Generative AI is rapidly becoming a core component of business operations, particularly in its integration with data governance and democratization. Informatica’s “Cloud Data Governance & Democratization Modernization Workshop” provided an in-depth look into these emerging trends and challenges, drawing from key insights backed by a comprehensive survey of 600 data leaders across the U.S., EU and APAC regions.

The Generative AI Rise: A Data Challenge

With more than 45% of companies already using generative AI in their business processes and 54% planning to adopt it soon, AI is reshaping how businesses approach data. However, the success of AI depends on the quality of the data being used, which has emerged as the top challenge. According to the survey, 42% of data leaders view data quality as the most significant roadblock in adopting generative AI and large language models. This issue is particularly pressing as organizations struggle with integrating the sheer volume of data required to train these models effectively.

Investments in Data Management

In response to the generative AI challenge, 78% of data leaders expect to increase their investments in data management, with 100% planning to strengthen their data capabilities. However, the complexity of managing data across multiple platforms is becoming a significantly more difficult. A striking 58% of respondents anticipate needing five or more tools to manage their data effectively, while nearly half report that their current tools are not fully cloud hosted.

The expansion of data sources is another critical challenge. Many data leaders are currently managing over a thousand data sources and this number is expected to increase in the coming year. The growing number of data sources highlights the need for a strong governance framework to ensure data quality, consistency and security.

Key Data Strategy Priorities for 2025

The workshop highlighted three main priorities for data leaders in 2025:

• Delivering reliable and consistent data for generative AI 
• Fostering a data-driven culture and improving data literacy 
• Enhancing governance over data 

These priorities reflect a broader market trend where AI adoption is not just about the technology itself but also about how organizations govern and prepare their data for AI’s transformative potential. AI readiness is becoming a top metric for measuring the effectiveness of data strategies, with 43% of data leaders citing it as a crucial performance indicator.

Generative AI: Challenges and Opportunities

The rise of generative AI has brought along several challenges, including data privacy and protection AI ethics and the need for more domain-specific data for training and fine-tuning LLMs. Despite these hurdles, data leaders remain optimistic. A significant 73% of those implementing AI expect faster insights and 60% are using AI to democratize access to data through self-service initiatives.

To overcome these challenges, data leaders are exploring new tactics such as prompt engineering with third-party LLMs (57%) and evaluating open-source LLMs (51%). However, even with these innovations, the effective governance and ethical use of AI remain critical concerns for data leaders globally.

Roadblocks to AI and Data Management Implementation

The survey revealed that 99% of these leaders struggle with issues that hinder their ability to effectively execute their plans for managing and using data. These roadblocks include the increasing volume and variety of data the growing demand from data consumers and the inability to scale data delivery effectively. Furthermore, organizational challenges such as a lack of support from business leadership (45%) and misalignment across business units (44%) are emerging as significant barriers to success.

Data Management as the Foundation for AI Success

As AI continues to reshape the business landscape, it becomes clear that successful AI implementation relies on robust data management practices. Key capabilities being invested in include:

• Data privacy and protection. 
• Data quality and observability.
• Data integration and engineering.

These investments, driven by the need for AI readiness, are part of a broader effort to improve governance, security and data literacy across organizations. However, the challenge remains in managing an ever-growing number of tools and platforms. Data leaders are increasingly recognizing the need for a more streamlined approach with 48% considering upskilling or reskilling their staff to manage AI and data governance more effectively.

Conclusion: AI and Data Governance—A Synergistic Relationship

The future of AI and data management is deeply intertwined. To fully realize the potential of generative AI, businesses must invest in robust data governance frameworks that ensure data quality, security, and ethical use. Informatica’s workshop underscored that AI is not just a tool but a driver of transformational change, offering businesses the opportunity to revolutionize their data strategies and achieve new levels of inovation With AI and data management going hand in hand, the message from data leaders is clear: the future of data governance is here and those who can navigate its complexities will lead the charge into a new era of data-driven decision-making.