As AI reshapes the digital landscape, data centers are under increasing pressure to deliver unprecedented levels of power density, scalability, and sustainability. To meet these demands, businesses must align their infrastructure with evolving AI workloads and adopt a proactive asset management strategy that helps ensure resilience, accelerate time to market, and maximize return on investment in a future-ready digital economy.
AI at the core of a new technological era
We are entering a transformative phase in technological development, with AI at its epicenter. Its potential spans across industries, unlocking new efficiencies and innovations. However, this evolution demands a radical shift in the infrastructure that supports it—particularly data centers, which serve as the backbone of AI’s computational power.
With this promise comes a pressing challenge: power. As AI workloads grow more complex and energy-intensive, data centers must grapple with power densification. But how much power is needed? When will it be required? And where will it come from? These questions are critical, especially as many regions face power shortages that hinder data center expansion. In some cases, utilities are even negotiating “flexibility” clauses—limiting power availability during peak periods. So, it’s not just more power, it’s more optimized power.
Simultaneously, sustainability remains a non-negotiable priority. Organizations must decarbonize their operations (essentially in Scope 2) and reduce infrastructure impact (Scope 3) to remain resilient and profitable. Balancing AI’s power demands with environmental responsibility is no longer optional—it’s a strategic necessity.
This convergence of power, performance, and sustainability raises a pivotal question: are existing data centers equipped to meet the demands of an AI-driven future? The answer lies in understanding the diversity of AI workloads and tailoring infrastructure accordingly.
Tailoring infrastructure to AI workloads
AI is not monolithic. From GenAI training to real-time inference, each workload has various infrastructure needs, for example:
- GenAI Training: Requires ultra-dense, high-power greenfield data centers (e.g., 600kW per rack, 1GW+ site capacity). These are capital-intensive and today suited to hyperscales;
- AI Training (domain-specific): Can often be deployed in brownfield sites—if they support moderate-to-high rack densities and scalable power.
- AI Inference: Less power-intensive but demands high availability, large bandwidth and in some cases low latency, making it possible for retrofitting legacy or colocation spaces.
- GenAI Inference at Scale: Introduces challenges like massive bandwidth and edge integration, which can be addressed by repurposing edge-ready brownfield sites.
Understanding these nuances is key to making smart, future-proof infrastructure investments.
Reimagining existing assets: Brownfield sites as AI enablers
While greenfield builds are essential for GenAI, they come with high costs, long timelines, and significant environmental footprints. In contrast, brownfield sites—existing facilities—could offer a faster, more cost-effective path to AI-readiness when approached strategically. These sites represent not just physical infrastructure, but years of capital investment, operational knowledge, and embedded value that should not be discarded lightly.
Organizations struggle to have substantial resources into building and maintaining data centers infrastructure. These investments encompass not only physical assets like power, cooling, and networking, but also intangible assets such as location advantages, regulatory approvals, and operational expertise. By reimagining brownfield sites for AI, companies can preserve and extend the value of these investments, avoiding the sunk-cost trap and accelerating time-to-value. Rather than starting from nothing, a strategic retrofit or hybrid upgrade allows enterprises to:
- Maximize ROI on existing infrastructure by adapting it for modern workloads.
- Reduce capital expenditure by leveraging what’s already built and paid for.
- Minimize disruption to ongoing operations, especially in mission-critical environments.
- Accelerate deployment of AI capabilities by bypassing lengthy permitting and construction phases.
- Lower environmental impact by reducing the need for new construction and making efficient use of existing resources.
Three brownfield scenarios to watch
- Next-Phase Ready Sites: recently built data centers with unfinished growth phases can scale quickly with minimal disruption.
- End-of-Contract Legacy Sites: Older facilities can be retrofitted for mid-density AI workloads or hybrid cloud deployments.
- Power-Ready, Uncommitted Sites: Sites with secured power but no tenants can be modularly adapted for evolving AI needs.
In many cases, after assessing infrastructure readiness, brownfield sites may benefit from an hybrid approach—combining existing assets with new AI-specific modules. This strategy supports both traditional and AI workloads and can be considered a real competitive advantage while maximizing ROI as well as minimizing environmental impact.
