Scale Data and Analytics to Support Your AI Journeys

Drive business outcomes, build capabilities and foster behavior change while navigating AI transformation.

Successful AI transformation through three journeys

As AI becomes increasingly integrated into business operations, the pressure is mounting to deliver meaningful outcomes while managing complexity and uncertainty. With 49% of organizations saying that demonstrating AI value is a top barrier to success, tensions are rising among many C-level executives over who has authority and who is responsible for various facets of AI transformation. 

The keynote at Gartner Data & Analytics Summit 2025 emphasized that chief data and analytics officers (CDAOs) must concentrate on consistently executing AI initiatives to maintain relevance and demonstrate the significance of their teams and functions.

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    CDAOs navigate high expectations for AI transformation

    In the face of CEO’s and CIO’s high expectations on AI, organizations should embark on three journeys to successfully navigate the AI transformation: achieving business outcomes, building data and analytics capabilities, and fostering behavioral change.

    No. 1: Establish trust in service of business outcomes

    To build confidence in both the data that feeds AI and AI itself, implement trust models that assess data trustworthiness based on value, lineage and risk. These models provide a framework for understanding the reliability of data and help set appropriate AI governance levels. Those organizations that overcome barriers to implementation achieve higher levels of risk mitigation and value creation. 

    No. 2: Create an adaptive ecosystem

    Developing a robust data and analytics ecosystem that supports AI initiatives requires a shift from traditional data foundations to dynamic, diverse and adaptable data ecosystems. When building or extending a data ecosystem, ensure that it is richly varied by using a range of tools and technologies to create the organization’s AI tech stack.

    Data readiness, rather than data quality, should be the primary focus. This means ensuring that data is AI ready and can be repurposed across multiple AI use cases.

    Additionally, active (as opposed to passive) metadata provides real-time insights into data and infuses trust into financial decisions around data, analytics and AI. This enables the organization to optimize data workflows, platform management and financial operations for not just performance and cost efficiency but also reliability and integrity. The end result is the evolution from a tech stack to a “trust stack” that fosters confidence among users and stakeholders.

    No. 3: Embrace new roles and skills to foster behavioral change

    The number-one roadblock to data and analytics success is a company culture that is not data driven. While training and education focused on data literacy and AI literacy is a good start, culture change isn't just about making people understand or trust the data — it’s about shifting behaviors. This involves creating roles dedicated to change management and fostering a continuous learning environment.

    Behavioral changes will be gradual, starting with small habits that turn into core components of an organization’s culture over time. Doing this across teams and disciplines is essential to harnessing the full potential of AI.

    Scaling data & analytics for AI FAQs

    Why is data readiness more important than data quality in AI initiatives?

    Data readiness focuses on the fitness of data for specific use cases, ensuring it is suitable for AI applications, rather than just being accurate or complete. This approach enhances efficiency and allows organizations to align data with their strategic goals.

    What is the value of active metadata in data ecosystems?

    Active metadata enhances data observability and optimizes data ecosystems for efficiency and innovation by providing real-time insights. When organizations transform passive metadata into actionable intelligence, they can improve data governance, streamline operations and drive more informed decision-making.

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