On June 6th, 2024, AI at Wharton partnered with Informatica to bring dozens of industry leaders and Chief Data Officers (CDOs) to Wharton’s campus for a collaborative brainstorming session with many of our leading AI academics. Their goal was to design a framework for companies and their CDOs to reference in order to responsibly and effectively leverage generative AI and increase value for their firms.
We are thrilled to now publish the result of these combined efforts through a new white paper: “Designing an AI-Ready Data Framework for CDOs to Maximize AI Value.”
This white paper highlights the critical need for securing executive buy-in through pilot projects that “generate economic value swiftly” and offers insights into fostering collaboration between business leaders and data solution developers. By linking data directly to business value and implementing robust governance structures, organizations can accelerate GenAI adoption and scale effectively.
Key strategies include fostering an AI governance committee, promoting cross-functional collaboration, and investing in change management to ensure alignment across the organization. The white paper also warns against falling into the hype cycle, urging CDOs to focus on targeted AI use cases rather than chasing trends. As Jason Beyer, Chief Data Officer at Fortive noted, “Get beyond the hype cycle by aligning with leaders on targeted uses of AI. By understanding the specific challenge you are trying to solve, you can avoid the assumption that AI is a hammer in your hand that will solve all the problems.”
The white paper also dives into the six major risks associated with Large Language Models (LLMs), including data security, intellectual property, and bias. It underscores the importance of a federated data governance model that balances agility with oversight, ensuring that data is used responsibly to drive business outcomes. For organizations looking to maximize the benefits of AI while mitigating its risks, this white paper offers a comprehensive guide to navigating the challenges and opportunities of an AI-driven future.
Read the full paper below.
