Every millisecond matters in today’s AI-driven world. A lag in fraud detection can mean millions in lost revenue. A delay in customer engagement could be a missed opportunity. A split-second slowdown in an autonomous vehicle could put lives at risk. These scenarios aren’t hypothetical. They’re everyday challenges that put unprecedented demands on enterprise infrastructure.

As organizations scale AI beyond experimentation, they’re rethinking their cloud investments. Repatriation—the strategic shift of workloads from public cloud back to on-prem or private environments—has gained traction as enterprises seek better performance, cost optimization, and stronger security. But this isn’t about abandoning the cloud. It’s about deploying AI workloads strategically to maximize efficiency and control.

Full repatriation is rare: Only about 9% of enterprises plan to move entire workloads off the cloud. Most are selectively repatriating key elements, such as high-value data, compute-intensive processes, or sensitive workloads.

The real goal? Building infrastructure that is dynamic, distributed, and optimized for today’s AI workloads, as well as the future. 

Optimizing Infrastructure for Training, Inference, and Innovation

As stated in the recent Forrester report, The Rise of the AI Cloud, “AI workloads vary significantly and as such, so do their infrastructure requirements.”

AI-driven organizations are rethinking their infrastructure strategies to support the next wave of AI—ensuring models, applications, and autonomous agents operate closer to where the data lives, across all locations and regions. 

This is especially evident in the distinction between training and inference. The high compute demands of AI model training dictate where it occurs, often in the cloud or specialized AI facilities that provide access to high-performance GPUs. Meanwhile, organizations handling sensitive data may opt for on-prem training to maintain tighter control over security and compliance.

But training is just the beginning. Once a model is trained, it must shift from learning to acting—processing live data, generating real-time insights, and scaling on demand. Unlike training, which can occur anywhere, inference must happen everywhere, ensuring AI is responsive to real-time needs. To achieve this, enterprises must push AI beyond centralized cloud infrastructure and move it closer to the data source onto the edge where it can flag fraud, guide autonomous vehicles, or personalize customer interactions instantly.

And now, the emergence of agentic AI is pushing infrastructure demands even further. As AI moves beyond training and passive analysis to autonomous decision-making and orchestration, engineering teams are under pressure to design high-performance, distributed computing environments to keep pace with AI’s growing complexity.

AI-first global clouds have emerged to meet this need, allowing organizations to compose an adaptable infrastructure and move inference closer to where data is created while seamlessly integrating with on-premises and private cloud environments. This approach reduces bandwidth consumption, lowers data transfer costs, strengthens privacy and security, and optimizes AI performance.

Four Best Practices of AI-First Enterprises 

AI innovators are rewriting the playbook, moving beyond rigid hyperscaler dependence or expensive on-prem infrastructure. Instead, they’re building distributed, open, and flexible multicloud AI-first environments designed to evolve alongside their business. What does that look like in practice?

1. Silicon Diversity: The rapid growth of AI workloads has exposed a critical challenge—traditional compute architectures struggle to efficiently support inference at scale. AI-first enterprises are overcoming this by leveraging a mix of GPUs, CPUs, and specialized AI chips optimized for different stages of the AI lifecycle. As Gartner notes, the scale of infrastructure needed for inference varies dramatically, from hyperscalers deploying trillion-weight models to enterprises running smaller, more targeted models. By embracing silicon diversity, businesses can allocate the right compute resources for each specific use case, reducing costs and eliminating bottlenecks.

2. Serverless Inference: As AI innovation accelerates, enterprises face increasing challenges in procuring, maintaining, and upgrading compute resources. Serverless inference provides a seamless solution, enabling businesses to run AI workloads on cloud-based compute that scales automatically. This approach eliminates infrastructure overhead, capital expenses, and the risk of rapid hardware obsolescence. Instead of managing infrastructure, enterprises can focus on AI innovation, knowing that the underlying compute will scale dynamically to meet demand.

3. Real-Time Data Integration: AI models are only as good as the data they access. To ensure real-time, relevant insights, companies are integrating retrieval-augmented generation (RAG), vector databases, and streaming analytics platforms like Apache Kafka. This combination allows enterprises to securely manage both proprietary and public data, maintain data sovereignty, and deliver ultra-low-latency AI responses. By structuring AI environments around real-time data pipelines, businesses can optimize contextual accuracy, reduce continuous model retraining costs, and enhance the responsiveness of AI-driven applications.

4. Open, Composable Infrastructure: Forrester predicts that the cloud will evolve into an abstracted, intelligent, and composable environment. AI-first enterprises are embracing this shift, treating infrastructure as an open, modular ecosystem spanning cloud, edge, and on-prem resources. This composability ensures agility, allowing organizations to implement the right technologies for the job without vendor lock-in. By maintaining this level of openness, enterprises can accelerate time-to-value and future-proof AI investments for long-term growth.

By adopting these practices, enterprises can build adaptive, scalable, and cost-efficient AI-first ecosystems.

AI Infrastructure for the Future: A Workload-First Mindset

The new mantra is clear: “For every AI workload, the right hosting environment.” 

Multicloud isn’t just about avoiding vendor lock-in (though that is certainly a perk). It’s about taking control of your AI strategy and making your infrastructure work for you, as opposed to forcing things to work within the confines of your infrastructure. 

AI is constantly evolving—so why shouldn’t your infrastructure? Modern workloads demand flexibility, scalability, and performance. Instead of rigidly relying on hyperscalers or fully repatriating to on-prem, successful enterprises prioritize agility and continuously optimize their environments to keep pace with AI innovation. This means leveraging hyperscalers for general workloads, AI-first clouds for inference and automation, and on-prem or edge computing for latency-sensitive, compliance-driven tasks—while ensuring infrastructure remains open, composable, and ready for the future.