Is Data And AI Sovereignty The Defining Debate Of 2025?

The rapid pace of innovation in artificial intelligence (AI) and data technologies has captured global attention and created a buzz around their expansive potential, driving innovation across industries—from healthcare to commerce and education. As organizations explore the potential of these technologies, a more pressing question is surfacing: who controls your data, and how?

This debate is reshaping boardroom conversations as organizations realize that data and AI sovereignty—the ability to maintain control, compliance, and consistency over critical digital assets—is becoming essential in an AI-driven world. Companies that prioritize sovereignty will not only protect their most valuable resources but also unlock new opportunities for growth and innovation.

The Evolution of Data and AI in Motion

McKinsey reports that AI-driven personalization models have the potential to boost profitability by 5% to 25%. This is a remarkable opportunity, especially when compared to the 5% average decline in profitability experienced by Fortune 500 companies in 2023. The gap only shows AI's power in creating personalized customer experiences that drive growth and efficiency.

AI's impact extends far beyond profit-driven enterprises. In education, platforms like DreamBox Learning and Riid leverage AI assistants to adapt to individual learning styles, making complex subjects like calculus accessible—something traditional classrooms often struggle to achieve. These innovations reveal a future where AI-powered co-pilots augment human capabilities and drive success across industries.

At the same time, IDC reports that 90% of enterprise data remains unstructured, leaving its full potential unrealized. AI offers a pathway to unlock this untapped value, transforming chaotic data into actionable insights that power decision-making and innovation.

"AI has the potential to drive GDP growth at levels we haven't seen in years," says Hervé Timsit, Chief Revenue Officer of EDB, a leader in Postgres data and AI. "Projections point to an annual increase of 1.5 percentage points fueled by AI alone. To put that into perspective, the first quarter GDP growth rate this year was just 0.7% annualized."

Spotting Patterns in the Modern Data and AI Landscape

Take this concept one step further into the enterprise world. In your organization, what hidden data assets—and value— could be unlocked with AI? Whether it's in medical imaging or front-line service and support, these new generative AI and machine learning systems are changing the way we work, think, and envision the future of our organizations.

"The processes, data, and generative AI models you create within your hybrid infrastructure are no longer just operational necessities; they're strategic assets," Timsit explains. "Over the past year, we've seen a seismic shift in how leaders perceive the value of these resources. Data and AI now sit at the center of competitive strategy."

Three patterns have emerged that are reshaping the enterprise landscape. These insights are based on conversations EDB conducted with hundreds of enterprise executives across the United States, Germany, and the United Kingdom in 2024:

First, the majority of enterprise data workloads are now hybrid, spanning on-premises systems and multiple clouds—according to the 2024 research conducted by EDB, 56-62% of critical workloads occur in a hybrid manner. This reflects a dual need: to maintain regulatory compliance and industry security standards while maintaining data agility.

Second, AI is no longer experimental—it's deeply embedded in enterprise operations. From personalization to product innovation, AI is already driving measurable returns. The same EDB research revealed that 51% of business leaders say it's "full steam ahead" for AI experimentation with data modeling, and 63% view AI-enabled data workloads as critical for tackling the complexity of the future.

Finally, leaders are increasingly focused on AI's ability to empower their workforce, streamline decision-making, and elevate customer experiences. These transformations hinge on an organization's ability to maintain control over its data and AI systems.

Balancing Data Control and Agility

While AI-driven transformation offers immense potential, many organizations struggle to maintain full control over their data and AI systems in hybrid environments that span on-premises and cloud platforms. To navigate this challenge, businesses can adopt key strategies to balance control and agility:

• Prioritize Observability and Governance: Ensuring end-to-end visibility across hybrid data environments allows businesses to maintain oversight of performance, security, and compliance. Tools and frameworks that enable observability help organizations anticipate issues and optimize systems without sacrificing innovation.

• Adopt Hybrid Cloud Architectures: A hybrid approach offers the best of both worlds: the scalability of cloud environments for innovation and the control of on-premises systems for sensitive workloads. Companies that structure their infrastructure to align with their unique regulatory and operational requirements will be better positioned to balance these priorities.

• Invest in AI-Driven Automation: Modern AI-driven technologies and database services can enhance operational agility by automating critical tasks—like scaling workloads, managing performance, or maintaining compliance. Automation ensures that businesses can innovate at speed without increasing administrative complexity.

Timsit explains: "Organizations that adopt strategies built on agility and control are not only more resilient but also more competitive. They can confidently scale AI-driven initiatives, knowing their critical assets remain secure, visible, and compliant."

What's Next

Data and AI will continue to reshape the global economy. The organizations that harness these technologies—while maintaining control over their critical assets—will be the ones that thrive in the years ahead.