Proactive asset management: The key to AI-ready infrastructure
To navigate this complexity, businesses must shift from reactive upgrades to a proactive asset management strategy. This approach enables:
- AI infrastructure planning and assessment – Evaluate current facilities to determine their suitability for supporting AI workloads;
- AI demand and forecasting – evaluate and anticipate AI workload requirements to guide infrastructure planning and investment;
- Infrastructure mapping – Identify which sites can support specific AI workloads based on power, cooling, and connectivity;
- Evaluate power needs providing better and/or more power sourcing with strategy, market analysis, real time sourcing, power purchase agreement negotiation;
- Provide potential “on site” alternative power solutions “bending the curve” of power demand including financial services;
- Scenario planning – Model different AI growth paths alongside their infrastructure implications, enabling targeted upgrades or hybrid deployments that align with both current capabilities and future demands;
- Investment optimization – Prioritize upgrades where return on investment is highest, ensuring efficient capital allocation;
- Risk mitigation – Anticipate and plan for regulatory, energy, and supply chain constraints to maintain operational continuity;
- Trusted vendor partnership – Collaborate with experts like Schneider Electric to evaluate, design, and implement AI-ready solutions.
By aligning infrastructure with AI’s diverse needs, organizations can accelerate time to market, optimize costs, and ensure long-term resilience in a rapidly evolving digital economy.
Time to Market: Time for environmental analysis, authorization, and permit, building construction and existing grid and network connections significantly increase the initial phase of the project. In additional to this, existing sites may have available space for additional power grid connection or on-site power generation. Modernization makes possible modular prefabricated mechanical and electrical infrastructure that will densify installation to enhance power and cooling capacity while minimizing risks. All reducing significantly time to build vs. “green fields.” Cost Optimization: Retrofitting brownfield sites for AI workloads can reduce facility capital expenditure by up to 30% compared to greenfield builds. Leveraging existing building construction and potentially some of the power and cooling infrastructure, while integrating modular AI-ready components, avoiding costs of new construction, and accelerating ROI.
Resilience and Operation excellence: To meet performance while optimizing operational cost and manage people skills, latest data center take benefit of the digitalization of operating and maintenance of infrastructure using preventive and predictive maintenance with embedded AI & analytics, leveraging virtual & augmented reality tools to de-risk and more centralized resources, finally prepare recycling of equipment for secondary market or “end of life” retrofitting critical row material.
Sustainability Gains: Modernizing existing facilities instead of building new ones can reduce embodied carbon emissions by up to 50% for equivalent power, supporting significant Scope 3 reduction goals while enhancing energy efficiency by 20 to 50% reducing equivalent Scope 2 emission and energy used.1
Building the bridge to an AI-driven future
As said, while AI reshapes the digital economy, the pressure on data centers to deliver unprecedented levels of power, scalability, and sustainability is intensifying. This transformation is not just about deploying more compute—it’s also about defining a strategy of infrastructure to align with the diverse and evolving demands of AI workloads with cloud services business, and combine existing data centers, new construction as well as refurbishing sites.
From ultra-dense GenAI training to inference applications at the edge, each AI use case requires a tailored AI-ready asset strategy. As AI is a race, while greenfield builds play a critical role, they may not be the only path forward. Brownfield data centers—rich in embedded value, location advantages, and operational maturity—offer potential opportunity.
Modernizing these existing assets is a strategic imperative for sustainability performance, time to market and can provide competitiveness advantages as well as data center lifecycle optimization. It reduces construction phase by bypassing lengthy construction timelines, cut embodied carbon emissions, increase efficiency, consider operation resources and associated risks, add agility, and can provide more business opportunity.
Future-proof your data center with proactive asset management for maximum ROI
Ready to future-proof your data center and boost operational efficiency? Discover how implementing a proactive asset management strategy can deliver rapid benefits and a measurable return on investment. Connect with Brice Martinot-Lagarde on LinkedIn or schedule your free 30-minute consultation today. Let’s work together to build a smarter, more resilient IT infrastructure that’s ready for tomorrow’s challenges.
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