The AI gold rush is on—why MSPs hold the key to infrastructure success and growth

Guiding through the AI-driven infrastructure shift

In the 2020s we’ve crossed a Rubicon in the AI revolution. What were once long-discussed concepts about the possibilities and pitfalls of AI have exploded into reality. With generative tools leading breakthroughs in content creation, data analysis, and coding, the market reflects this momentum - AI is set to soar from $93 billion in 2020 to $826 billion by 2030. Moreover, key analysts like Gartner now predict a massive growth in enterprise demand for consumption-based as-a-service based offerings, further amplifying the market opportunity.

However, with great potential comes great responsibility. The pressure is now on for managed service providers. As stewards of digital infrastructure, MSPs must go beyond the mere baseline of provisioning resources. They need to entirely reimagine their role in helping clients harness AI effectively. But this key challenge is also a chance - and nowhere is this more true than in today’s dynamic AI landscape. If MSPs play this right, they can turn this moment into a golden opportunity, expand their service offerings and capitalise on the growing needs around AI.

Legacy storage is tired

Traditional infrastructure approaches - especially legacy storage systems - are now totally inadequate for today’s demands. They are not designed to handle the unpredictable, dynamic, high-throughput demands of AI workloads. To remain relevant and create long-term value, MSPs should therefore opt for super-agile, software-defined, multidimensional scaling solutions. This approach empowers them to scale infrastructure independently across multiple axes, providing the flexibility that AI workloads demand.

As AI reshapes industry expectations, clients want partners who understand the strategic value of AI and can architect infrastructure that accelerates innovation.

To step into this expanded role, MSPs must evolve from service providers into strategic AI advisors. This means investing in a highly flexible, scalable, intelligent infrastructure that aligns with business outcomes - whether enabling real-time analytics, streamlining data governance, or scaling AI model training environments. By adopting infrastructure models that prioritise flexibility and performance, MSPs can directly support their clients’ AI-driven transformations and secure their own growth in the process.

Delivering hyperscaler agility in the private cloud

Many organisations seek the elasticity of public clouds but require the data control and compliance guarantees of private environments. MSPs can bridge this gap by deploying private cloud platforms that emulate the agility of hyperscalers. With automated scaling, user-friendly interfaces, and rapid provisioning, MSPs can meet client expectations while ensuring data remains secure and localised.

Packaging scale with compliance as a unified offering

AI workloads are inherently unpredictable, with spikes in data usage and performance needs. Through multidimensional scaling, MSPs can fine-tune their infrastructure - ramping up resources only where needed. This not only prevents overprovisioning but also ensures that sensitive data remains compliant with industry regulations, such as GDPR or HIPAA.

Going local: optimising infrastructure for data sovereignty

As data privacy regulations tighten globally, localised infrastructure is becoming a necessity. By deploying regional cloud offerings, MSPs can help clients meet national data residency requirements while delivering low-latency performance. This localised approach is not just a compliance measure - it’s a strategic advantage.

Supporting consumption-based and multi-tenant models

The shift to AI is accelerating the demand for flexible billing models. MSPs should offer consumption-based pricing and multi-tenant architecture to accommodate the bursty, iterative nature of AI development. This ensures clients can scale up or down based on actual usage, improving satisfaction while maintaining cost transparency.

MSPs that proactively support AI workloads with tailored infrastructure are positioned to unlock significant new revenue streams. AI is resource-intensive, and clients are seeking partners who can meet these requirements with resilient, high-performance solutions.

Capturing AI infrastructure spend

The demand for compute and storage is rising in tandem with AI adoption. MSPs that offer AI-optimised SLAs, scalable capacity, and high-throughput processing will stand out as preferred partners in this growing market.

With multidimensional scaling, MSPs can deliver infrastructure that precisely matches workload demands. Whether scaling up storage for massive datasets or reducing latency for inference workloads, this tailored approach boosts efficiency and protects margins - turning infrastructure from a cost center into a strategic asset.

Why now is the time to embrace modern storage offerings

The age of AI demands a new playbook for infrastructure - and MSPs have a pivotal role to play. By embracing software-defined, multidimensional storage scaling, MSPs can provide the flexibility, performance, and compliance clients need to succeed in a data-driven world.

This transformation isn’t just about technology—for MSPs, it’s a golden growth strategy. Those who act now will be better positioned to serve AI-driven businesses, open up new revenue channels, and evolve into indispensable partners in digital innovation. MSPs that welcome the challenges of AI today will lead the market tomorrow.