Artificial intelligence is projected to contribute $19.9 trillion to the global economy by 2030, unlocking unprecedented innovation and opportunities for business growth. But this promise comes with a paradox: AI brings a growing, unsustainable demand for data, power, and budget.

For years, enterprises have considered power and energy a necessary means to an end, a line item that simply grows with the company’s footprint. The reality is that electricity, at 46%, is already the largest operational expense for enterprise data centers. Now, the era of data and AI is a force multiplier. Data center energy consumption for AI workloads is forecasted to grow at a staggering 45% CAGR, pushing even the largest tech companies to their limits.

But the future won’t be won on power bills. The winners will be those who focus on efficiency, delivering AI applications on an architecture that optimizes cost, energy, and performance at scale, without sacrificing compliance, resilience, or speed. It’s not just about migrating workloads from inefficient legacy systems into the cloud, a strategy that has proven to be too expensive and restrictive in its own right. Instead, it’s about building a sustainable, sovereign data and AI platform, one that unifies workloads, keeps data under your control, and makes it AI-ready from the start.

Three Common Traps Driving Up Power and Costs in the AI Era

“Enterprises aren’t intentionally inefficient. What they’re up against is phantom energy—the hidden power drain that most enterprises overlook,” said Lizzy Nguyen, Director of Product Marketing at EDB. “It’s the electricity your devices use even when you think they’re off. The same thing happens in enterprise IT. Idle servers quietly waste 30% to 40% of their power, and, globally, a third of servers aren’t even fully utilized.”

The waste is often a by-product of outdated architectures, fear-driven overprovisioning, legacy drains, and the complexity of evolving IT landscapes. Here are three common scenarios in which this inefficiency is amplified by AI:

1. Meeting new AI demands

Building the next generation of generative and agentic AI applications comes at a cost, especially for more advanced AI use cases that require resource-hungry GPUs. Data centers are expanding and becoming unsustainable as enterprises layer new AI capacity on top of years of accumulated infrastructure. The result is a patchwork of siloed IT decisions, fragmented cloud deployments, and rushed GPU clusters that are costly to power, difficult to cool, and nearly impossible to manage efficiently. Each new addition adds complexity, driving up idle consumption, duplicating storage, and straining budgets under the weight of rising energy demands. The real challenge is achieving “AI-readiness” without spiraling compute and power costs.

2. Overprovisioning and the fear of failure

To protect business-critical applications and maintain continuity, many enterprises overprovision their databases by default. This common practice of “scaling by credit card” was born of a fear of a poor customer experience, but the cost is substantial. Simply throwing more resources at the problem, whether on-premises or in the cloud, is unsustainable. These organizations are running at 30% server capacity while paying for 100% of the resources. AI’s unpredictable demand spikes only intensify the waste, as enterprises double down on this costly “security blanket.”

3. The “lift and shift” trap

Many enterprises attempted to modernize by simply moving their monolithic apps to the cloud. While this “lift and shift” approach may deliver short-term benefits, it leaves enterprises unprepared for modern AI applications, which are increasingly microservices-driven and demand flexible, distributed systems with seamless lakehouse integration. Without rethinking and optimizing their architectures, enterprises not only slow down AI adoption but also lock themselves into higher energy costs.

Sovereign Data and AI: Bringing Efficiency, ROI, and Performance Together

It’s no wonder that 83% of enterprises globally now rank power efficiency among the top three drivers for rethinking data center architectures in the age of agentic AI, according to EDB research (May 2025). The leading 13% of AI-driven enterprises are twice as likely to design for energy-efficient model execution that reduces complexity and costs, which has allowed them to deliver 2x more mainstream agentic AI and GenAI applications, while reaping 5x the economic advantages. They’re proving it’s possible to optimize energy use without sacrificing performance, agility, or scale.

“Enterprises today face a stark choice: Either endlessly increase their energy supply or find ways to do more with less. Efficiency is the only path forward,” said Nguyen. “Data and AI sovereignty means not just security and governance but complete control over the architecture itself––from infrastructure and data pipelines to AI orchestration. That level of control accelerates AI delivery, drives down costs, and unlocks energy efficiency by design.”

Building a Future-Proof AI Foundation with EDB Postgres^® AI

By integrating with your existing data ecosystem, EDB Postgres AI (EDB PG AI) extends the value of your infrastructure and transforms your core operational data into an AI-ready asset–all while ensuring data and AI sovereignty. It empowers teams to quickly develop GenAI applications, optimize IT and resources, minimize idle energy usage, and reduce overall total cost of ownership (TCO) by 51%.

• Responsible AI readiness: EDB Postgres^® AI Factory accelerates the deployment of GenAI applications and AI agents by 3x. With an easy-to-use application builder and automated AI orchestration, EDB PG AI removes up to 80% of the integration work, all in one secure, sovereign platform. It also eliminates dependence on fragmented cloud services and disparate third-party AI components, enabling a faster, more secure path to sovereign AI.
• Fine-tuning and intelligent optimizations: EDB PG AI delivers intelligent recommendations to proactively optimize system configurations. This directly reduces the raw compute needed to support critical workloads, freeing up resources and immediately lowering energy consumption, all while seeing up to 8x faster performance.
• Unified, modern architecture: EDB PG AI unifies core operational, analytical, and AI workloads; simplifies migrations and refactoring; and delivers a distributed architecture to optimize the entire IT landscape by reducing compute requirements. This shift not only unlocks a modern, microservices-driven approach for AI applications but can also reduce data center emissions by up to 87%.

“As the industry’s first Postgres-based sovereign data and AI platform, EDB Postgres AI is meticulously engineered to optimize for energy efficiency at every layer, from foundational operational workloads to the most demanding next-generation AI tasks. The platform doesn't just enable AI, it powers it responsibly and efficiently,” said Nguyen. “On average, we’ve seen up to 20%–40% cost reductions per workload, and up to 50% lower emissions.”

Taking the Next Steps

Don’t let the paradox slow down your path to value with AI applications. By becoming your own sovereign data and AI platform, you don’t have to choose between innovation, cost savings, and energy efficiency. EDB PG AI offers a simple path to win in the data and AI era.

Try the EDB Postgres^® AI Efficiency Calculator to estimate how much you can optimize energy efficiency, cost, and performance for your business. Download an independent study detailing how three massive financial institutions achieved energy consumption reductions of up to 81% and emissions reductions as high as 87% with EDB PG AI.