Data Management for AI Success

As artificial intelligence (AI) continues to advance, leading enterprises are recognizing that effective data management is critical to unlocking AI’s full potential. In addition, Gartner insights has identified building a common data platform, including data governance and data management, as one of the four proven priorities for CDAO success. Despite this, many data and analytics leaders are still grappling with how to ensure proper data management across all their applications in order to implement more sophisticated AI solutions.

For the second consecutive year, Gartner CDAO Community members have identified AI as their top priority. To address ongoing challenges, data and analytics leaders from the Southern California CDAO community recently convened for a town hall to share strategies for overcoming common obstacles, maximizing the ROI of AI initiatives, and ensuring that AI applications are grounded in high-quality enterprise data. They also explored the implementation of agentic AI solutions to automate data processing, enhance employee productivity, and support more informed decision-making.

The program was moderated by Ami Dave, Senior Principal, Data Analytics Solution Value Practice at Google Cloud. Community members Yiqun (Helen) Li, Executive Director of Data & Analytics in BR&D, Digital Transformation for Eli Lilly and Company; Philip Kim, Chief Data & Analytics Officer for Public Storage; Jesse Mauser, Chief Data Officer for Risk Placement Services; and Arun Yarlagadda, CDO, Data Platform for American Honda Motor Company guided small groups as discussion leaders.
 

Highlights from the discussion

1. Data governance remains the foundation for AI success

Despite the excitement around generative AI and agent-driven innovation, data governance is still the “word of the day.” Ensuring the right data stewards are in place and maintaining data quality and observability are seen as critical, especially in highly regulated industries like healthcare and financial services. As one data leader shared, “You can have innovation and get more intelligent about your data – but data governance is at the core.”

1. GenAI and Agentic AI – Early days, high aspirations

Organizations are in the nascent stages of deploying generative AI and agentic AI. For many CDAOs, the “results are starting to come in” for use cases to improve productivity and enable real-time decision making. However, agent-driven use cases, driven by agentic support, are still in the proof-of-concept phases and are evolving toward a “north star” for AI efforts.

1. Data literacy and people engagement are ongoing challenges

Data literacy remains a pain point for many data leaders, and training, engagement, and resource allocation were repeatedly highlighted. One CDAO said, “The people-side of the equation, such as literacy and resources, are important. You have to keep them engaged.” They also discussed a need to train people in new approaches, such as reinforcement learning, as teams “learn by doing.”

1. Balancing hype, uncertainty, and practical progress

There is significant uncertainty about how quickly the environment is moving and how to separate hype from reality. While horizontal GenAI use cases like chatbots are showing success, vertical, function-based applications remain more challenging. Participants stressed the importance of prototyping, practicing safety thresholds, and logging interactions for traceability and feedback, as organizations navigate the evolving landscape of AI capabilities.

CDAOs can continue this discussion with peers at an upcoming in-person community gathering. Existing community members can sign in to find events and register with one click. If you are new to Gartner C-level Communities, apply to join your local CDAO community to connect with peers on critical priorities.
 

By CDAOs, For CDAOs™

Join the conversation with peers in your local CDAO community.