Mark Clayman, CEO of Netrio – Interview Series

Mark Clayman, CEO of Netrio, is a veteran technology executive with more than two decades of leadership experience across cloud computing, managed IT services, cybersecurity, and enterprise infrastructure. Before joining Netrio in 2024, he served as CEO of Navisite through its acquisition by Accenture, and previously led RDX and TriCore Solutions during periods of significant growth and successful acquisitions. Earlier in his career, Clayman held senior leadership roles at Navisite and served as CIO of Surebridge, building a reputation for scaling technology organizations, integrating acquired companies, and modernizing managed service operations for enterprise and mid-market customers.

Netrio is a North American managed IT services provider focused on helping small and mid-market organizations modernize and secure their technology environments. The company specializes in managed IT, cloud services, cybersecurity, infrastructure management, and digital transformation solutions, with expertise across industries such as healthcare, financial services, and retail. Following its merger with SUCCESS Computer Consulting, Netrio has expanded its reach and capabilities, positioning itself as a larger-scale MSP designed to provide enterprise-grade technology support, operational efficiency, and cybersecurity protection to growing businesses navigating increasingly complex IT environments.

You’ve led multiple managed services and cloud companies through major acquisitions, including roles at RDX, Navisite, TriCore Solutions, and now Netrio. How has your perspective on enterprise IT transformation evolved across those different eras, and what convinced you that AI would become the next foundational shift for Managed Service Providers?

Over the years, I’ve found that enterprise IT transformation is not just about a new technology category. It might start that way with managed hosting, cloud, security, automation and now AI, but the real transformation happens when that technology changes how businesses operate and compete. Earlier in my career, the focus was on infrastructure: where workloads lived, how systems were managed, how to improve uptime, and how to reduce cost. Over time, the conversation moved to how and where can technology create value for the organization. Customers started asking not just, ‘Can you run this environment?’ but ‘Can you help me become more efficient, more secure, more resilient, and more competitive?’ AI is a foundational shift because it touches all those questions at once. For MSPs, AI is not just a new tool that we add to the portfolio…It’s a paradigm shift that changes the way services are delivered, how engineers work, how customers consume support, and how we create business value.

You’ve described AI as becoming the new delivery backbone for MSPs. What does an AI-native MSP actually look like five years from now compared to the traditional MSP model enterprises are used to today?

The traditional MSP model has been built around people, processes, and tools responding to issues: monitoring an environment, opening a ticket, assigning an engineer, resolving the issue, and reporting back to the customer. An AI driven MSP will look very different. AI will sit across the entire delivery model, from monitoring and ITSM to security, endpoint management, customer communication, analytics and business workflow optimization. Within the next 2-3 years, MSP leaders will not just be using AI to answer questions faster; they will be using it to detect patterns, prioritize risk, remediate known issues, summarize and update tickets, analyze sentiment, and guide engineers toward the next best action. Also, and most important, the customer experience will be much more self-service and AI platform driven.

Netrio is pushing an “AI-first, human-second” support philosophy. What has changed in user behavior that makes customers increasingly comfortable interacting with AI before speaking to a human technician?

The biggest change is that people’s expectations have been reset by the technology they use every day. In their personal lives, users are accustomed to getting instant answers, searching for help on their own, interacting with chatbots and intelligent assistants, and resolving basic issues without waiting for someone to call them back. That behavior is now showing up in the workplace. If an AI system can help someone unlock an account, diagnose a connectivity issue, answer a “how-do-I” question, or route a request properly in seconds, many users would prefer that over the old model of submitting a ticket and waiting for someone to get back to them. However, it’s important to note that AI-first cannot mean human-never. It means we should use AI where it creates speed, consistency, and convenience, and then bring in people when the issue is complex, sensitive, or requires judgment.

A lot of companies talk about AI copilots, but you’re emphasizing agentic AI and autonomous remediation. How close are we to AI systems independently resolving the majority of routine IT incidents without human intervention?

We are getting closer, but I think it is important to be practical about what autonomous really means. There are already many routine incidents where AI can diagnose the problem, recommend the action, take the action, and update the ticket in a way that looks very similar to what a human engineer would do. That is very different from a simple chatbot or copilot that only suggests an answer.

There is a big opportunity leveraging agentic AI that can operate within defined guardrails: it knows the environment, understands the policy, has the right access, and can remediate known classes of issues safely. At Netrio, because of the complexity of our environments, we think carefully about where to use partner-native AI and where we need to build our own capabilities. We support many customers across different environments, and an AI agent has to understand that multi-tenant complexity. In some areas, like infrastructure alerting or security alert consolidation, mature vendor AI may be the right answer. In other areas, especially where the workflow spans customer environments, endpoint access, credentials, and remote support, MSP-specific intelligence becomes very important.

One of the more interesting ideas you raised is AI-driven sentiment analysis on every support ticket. How important will emotional intelligence and customer sentiment become in the next generation of enterprise IT operations?

