AI is upending Enterprise Architecture–what are the opportunities for MSPs?

• MSPs must design infrastructure fit for real‑time, high‑demand AI workloads
• Mesh‑style network design, edge computing, SD‑WAN optimization and advanced cooling are becoming the norm
• Cost efficiency is part of the architecture and workload “right‑sizing” essential
• MSP teams need to continuously upskill in AI infrastructure deployment and orchestration
• Collaboration with cloud providers, hardware vendors and network specialists helps deliver scalable, secure, AI‑ready architectures

AI is driving value and growth across all kinds of organizations—but it’s a hungry beast that’s reshaping enterprise architecture at breakneck speed. What exactly is the impact, and how can MSPs lead from the front and keep pace with the change, designing enterprise architecture that’s ready for the future and helping clients navigate AI integration? 

AI is rapidly impacting enterprise architecture

It’s not hard to imagine that rolling out resource-hungry AI has a huge impact on enterprise architecture (EA), the strategic blueprint that aligns business processes, technology infrastructure, and information systems with business goals.  

A fit-for-purpose enterprise architecture goes a long way in enabling not just the day-to-day but also alignment, standardization, and growth. Supporting the design and delivery of enterprise architecture gives MSPs an opportunity to build efficiency and innovation into your client’s operations as well as structure our ongoing relationship with them.  

But the beast that is AI is still growing quickly. A May 2025 Gartner analysis indicated that by 2029, 50% of Cloud compute resources will be dedicated to AI applications, a huge jump from the 10% we see today, while IDC Research reports that 47% of North American companies found GenAI impacted connectivity and IT roadmaps throughout 2024. 

How is AI changing enterprise architecture?

It’s no secret that AI data centers consume masses of water, but you might not know that just one AI data center campus can, at peak demand, use the same amount of energy as the annual energy consumption of a city of 1.8 million people. And while raw resource consumption is a major concern—especially for data center providers—when specifically thinking about IT architecture, there are many more details to consider.  

That’s because AI use cases fundamentally alter the requirements of the supporting computing and networking infrastructure. An example: AI use cases working in real time—autonomous vehicles, manufacturing robots, high-frequency financial trading algorithms.  

The nature of these workloads means that AI apps become very sensitive to latency, where even a minor network bottleneck can disrupt mission-critical applications. 

General-purpose network infrastructure doesn’t work well when latency is lagging, so MSPs now need expertise in building highly optimized, ultra-low latency AI infrastructure that supports real-time AI use cases. 

Indeed, many of the changes that make enterprise architecture fit for AI revolve around networking.  

In Cisco’s recent global study of senior IT and business leaders, Chintan Patel, CTO and Vice President of Solutions Engineering, said “AI is changing everything — and [networking] infrastructure is at the heart of that reinvention. Those who act now will be the ones who lead in the AI era.” (4). 

A few other ways in which AI is changing the fabric of enterprise architecture include: 

• New AI projects demand rapid scaling of specialized, often GPU-based computing resources that may need to be switched on—and off again—very quickly. That’s a pronounced jump in agility and responsiveness across the enterprise architecture.
   
• AI can be data-intensive, transmitting massive datasets. That impacts WAN design and Cloud interconnect strategies, which now need to support enormous bandwidth requirements.
   
• With AI, compute and storage often happen close to the Edge, even on the user device. It demands an enterprise architecture that looks less like a hub-and-spoke model, and more like a mesh-like network design. 

Cost is critical too. That power-hungry AI app can quickly push up Cloud vendor bills, which risks erasing the benefits of using the AI in the first place. So, the entire enterprise architecture must be built around cost efficiency.  

Reskill quickly—and continuously 

MSPs that ignore the impact of AI on enterprise architecture will get caught out: Cisco’s survey suggests 97% of respondents are expanding the use of AI. After all, your clients rely on your expertise to prepare them for technological change—not to react to it. 

Reskilling is arguably top of the priority list. Training teams in topics like automated provisioning and orchestration is key, given how quickly AI moves and how rapidly companies need to fire up AI infrastructure. Awareness across the following is also crucial: 

• Building, monitoring, and maintaining high-density, high-power AI server racks, including advanced cooling solutions and high-performance interconnects. 
• Network management, with skilled team members who can apply SD-WAN technology to ensure intelligent bandwidth allocation for AI traffic. 
• Remote management and troubleshooting of distributed Edge compute facilities that do serious AI work. 
• Cloud FinOps practices, and AI-specific measurements like watts per inference/training hour. 

