Despite the relentless hype about and near-ubiquitous experimentation with artificial intelligence, the harsh reality is that most AI initiatives are not mature enough to scale. According to IT Pro, 93% of organizations today are actively using or building AI systems. Yet fewer than 10% have embedded any kind of robust governance framework or tooling into their development lifecycles. That chasm between experimentation and operationalization may explain why McKinsey asserts that only 1% of AI—including generative and agentic models—is currently in a “mature” state.
This discrepancy underscores a systemic issue: Enterprises may be building AI, but without governance, the programs are not getting into production at scale. They remain trapped in prototypes, proofs of concept and internal showcases—promising, but not transformative. As with a pachinko machine, for every 99 AI attempts, perhaps one reaches the intended outcome.
The myth of momentum vs. the reality of maturity
Recent research from EDB involving more than 2,050 global enterprises, each with more than 500 employees and spanning 13 countries, revealed a significant challenge: Over 50% of AI experiments never reach production and only 1% of AI projects currently deliver truly transformative results.
One might expect maturity to follow momentum, but the data suggests otherwise. Even with robust budgets and access to large language models (LLMs), most organizations haven’t cracked the code on “productionalizing” AI in a sustainable, compliant and value-generating way.
Meanwhile, ambitions remain sky-high. A full 95% of global enterprises surveyed said they aimed to become their own “Amazonesque” AI and data platforms within the next three years. Of these, 25% want to achieve this immediately and 75% within the year.
What’s holding AI back?
For most of these organizations, scale is not hindered by vision or even budget—it’s blocked by a lack of sovereignty.
Sovereignty in AI and data does not simply mean keeping data “on prem” or under national control. It means enterprises take full ownership of their data infrastructure, governance stack and security posture. It’s a shift from renting capability to architecting it deliberately, end to end.
After running more than 15,000 simulations analyzing the investment theses of these enterprises across 15 business domains and across their GenAI and agentic AI strategies, one powerful insight emerged from EDB’s research: Organizations that operationalized sovereignty over their AI and data saw 2x more AI initiatives being successfully launched and realized 5x the returns, measured in terms of economics, innovation and efficiency. We called these organizations the “Deeply Committed.”
Sovereignty as a strategic imperative
Sovereignty provided these organizations with tangible advantages:
- Anytime, anywhere secure access to data
- Regulatory and compliance alignment from day one
- Elimination of silos across the data value chain
- Custom control over the entire AI lifecycle, from ingestion to inference
Interestingly, 49% of those who saw transformative ROI had adopted hybrid data infrastructures to enable this sovereignty. Many (depending on region and industry) gravitated toward Postgres® as a foundational technology. This gave them the flexibility and control to unify legacy and new data across platforms without sacrificing performance or compliance.
“They’re not renting AI space, they’re building AI homes. These companies aren’t just layering AI on top of old systems. They’re rewiring the infrastructure for sovereignty, owning compliance, owning security and unifying their data so that it works with AI. That’s the only way to get past the 1% maturity ceiling.” – Kevin Dallas, CEO, EnterpriseDB (EDB)
Owning the blueprint, not just the tech
What sets successful enterprises apart isn’t just that they’ve adopted sovereignty. It’s how deliberately they’ve gone about doing so. They’ve moved away from indiscriminate investment and focused instead on blueprinting for success. That means aligning business goals with AI governance, ensuring traceability from data ingestion to output, and baking in compliance as an architectural feature, not an afterthought.
These organizations understand that AI success is less about doing more and far more about doing the right things in the right order. Governance isn’t a barrier—it’s the very foundation upon which scalable, compliant and high-value AI rests.
The path forward: From experimentation to production
For CIOs and CTOs, the path forward is clear but narrow. Throwing more projects at the wall and hoping something sticks is no longer viable. To meet the aspirations of becoming self-sufficient AI and data platforms, enterprises must:
- Embed governance and compliance into the AI lifecycle from day one.
- Adopt hybrid data strategies that balance agility with control.
- Ensure their teams have sovereignty over the full data-to-decision journey.
- Move from pilots to production with deliberate architectural choices.
Without sovereignty, AI remains a costly experiment. With it, AI becomes a core operating system for the enterprise, one that delivers real returns, fosters innovation and stands up to regulatory scrutiny.
The future isn’t just about building AI. It’s about governing it—and owning it.
