AI as Infrastructure: Leading U.S. MSPs See AI as a Core Service, Not a Future Add-On

For years, AI has been positioned as a future enabler, an optional enhancement for tech-forward companies, or an add-on for tomorrow’s IT stack. However, the era of AI as an afterthought is over. The managed service provider (MSP) community, long at the center of small and mid-sized business innovation, now sees AI not as a future add-on but as core infrastructure. Today, 64% of MSPs report that they are already using AI in internal processes and service delivery. More than 40% say AI services are now a primary business differentiator.

The shift is subtle but seismic. It’s not just about helping customers implement AI; it’s about re-architecting the very foundation of managed services using AI itself. High-growth MSPs are automating policy enforcement, threat detection, customer support, identity governance, and even decision-making. As enterprise AI spending is projected to skyrocket to $309 billion by 2032, the organizations best positioned to win aren’t necessarily the ones with the flashiest large language models (LLMs), they’re the ones with the infrastructure to make AI real, reliable, and scalable. That puts MSPs squarely in the spotlight.

From Tools to Infrastructure: The AI Inflection Point

AI’s evolution from a productivity tool to operational infrastructure is rewriting the rules for MSPs. In the past, core MSP functions centered on monitoring, patching, securing, and supporting IT environments. These tasks, while critical, were largely reactive. With AI, they’re now proactive and predictive.

Take security, for example. AI is becoming the control plane of enterprise risk. Vendors like CrowdStrike and Palo Alto Networks are already reporting AI-driven revenue growth as they embed intelligent detection and automated response capabilities directly into their platforms. Legacy infrastructure is struggling to keep up.

For MSPs, this transformation isn’t theoretical; it’s operational. AI is being used to automate security policy enforcement at machine speed, reducing risk and human error. It enables the proactive detection of threats using behavioral analytics and anomaly detection, identifying risks before they escalate. MSPs are also delivering 24/7 support through intelligent, context-aware chatbots that provide real-time assistance. Identity orchestration for onboarding and offboarding is being streamlined through AI, saving time and enhancing security. Patch cycles are also being managed based on real-time vulnerability intelligence, ensuring systems remain current without the delays of manual processes.

These are not pilot projects. They are production-grade systems being implemented now.

Why MSPs Are Becoming the Frontline of AI Adoption

MSPs are uniquely positioned to lead the AI charge for a simple reason: they already manage the complexity of IT environments that small and medium-sized businesses (SMBs) depend on every day. While enterprise tech giants may set the standard in model development and cloud innovation, it’s MSPs who operationalize those advancements for the rest of the economy.

In many ways, MSPs are the real AI adoption layer. They translate promise into practice. This means they’re on the frontlines of helping organizations navigate AI governance and compliance requirements, integrate AI into zero trust architectures, modernize endpoints and infrastructure for AI workloads, and ensure security and resilience in hybrid environments.

AI is not just another service offering, it’s becoming the foundation of all services. MSPs who treat AI as an add-on will struggle to remain competitive. Those who treat it as infrastructure will define the future of managed services.

AI Is Redefining “Managed”

What does “managed” mean in the AI era? For years, managed services were defined by SLA-based support, uptime guarantees, and cost efficiency. AI is raising expectations dramatically.

“Managed” now means:

• Predictive instead of reactive: Systems must detect, diagnose, and remediate problems before customers even know they exist.
• Context-aware instead of rules-based: Service automation must understand intent and adapt to nuanced user behaviors.
• Always-on instead of business hours: Intelligent agents deliver 24/7 support and escalation paths, tailored to each customer.
• Secure by design: AI-enabled platforms must integrate security policy enforcement as a default, not an overlay.

This shift is forcing a new business model for MSPs, one that values outcomes over effort and intelligence over hours billed. In many cases, this requires rethinking not just the tools MSPs use but how they structure their teams, train their staff, and price their services.

Customers Expect More, Faster

As AI continues to saturate enterprise and consumer technology alike, customer expectations are rising fast. Businesses that once tolerated ticket-based support now expect real-time resolutions. Manual updates feel archaic. And policy enforcement that takes days instead of seconds is seen as a security risk, not a procedural delay.

MSPs are expected to deliver instant onboarding across identity, endpoints, and applications, reducing delays and accelerating productivity from day one. Proactive compliance monitoring must be in place to keep pace with ever-changing standards, particularly in highly regulated sectors. Customers increasingly demand integrated AI tools that they are able to deploy in their own environments, enabling them to take control of their data and decision-making. Detailed AI observability and explainability are becoming essential, ensuring that AI-powered decisions are transparent, traceable, and trustworthy.

AI isn’t replacing the human touch in managed services; it’s enhancing it. The MSPs that thrive in this new landscape will strike the right balance between automated intelligence and human judgment, between speed and accountability.

Owning the AI Infrastructure Layer

Enterprise AI may be a $309 billion opportunity, but the spoils won’t go to those who merely resell AI tools. The real value lies in owning the infrastructure hardware, software, identity, data, and access on which AI runs. MSPs that succeed in embedding AI into their core offerings will move beyond support providers to become strategic infrastructure partners.

To succeed, MSPs must forge partnerships with AI-native infrastructure providers who can help them scale effectively and securely. They need to build scalable, secure, and observable management layers that offer visibility across customer environments. Internal teams must be trained in emerging skills like prompt engineering, model oversight, and full-lifecycle AI governance. Critically, MSPs must deliver AI-enabled services that are measurable, outcomes-based, and simple for customers to consume.

In doing so, MSPs future-proof their business, differentiate themselves in a crowded market, and, most importantly, help their customers grow faster and safer.

The Year of the AI-Native MSP

We may look back on 2025 as the year AI stopped being a tech trend and started becoming a baseline expectation in managed services. For many MSPs, the shift is already underway. The winners in this new era won’t be those who talk about AI as the future, they’ll be the ones quietly building it into the fabric of everything they offer.

The playbook is being rewritten. The question is no longer whether MSPs will adopt AI. It’s how well they do it and how fast.