Agentic AI is changing how the MSP business works. Is your MSP future-ready? Read this article to learn how to clean up, streamline, and upgrade your stack for agentic AI, and become the AI-first MSP clients expect.

AI has moved from pilot projects to mainstream adoption. It’s already changing how technicians work, how services are delivered, and is even reshaping how MSP businesses operate.

Yet, many MSPs are still running on models that haven’t evolved in years. To keep up with evolving client expectations and scale profitably, the new generation of MSPs has to be faster, smarter, and designed to scale. MSPs have to rebuild themselves to be AI-first. 

And this involves a lot more than just stacking on new AI-powered tools. It’s about rewiring existing ones to support agentic workflows and making foundational changes, such as cleaning up and updating data, streamlining workflows and processes, ensuring tool interoperability, and defining role clarity. Only then can the stack be truly AI-ready and designed for the agentic AI way of working.

Building your AI tech stack 

You don’t need twenty different tools to run a modern MSP. You need the right stack. A carefully considered, thoughtfully planned, tightly integrated network, ready for AI to plug in and perform.

The best way to get this done is to build an AI-ready tech stack layer by layer, so it can scale with your business.

Layer 1: Core systems

Your PSA, RMM, documentation, and project management tools form the backbone of an AI-ready MSP stack. These systems need to be structured and consistent because AI agents rely on them for actions, summaries, and triggers. If your RMM doesn’t report failed patches, your AI won’t catch them. Make the backbone AI-ready by defining ticket lifecycles clearly, syncing device metadata without gaps, and ensuring knowledge bases are searchable and accessible to AI.

Layer 2: Observability and data hygiene

Once the core systems are in order, it is critical to ensure data hygiene because AI builds patterns and decisions on the data it sees. This data includes device monitoring data, patch success rates, ticket sentiment and volume, SLA timers, and workflow logs. But signals are only useful if the data is clean. Ask yourself: Are patch results tracked per device? Can tickets be segmented by client, type, or urgency? Are customer communications captured and classified? Evaluate and refine every stream of data, because your autonomous actions will only be as good as the inputs.

Layer 3: Workflow readiness

The next layer is where you actually make the shift from basic automation to autonomous processes. This is where workflows are redesigned to make it easy and reliable for AI to take action without breaking anything. AI is only as effective as the workflows it can act on, so take the time to set processes and workflows by exploring questions like:  Can tickets be assigned automatically through APIs? Are scripts reusable and modular? Can patches, alerts, emails, and projects be linked together? The more structured your workflows, the more effective your AI becomes.

Layer 4: AI interface and agent layer

This is where your agentic AI lives, as AI agents take over repetitive tasks and free up technicians for strategic work. You begin to create multiple AI agents to handle different workflows and build an agentic workforce with L1 agents for tickets, dispatcher agents for routing, patch agents for policy-based updates, etc. Start small, begin with informational AI agents. Then allow them to act on narrow, low-risk tasks. Expand their scope of service as trust builds.

AI-readiness doesn’t end with building the stack. It also requires a change in perspective towards IT service management. What counted as success for MSPs a few years ago may not count today. As client expectations evolve and workflows shift, the way you measure success has to evolve too.

Metrics that matter

In the past, MSPs measured success by how many tickets they could close. In an AI-first world, success is defined by how many tickets never need to be created in the first place. 

If AI isn’t saving time, improving team performance, enhancing client satisfaction, or driving revenue while reducing churn, then it’s just another tool collecting dust. It’s valuable only when you can point to the business outcomes it delivers.

Here are some metrics modern MSPs should be tracking: 

Tickets resolved by AI: This looks at the percentage of L1 issues closed without human intervention. It is one of the key metrics to measure, as it directly impacts how much technician time is freed up and lowers the cost per ticket

Mean time to resolution (MTTR): This tracks how quickly issues are resolved and measures efficiency gains from AI-driven dispatch and guided resolution. AI agents can help with faster triage, and AI-suggested actions can cut MTTR by 25–40%.

Technician efficiency: Measuring tickets closed per tech, revenue per tech, and cost per resolved ticket helps you understand how teams perform and enables junior technicians to become high performers faster.

Client health score improvements: Tracking client-specific details such as uptime, satisfaction, responsiveness, and ticket load is valuable as they can be used to predict churn and highlight upsell opportunities. 

SLAs at risk avoided: This measures how often AI preemptively flags issues that would have broken SLAs and is especially critical for MSPs working with clients in the healthcare, legal, and financial sectors.

Revenue from AI-driven upsells: This is to track new MRR or one-time upsell revenue generated through AI-powered QBRs or growth agent prompts.

Nurturing an AI-first culture 


The final piece in putting the foundation in place, and truly making AI work, is upskilling your workforce and building an AI-first culture.

These could be small but tangible steps that help establish the role that AI will play. You’d have to start with training technicians to see AI as a performance booster and not a threat. Sharing examples where it saved time or prevented burnout can help gain trust. 

Be sure to redefine roles clearly and highlight how AI handles the repetitive grunt work while humans focus on strategic, complex tasks. 

Major culture shifts only stick when leadership models them. Leaders should use AI in their own work, highlight AI-driven insights in reviews, and make it clear that success is measured by how effectively AI supports outcomes. Add AI metrics to your weekly leadership dashboard and celebrate AI wins. At the same time, encourage experimentation by creating safe spaces where technicians can test AI, share results, and learn how they can make the best use of this constantly evolving technology.

Ready to become the MSP of tomorrow, today? 

Every MSP now has access to AI. But if AI isn’t used right, organizations risk wasting both the potential of AI agents and technicians’ time.

What will set a successful MSP apart from one that falls prey to technological advancements is when and how well they adopt AI. An MSP that works with the right AI tools, and pairs AI agents well with human experts, will certainly be able to scale irrespective of where the market is headed. 

SuperOps has been built to enable MSPs to drive efficiency and scale profitably. You can see for yourself how SuperOps can help you upgrade your IT infrastructure and integrate AI into your workflows; schedule a demo with our AI experts today! 


This article is an edited excerpt from the book titled ‘MSP of tomorrow, today’ authored by Damo Vasudevan, VP of Product at SuperOps, where he discusses how you can build an AI-native MSP and upgrade your stack to become the IT partner clients expect. To access the digital copy of the book, click here.