutions-as-a-service, vs. purchase “off-the-shelf” AI solutions with little-to-no customization.\n7. Platform and Technology: Organizations apply the necessary cloud, data and AI infrastructure, software, self-serve capabilities and industry best practices, and they adopt the latest tools available from platform and technology partners.\n8. Experimentation Data—Change: Organizations improved their use of experimentation data between 2018 and 2021, effectively translating into a higher data and AI maturity. Experimentation data is the use of internal and external data to design new models and generate new insights. To do that, organizations use enterprise-grade cloud platforms to keep data clean and trustworthy, and to support decision making at greater speed and scale.\n9. Data Management and Governance: Organizations scale their data management and governance practices to increase data quality, trust and ethics across entities—e.g. by implementing master data management and ensuring security, compliance and interoperability.\n10. Data Management and Governance—Change: Organizations improved their data management and governance practices between 2018 and 2021, effectively translating into a higher data and AI maturity.",
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