Future of Managed Service Providers: Automation, Security, and AI

The role of managed service providers (MSPs) has undergone a fundamental transformation over the last two decades. Starting as simple value-added resellers (VARs) focused on moving hardware, they evolved by adding services and, eventually, a software layer, creating new categories of service offerings. The key differentiator for success became the ability to capture recurring revenue, rather than relying on one-time sales. This transformation is critical because the central metric for valuing an MSP business today is its EBITDA (profit margin). Companies looking to be acquired for a reasonable multiple must establish strong fundamentals, focusing on growth and robust profit margins. Michelle Accardi, with her experience running roll-up MSP Logically, stresses that a healthy business is defined by its ability to generate revenue and maintain profitability.

A crucial financial benchmark discussed for these service businesses is the "Rule of 40," a metric commonly used in the SaaS world. This rule suggests that the sum of a company’s growth rate and its profit margin (EBITDA percentage) should roughly equal forty percent. Many MSPs, however, struggle to meet both growth and profitability goals simultaneously. To achieve this level of performance, MSPs must focus on driving profit through either growth (by adding new services) or improving efficiency (through cost-cutting/automation). Ultimately, businesses aiming for the highest returns should target an EBITDA margin of 15% to 20% combined with a growth engine of 15% to 20%. This strategic focus on financial health is crucial for achieving long-term success and a favorable business valuation.

Driving efficiency is the cornerstone of a successful modern managed service provider (MSP) business. Outside of the Rule of 40 metric, core success attributes for an MSP include automation, a strong toolset, and the ability to leverage resources nearshore or offshore. However, the foundational element is having the right people in place who align with the core value proposition. Beyond operational efficiency, the most critical factor is the ability to monetize the technology stack. MSPs often experiment with new technology but fail to develop offerings around it to sell to their customer base. Every dollar spent on the tech stack must be viewed as an investment intended to generate more revenue and provide additional value to the end customer. This approach ensures that the business is not just technically capable, but fundamentally profitable and scalable for the future.

For growing managed service providers (MSPs), especially those with several hundred customers and a team of ten to twenty people, identifying areas for growth can be challenging in a brownfield environment where they must displace a competitor. Michelle Accardi suggests that the most critical first step, which sounds rudimentary, is for the MSP to understand what their customers truly have. This means taking a comprehensive inventory of all assets. From this inventory, MSPs can discover a wealth of information that informs them about what new, high-value services, particularly in security and IT automation, they should be selling. By understanding how a customer's environment changes on a daily, monthly, or yearly basis, an MSP can help them rationalize their existing IT and security spending. This focus on a source of truth for assets, though unsexy, is where the real money is found in the services business.

Liongard’s core offering aligns perfectly with this need for a source of truth, establishing itself as a cybersecurity SaaS platform. Their platform automates asset discovery, inventory, and monitoring of configuration changes to identify vulnerabilities and risks preemptively. The company targets MSPs and MSSPs with more than twenty customers, as complexity in asset inventory and risk management increases with customer count. A key feature is the use of AI to generate asset summaries for account managers, enabling them to discuss customer environments intelligently without requiring a technical background. By integrating with over 90 different IT systems, Liongard becomes a reliable, central source of truth for MSPs, enabling them to build their own automation on top, whether through RPA or agent-based AI.

The discussion extends into the emerging concern of Shadow AI—users bringing their own unmanaged AI tools into the workplace, similar to the "bring your own device" trend. Managing these tools is complicated as they don't fit into traditional hardware or human resource management systems. Michelle Accardi argues that the core focus for security shouldn't be the AI tools themselves, but rather identities. Security must focus on identifying which identities have access to critical systems and underlying data and ensuring that access is properly tracked and controlled. Liongard is also integrating generative AI directly into its platform with the upcoming Answer IQ feature. This will enable partners to utilize natural language search to query the massive data lake for immediate insights, such as identifying which customers lack MFA-enabled accounts or determining which ports on a firewall pose a risk, thereby democratizing technical data for non-technical account managers.

The immediate focus for managed service providers (MSPs) in adopting AI is two-fold: first, helping customers leverage available tools, such as Microsoft Copilot. Second, and more importantly for sophisticated MSPs, is utilizing AI internally to enhance their own service delivery and achieve efficiency. This internal automation, often achieved by mining data from a source of truth to identify new service offerings, must precede external services. Horizontal use cases, such as Copilot, are the current primary offerings, although some niche players are developing vertical-specific applications for industries like legal and hospitality. Ultimately, the goal is for MSPs to leverage AI to increase their efficiency before creating new bundled offers for their customers.

A significant area of transformation is the use of AI agents to handle basic, high-volume customer requests. Instead of logging a traditional ticket, customers can use a self-service interface, such as a ChatGPT-like bot, to resolve simple problems and escalate to a human technician only when necessary. This shift is already evident in margin-constrained businesses, such as the hospitality and retail industries. For MSPs, this means the first line of defense—solving common, simple issues like Wi-Fi connectivity problems—can be automated. While this automation helps drive necessary profit margins, it also presents a risk to entry-level engineers.

The changing landscape of service delivery has a direct impact on the talent pool. Historically, MSPs recruited frontline support from community colleges and trade schools. While new automation in the PSA (Professional Services Automation) industry created new categories and jobs in the past, the rise of AI agents means the path forward is complex. Michelle Accardi suggests a bifurcated path: some systems will utilize agentic AI to replace lower-skilled talent. In contrast, others will create new paradigms where talent focuses on roles such as training AI models. The consensus is that lower-skilled workers are most at risk. However, top-tier talent with critical thinking skills will remain indispensable for solving edge cases and complex problems that an AI model cannot efficiently address. The core skill set for future success encompasses not only technical knowledge but also curiosity, building a strong network, and understanding the economics of business.