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Emerging Infrastructure Transformations in AI AdoptionEmerging Infrastructure Transformations in AI AdoptionEmerging Infrastructure Transformations in AI Adoption

Organizations must transform their data ecosystems through four critical infrastructure upgrades to fully realize AI's competitive advantages.

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Industry Perspectives

August 27, 2025

5 Min Read

AI in gears

Alamy

By Hardik Chawla

Artificial intelligence is only as powerful as the data infrastructure that supports it. To successfully adopt and scale AI, organizations must take a strategic, step-by-step approach to modernizing and optimizing data ecosystems. Although challenges are inevitable, businesses can minimize disruption, control costs, and create sustainable competitive advantages by implementing one or more infrastructure transformations.

Four Essential Infrastructure Transformations

To unlock the full potential of AI , organizations can consider four critical infrastructure transformations: scaling storage and computing resources to handle AI workloads, implementing appropriate data governance for machine learning (ML), redesigning data pipelines to support AI processing needs, and managing the complex transition from legacy systems.

1. Scaling storage and compute

AI requires a significant amount of memory and computing power, and performance metrics, such as throughput, latency, and resiliency, play a much larger role. Balancing scalable solutions that decouple storage from compute is vital. Modern AI workloads introduce performance variability across hardware and require a mix of high-speed (hot) and archival (cold) storage, along with cluster-based compute architectures.

Recent ISC High Performance conference presenters addressed energy-capped performance issues, storage retrieval bottlenecks, and data workflow scheduling problems. Elastic demands and uneven wear of on-demand compute resources for AI and ML operations are a primary factor for IT to consider. Balanced scaling of infrastructure storage and compute clusters optimizes resource use in the face of emerging elastic use cases.

Throughput, latency, scalability, and resiliency are key metrics for measuring storage performance. Scaling storage with demand for AI solutions without contributing to technical debt is a careful balance to contemplate for infrastructure transformations. Scaling compute, such as graphics processing units (GPUs) or tensor processing units (TPUs), without balancing input/output (I/O) and storage can lead to bottlenecks in hardware utilization.

Upfront investment in high-throughput systems, like tiered storage solutions and high-bandwidth interconnects, can help future-proof systems. Careful estimations of proposed business processes' requirements for future architecture needs can be considered as a tripartite problem set of storage, compute, and energy demands .

2. Implementing data governance for ML

Data governance in AI extends beyond traditional access control. ML workflows have additional governance tasks such as lineage tracking, role-based permissions for model modification, and policy enforcement over how data is labeled, versioned, and reused. This includes dataset documentation, drift tracking, and LLM-specific controls over prompt inputs and generated outputs.

Governance frameworks that support continuous learning cycles are more valuable: Every inference and user correction can become training data. Systems that log, audit, and review how these feedback loops affect downstream behavior stand to benefit the most. Without structured oversight, model outputs risk reinforcing bias or violating compliance norms. Through metadata schemas, compliance monitors, and ML-aware policy engines, IT infrastructure becomes an opportunity to embed governance at the workflow layer.

3. Redesigning data pipelines for AI processing

AI pipelines demand combining data ingestion, transformation, model inference, and feedback loops into a seamless workflow. As models become more stateful and retain context over time, pipelines must support real-time, memory-intensive operations. Even Apache Spark documentation hints at future support for stateful algorithms (models that maintain internal memory of past interactions), reflecting a broader industry trend. AI workflows are moving toward stateful "agent" models that can handle ongoing, contextual tasks rather than stateless, single-pass processing. Holding one or more training models in memory to perform ingestion, processing, and output tasks allows for monitoring and continuous training deployment models.

Stateful model tasks are highly storage- and compute-intensive and create data-intensive workflows. To delegate some of the complexity, MLOps addresses the challenges of model degradation over time through continuous training and deployment of models, drawing on DevOps principles and integrating data pipelining tasks to coordinate modeling efforts. Designing governance and management practices around data pipelining enables AI processing to take advantage of advancements in analytical tools, which can leverage stateful or stateless analytics as needed. This approach is the elastic allocation model of resources, which can create uneven resource demand and component turnover for IT teams to monitor.

4. Legacy transition

A successful legacy transition focuses on modular upgrades and incremental improvements that reduce risk while enabling AI-driven capabilities to operate alongside existing legacy systems.

Containerizing model training and inference environments enables parallel operation and rollback insulation, mitigating risk while sandboxing deployment for departments to train with and adapt to new workflows.

Incremental Adoption as Risk Mitigation

Transforming IT infrastructures for AI workflows and pipelines can be a high-risk undertaking. Gradual refactoring enables targeted infrastructure changes that deliver meaningful, high-impact performance increases to selective business processes. This approach helps companies evaluate marketplace developments and maintain a competitive advantage while minimizing capital expenditures. Managing for future-proofed architectures and elastic scalability while maintaining a targeted focus on resource-intensive processes that deliver the greatest returns can be challenging to define. The process, however, will contribute to more transparent data governance and a deeper understanding of business processes.

The adoption of generative AI (GenAI) and hybrid AI continues to expand among companies with over 500ドル million in revenue . Data infrastructure and C-suite strategy remain primary hurdles and accelerators for realizing AI adoption. Mobilizing for shifts in operations to continuous integration and continuous training requires elastic search and high-capacity storage to realize the strategic advantage that AI technologies promise. Robust data governance programs and worker upskilling are critical management initiatives, but without a data infrastructure that supports the ongoing use of AI technologies in business functions, the best strategy will fail to scale.

The Future of Infrastructure Transformation

Enterprises that scale AI successfully do so with executive sponsorship, established governance, and balanced initiatives that consider targeted outcomes. Continuous improvement, development, and training offer operational solutions amid mounting regulatory and market pressures for AI feature adoptions in product delivery. It's imperative for leaders to align technical modernization with model governance, risk management, and workforce readiness. More than a compute problem, AI adoption is an architecture, governance, and resiliency challenge, and the solutions depend on infrastructure.

About the author

Hardik Chawla is a senior product manager with nearly eight years of experience in digital product management. He is responsible for supply chain optimization and technology, driving development of B2B platforms, API-first architecture, and AI/ML-driven products. He holds a bachelor's degree in electrical engineering and an MBA in technology management and strategy from the UCLA Anderson School of Management. Connect with Hardik on LinkedIn .