Reskilling is likely built into your practice already, but it needs to go from regimented efforts to a dynamic effort if you truly want to stay ahead.

Adjust workflows, too 

There is also an onus on updating workflows as AI mandates more dynamic and automated operations for MSPs. 

But even for the proactive, workflows must now integrate predictive analytics to anticipate and address infrastructure issues before they impact AI workloads. 

Enterprise workflows will increasingly rely on comprehensive network observability and AI-powered data enrichment for detailed insights into complex AI traffic patterns and resource utilization for “right-sizing”. 

How can MSPs turn AI architecture into an advantage? 

As we often say at MSP GLOBAL, change brings opportunity. Your clients are understandably anxious about the pace of adjustment imposed by AI, which leaves scope to lift your offering above and beyond the competition by becoming an AI enabler—but only if you reposition by offering modern, adaptable, AI-first enterprise architecture including: 

• AI Infrastructure-as-a-Service: the ability to design, deploy, and manage AI-ready infrastructure, including high-density compute, advanced cooling solutions, and high-performance networking—plus a core capability to “right-size”. 
   
• Cost optimization and FinOps for AI: expertise in specialist AI FinOps, optimizing workload placement for efficiency, and providing detailed cost-per-inference/training-hour analysis.
   
• Edge computing practice: knowledge to build distributed edge infrastructure, local data preprocessing, and ensuring seamless connectivity and orchestration from Edge to Cloud.
   
• Network modernization: the capacity to upgrade and manage next-generation fiber optic infrastructures and implement SD-WAN solutions that intelligently handle AI traffic. 

There are the security complexities of AI to contend with as well, because those security responsibilities are in your hands. Make sure you build the capability to secure AI models, data pipelines, and distributed AI deployments into your SecOps practice. 

The reliable hinge of partnership 

Finally, partnerships are key: partners provide the tools you need to serve clients’ growing AI requirements. It also helps you build a fit-for-purpose AI infrastructure ecosystem. That includes working closely with Cloud providers to understand and leverage their AI services and specialized hardware, while helping clients optimize cloud consumption for AI. 

Where your clients prefer to host their AI workloads on premise, consider working closely with leading GPU and specialized AI chip manufacturers and server providers to gain expertise and preferential access to cutting-edge AI compute technologies and cooling solutions. 

It’s also worth seeking out vendors of high-speed fiber solutions, SD-WAN platforms and the like to make sure you don’t miss opportunities to build AI-ready network performance into your client’s enterprise architecture. 

Get it right now, reap the rewards 

It’s true that with every technological change comes new architectures, reskilling, and the opportunity to stand out for technology solution providers that move quickly. 

The difference is that with AI, MSPs are under pressure to act faster than before. But the good thing about that? Get it right, and the opportunity to use AI-first enterprise architecture to make a difference to your client’s business operations and rise above the competition is arguably even greater than any other technological shift.

MSP GLOBAL 2025: Your front-row seat

AI and its role in the digital transformation ecosystem is one of the key topics we’ll be covering at MSP GLOBAL in October. Get a front-row seat at the below sessions that will help you turn AI and its impact on enterprise architecture into an advantage:

Build Your Own AI: An MSP Workshop for Operational Success, with Jon McCarrick, Senior Director of Education, Acronis
Where: Masterclass Stage
When: Wednesday October 22nd, 09:55am – 10:55am

From Infrastructure to Intelligence: How AIaaS and Managed Cloud Services Are Reshaping the Digital Landscape, with Ibrahim Edin, General Manager, ICT Cloud Computing Services GmbH
Where: Acronis Security Arena
When: Wednesday October 22nd, 15:15pm – 15:35pm

Leading in the Age of AI and Distributed IT: How MSPs Can Thrive with Secure, Scalable Access, Michael Reeves, Global Director, MSP Channel, 1Password
Where: Elevator Stage
When: Thursday October 23rd, 10:30am – 10:40am

The AI Inflection Point: Defining the Future of Your MSP, Arvind Parthiban, CEO and Co-founder, SuperOps
Where: Acronis Security Arena
When: Thursday October 23rd, 15:25pm – 15:45pm