Artificial Intelligence has quickly emerged as a transformative force, redefining how enterprises operate, innovate, and compete. From personalised customer experiences to accelerated drug discovery, the potential seems boundless. But beneath the headlines and hype lies a growing paradox.
Despite a surge in AI investment, many initiatives underperform or stall entirely. Cloud computing is expected to flourish in 2025, with AI serving as a key driving force. Hybrid cloud deployments are gaining momentum. Gartner predicts that by 2027, 90% of organisations will have adopted hybrid cloud strategies. At the same time, the disconnect between public cloud promises and enterprise realities has become increasingly difficult to ignore. At the heart of this disparity is an overlooked but critical factor: infrastructure readiness.
For enterprise leaders, this is a moment of inflection. The AI arms race demands more than ambition—it requires infrastructure strategies as bold and adaptive as the AI innovations they aim to support.
The infrastructure gap behind the AI boom
AI has been identified as a primary driver of cloud adoption. Organisations now seek scalable, on-demand compute to power model training and inferencing. Yet, AI outcomes have lagged expectations. Gartner reports that a high proportion of AI projects never make it into production, let alone deliver sustained value.
So where does it go wrong? One of the key issues is that although cloud infrastructures do offer scalability (and in some cases also performance requirements for AI), they fail to deliver on the specific locality and cost requirements demanded by customers. The result: misdirected or underutilised investments, ballooning costs, and frustrated teams.
Misaligned expectations and capabilities
AI’s complexity is often underestimated. Vendors tout soaring demand, but revenue growth frequently trails projections – highlighting a chasm between interest and implementation.
Meanwhile, enterprise teams grapple with limited in-house expertise, architectural challenges, and legacy infrastructure unfit for AI’s dynamic workloads. The gap is not just technical, but organisational.
In this uncertain terrain, leadership becomes paramount. Traditional IT decision-making – incremental and risk-averse – is ill-suited for AI’s breakneck pace. Infrastructure choices made today will determine the innovation capacity of tomorrow. Enterprises must act with clarity and courage.
A paradigm shift in storage requirements
Unlike conventional workloads, AI generates and consumes data at a scale and velocity that defies traditional data storage paradigms. These are not just larger datasets—they are more complex, variable, and distributed. For instance, new and broader AI inferencing use-cases will further push the need for high data capacities that do not fit into the available GPU memory, requiring new, efficient access methods for data storage in order to avoid inefficiencies.
Latency sensitivity, unpredictable access patterns, and global data distribution push legacy systems beyond their design limits. Traditional linear-scaling storage—scaling capacity and performance without considering metadata, or concurrency—becomes a bottleneck.
The limits of hoarding
Data hoarding – storing everything “just in case” – is no longer viable. AI demands real-time ingestion, continuous learning from historical data, and instant access across teams.
Legacy architectures create siloed storage pools that restrict data visibility and reuse, resulting in fragmented workflows and inefficiencies. They often suffer from performance bottlenecks that slow down AI training and inference processes, and impose significant operational overhead, consuming resources and reducing overall agility.
The case for multidimensional scaling (MDS)
What Is MDS?
Multidimensional Scaling (MDS) redefines how we think about storage scalability. It’s no longer sufficient to scale capacity and performance alone.
MDS architectures are built to scale across multiple dimensions, including applications, storage compute, S3 objects, S3 buckets, metadata, objects per second, throughput, and systems management. This multidimensional scalability ensures that systems maintain high performance and flexibility, even when handling unpredictable AI workloads.
Within the second part of the AI pipeline, AI workflows are inherently multi-stage – spanning training, fine-tuning, validation, deployment, and ongoing inference. Each stage presents distinct requirements. For example, in the case of service providers who offer AI/GPU-as-a-service to multiple enterprises, multi-tenancy may naturally be required.
MDS allows each workload to scale independently, without over provisioning or compromising performance. Just as importantly, MDS supports disaggregated architectures, allowing compute and storage to evolve separately—crucial for long-term agility and cost control.
Why object storage is built for AI
Many analysts strongly suggest that object storage is the ideal choice for AI, as it can handle large volumes of both structured and unstructured data in almost any format. Unlike traditional file or block storage, object storage is:
- Natively scalable to exabyte levels
- Ideal for unstructured data such as images, videos, text, sensor data, and model artifacts
- Inherently cloud-native, integrating seamlessly with containerized and serverless environments
Key benefits
Object storage offers essential features for AI infrastructure, providing a flat namespace that enables simple, scalable data organization without capacity limits. Its API-based access integrates seamlessly with machine learning frameworks and DevOps tools, making it ideal for cloud-native, stateless applications. Additionally, object storage supports open standards and S3-compatible interfaces, promoting resilience and avoiding vendor lock-in.
The MDS Connection
While object storage delivers the flexibility and scalability AI applications demand, true scalability must address the full spectrum of AI workflows, especially in later pipeline stages. Not all vendors can meet this multidimensional challenge. Currently, only one solution on the market offers scalable performance across ten distinct dimensions. Multidimensional scaling enables horizontal growth—adding storage or nodes—as needed to optimize for varying workload requirements such as cost, performance, and durability. This approach ensures global scale through data replication, geographic redundancy, and performance optimization, all while eliminating the need to re-architect systems. Designed for adaptability, object storage can dynamically scale resources in response to changing demands.
MDS benefits for IT teams
MDS transforms operations with an automation-first design, reducing manual tuning. Its unified access models eliminate data silos, with simplified scale that removes the pain of forklift upgrades and allows developers to code without worrying about limits.
Beyond efficiency, MDS also accelerates business outcomes. It offers faster time to results, from proof-of-concept to production. It improves compliance, with consistent data governance at scale. And it lowers total cost of ownership, via software-defined and commodity hardware deployments.
Preparing for the AI-First future
MDS is not just a tactical solution – it serves as a strategic foundation for long-term growth. It allows organisations to make a one-time investment and scale seamlessly as needs evolve, while building team expertise on a platform that supports both current and future workloads. By enabling the free flow of data and models across the organization, MDS helps foster an AI-first culture.
The storage landscape is changing
As foundation models grow and inference moves to the edge, storage strategies must adapt. New patterns – such as GPU-as-a-service and hybrid architectures – require systems that are fluid, composable, and location-agnostic.
Waiting for maturity is no longer an option. Enterprises that delay risk being outpaced by more agile competitors who align infrastructure with AI imperatives today.
MDS is more than a technical feature – it’s a business enabler. Visionary organisations recognise this and are already architecting for long-term differentiation.
Conclusion: MDS as the hidden hero
In the AI era, infrastructure is destiny. Multidimensional scaling, powered by modern object storage, is the silent engine driving success for forward-thinking enterprises. It’s not a trend or buzzword – it’s the architecture AI demands. Those who adopt MDS will do more than survive the AI revolution – they’ll lead it.