I think it will become a major part of how service quality is measured. In managed services, it’s not enough to know whether a ticket was closed or whether an SLA was met. You also need to know how the customer felt during that interaction. Were they frustrated? Were they confused? Did they feel heard? Did the issue keep recurring? Historically, MSPs might learn about those patterns in a monthly or quarterly business review or from a customer satisfaction survey, which is useful but often too late. AI-driven sentiment analysis gives us the ability to understand customer experience in near real time. That means we can identify where a customer is getting frustrated, where communication is not clear, or where a recurring issue is damaging trust, and we can intervene quickly. That’s the point where AI amplifies the human side of service, rather than replacing it.

You’ve mentioned that many organizations are struggling with “shadow AI” inside their companies. What are the biggest governance mistakes enterprises are making right now as employees rapidly adopt AI tools on their own?

The biggest mistake is letting AI adoption happen in a completely fragmented way. Employees are going to experiment, and in many cases that is a good thing, but the organization needs to understand what tools are being used, what data is being entered, and what risks are being created. Right now, a lot of companies have individuals using AI in pockets across the business, but there is no common policy, no measurement framework, no training model, and no clear roadmap to align with organizational goals.

That creates security risk, compliance risk, and wasted efforts. The ideal approach is not to shut experimentation down but instead formalize and organize it. Companies need governance frameworks, access controls, employee training, approved use cases, and a way to measure value. This is especially true in the mid-market, where companies often have the same pressure from boards and leadership to leverage AI, but they may not have the internal AI expertise, security, and compliance team to guide the effort.

As AI automates more basic MSP tasks, do you see the traditional economics of managed services fundamentally changing over the next decade?

Yes, I think the economics will change quite a bit. The traditional managed services model has often been built around labor, tools, and margin. If AI and automation reduce the amount of human effort required for basic support, monitoring, and remediation, then pricing pressure on those commoditized services is inevitable. But I do not view that as a negative. I view it as a forcing function for MSPs to move up the value chain. The value will increasingly come from helping customers apply technology to business outcomes: improving operations, automating workflows, strengthening security, and making better decisions.

That is why strategic conversations with customers are so important. MSPs need to understand how a customer runs their business – their supply chain, customer service model, internal processes, growth plans, and risk profile. Then we can use AI and automation to solve higher-value problems. Then the economics shift from how many people it takes to support an environment to what measurable value the MSP is creating for a business.

Many AI conversations focus on large enterprises, yet mid-market companies often lack the internal expertise to deploy AI responsibly. What unique challenges do mid-market organizations face that are being overlooked by the broader industry?

Mid-market companies are in a very interesting position. They have real business pressure to adopt AI, and in many cases, they have the same urgency as larger enterprises. Their leadership teams and boards are asking what is our AI strategy, where are the efficiencies, and how the company is going to stay competitive. But as mentioned earlier, they don’t have the same internal resources as a Fortune 500 company. They don’t have a dedicated AI governance team, a large security organization, a mature data science function, or the change management resources needed to move from AI pilots to production. So, they can get stuck between experimentation and execution. They know AI matters, but they need help identifying the right use cases, putting governance in place, securing the environment, training employees, and integrating AI into real workflows. That’s why our approach at Netrio is focused on practical adoption: readiness, governance, strategy, platform deployment, enablement, and ongoing development and support.

Netrio operates across industries including healthcare, financial services, and retail. Do you believe the future winners in AI-enabled managed services will be the companies with the deepest vertical expertise rather than the broadest technology stack?

Yes, I do. A broad technology stack is important, but it is not enough on its own. The real value comes when you understand the customer’s business deeply enough to know where AI can actually make a difference. Healthcare, financial services, retail, manufacturing, hospitality – each of those industries has different workflows, regulatory pressures, customer expectations, risk profiles, and operational realities. A generic AI deployment might create some efficiency, but a domain-specific solution can solve a much more meaningful problem. For example, the way you think about AI in a regulated financial services environment is different from how you think about AI in retail operations or hospitality workflows. The MSPs that win will be the ones that combine AI, automation, security, and vertical expertise.

Looking ahead, what parts of enterprise IT management do you believe will remain fundamentally human-driven no matter how advanced AI systems become?

The human-driven parts will be the areas that require trust, judgment, empathy, and strategic context. AI will continue to take on more of the repetitive operational work, and that is a good thing. It will help engineers move faster, improve response times, identify patterns, and automate known issues. But customers still need people who understand their business, can help them make tradeoffs, and can stand behind the recommendations being made. Security is a great example. AI can detect threats, prioritize alerts, and automate responses, but building a culture of security still depends heavily on people, training, leadership, and judgment. The same is true for AI adoption itself. You can deploy powerful tools, but someone still has to decide which use cases matter, what risks are acceptable, how employees should be trained, and how success should be measured.

Thank you for the great interview, readers who wish to learn more should visit Netrio.