In the 2020s, we crossed a Rubicon in the AI revolution. The once long-discussed concepts about the technology’s possibilities and pitfalls have exploded into reality. And MSPs who can leverage multidimensional scaling (MDS) likely will be able to profit from this big opportunity.
Generative AI tools are creating market momentum through breakthroughs in content creation, data analysis, and coding. 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. This amplifies the market opportunity for MSPs.
However, with great potential comes great responsibility. The pressure is on for MSPs. As stewards of digital infrastructure, MSPs must go beyond the mere baseline of provisioning resources.
Since AI services generally involve different amounts of data being accessed from multiple locations, clients need to convert traditional infrastructure approaches. This is especially true for legacy storage systems, which are inadequate for today’s demands. That makes MDS significantly viable.
MDS: A Pressing Challenge and Massive Market Opportunity
MDS, the concept that storage and compute services can be allocated in any direction and capacity, aren’t new to IT. In recent years, it has been applied to accommodate cloud computing, IoT, data lakes, and the like.
However, MDS principles will rise to new levels in a post-AI world. As a result, MSPs are now under immense pressure to help their clients scale infrastructure independently across multiple axes.
This challenge represents a major revenue opportunity.
Leverage MDS: 7 Ways to Capitalize with AI
Here’s how MSPs can leverage the MDS journey to expand their service offerings and capitalize on the growing needs around AI.
No. 1: The Flexibility to Deliver Innovation
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. They must invest in a flexible, scalable, intelligent infrastructure that aligns with business outcomes, whether that’s enabling real-time analytics, streamlining data governance, or scaling AI model training environments.
By adopting infrastructure models that prioritize flexibility and performance, MSPs can directly support their clients’ AI-driven transformations. This also allows them to secure their own growth in the process.
No. 2: Private Clouds Bridge Convenience and Compliance
Many organizations 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 that data remains secure and localized.
Paul Speciale
No. 3: Package Scale with Compliance as a Unified Offering
AI workloads are inherently unpredictable. Spikes in data usage and performance needs are common. Through MDS, 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.
No. 4: Go Local by Optimizing Infrastructure for Data Sovereignty
As data privacy regulations tighten globally, localized infrastructure becomes a necessity. By deploying regional cloud offerings, MSPs can help clients meet national data residency requirements while delivering low-latency performance.
This localized approach is a compliance measure, a strategic advantage, and a revenue generator.
No. 5: Supporting Consumption-based and Multitenant Models
The shift to AI accelerates the demand for flexible billing models, particularly from small and midsized businesses (SMBs). MSPs should offer consumption-based pricing and multitenant 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.
No. 6: Capture AI Infrastructure Spend
MSPs that proactively support AI workloads with tailored infrastructure are positioned to unlock significant new revenue streams. AI is resource-intensive; the demand for compute and storage rises in tandem with AI adoption.
MSPs that offer AI-optimized SLAs and high-throughput processing will stand out as preferred partners in this growing market.
No. 7: Enhance Margins Through Targeted Scaling
With MDS, MSPs can deliver infrastructure that precisely matches workload demands, something midmarket customers are keen on. Whether scaling up storage for massive datasets or reducing latency for inference workloads, this approach boosts efficiency and protects margins. It turns infrastructure from a cost center into a strategic asset.
Strategic Imperative: Embrace Modern AI Infrastructure
The age of AI demands a new playbook for infrastructure and MSPs play a pivotal role. By embracing software-defined MDS, service providers can provide the flexibility, performance, and compliance clients need to succeed in a data-driven world.
This transformation goes beyond just technology. For MSPs, it’s a golden growth strategy. Those who act now will be better positioned to serve AI-driven businesses, open new revenue channels, and evolve into indispensable partners in digital innovation.
MSPs that welcome the challenges of AI today will lead the market tomorrow.
Paul Speciale is chief marketing officer at cyber-resilient data storage company Scality. Prior to this, he held various leadership roles in companies such as Appcara, Amplidata, and Savvis, focusing on cloud computing and storage technologies.
Featured image: iStock
