There’s no denying that AI is the next major shift in how MSPs deliver value. But diving in headfirst without fully understanding the tools, use cases, and implications can be risky. That’s true not only for you, but for your clients as well. The smart move? Build your own internal AI lab.
An AI lab is a safe, low-cost environment where your team can experiment, learn, and evaluate tools before rolling them out into production.
This guide is all about helping you do just that, without blowing the budget or adding risk to client operations.
Start with a Clear Objective
Before grabbing the latest AI-powered ticketing plugin or chatbot tool, pause and define your goals. What are you hoping to test or learn? Is it automating internal tasks like documentation or time entries? Exploring AI for endpoint monitoring? Enhancing customer interactions?
A solid AI lab doesn’t just play with shiny objects. It sets clear objectives like:
Rania Succar
- Can this tool save us X number of hours?
- Can it reduce response times or boost accuracy?
- Could it replace a manual task we struggle to staff?
Defining the “why” will help you evaluate AI solutions with a business mindset, not just a curiosity. “Done right, AI amplifies your strengths,” Kaseya CEO Rania Succar said during her opening keynote at DattoCon 2025.
Designate a Safe, Isolated Environment
Your AI lab doesn’t need a separate building or its own server rack. It needs boundaries, especially for tools that touch tickets, data, or any production-related activities.
Set up a sandboxed environment, such as a dedicated Microsoft 365 tenant, a cloned PSA instance, or simply a small group of internal devices disconnected from client networks. Keep the lab isolated so the tests don’t disrupt real workflows.
Make it known that the lab is for exploration, not production use. This helps avoid the accidental “Hey, why is this ticket getting weird AI responses again?” moments.
Use Internal Workflows as Your Testing Ground
One of the best things about being an MSP is you already have plenty of repetitive internal workflows. It makes them perfect candidates for testing AI.
Start small with automations. For example:
- Draft emails based on ticket data.
- Summarize meeting notes or client calls.
- Create internal documentation from chat transcripts.
- Parse logs or alerts into more readable summaries.
You’ll learn how different AI tools handle structured versus unstructured data, whether they integrate well with your current stack, and how much manual oversight they still require.
These experiments build internal confidence and help your team see the real-world value.
Keep It Lean and Use Freemium Tools
There’s no need to pay enterprise prices during the experimentation phase. Many AI platforms offer freemium models or free trials that are more than sufficient for internal use, such as:
- ChatGPT and Claude for text-based analysis or ticket summarization
- Zapier with AI integrations for workflow automation
- Microsoft Copilot in a dev/test 365 tenant
- Open-source AI tools for parsing and analysis
Note: You must fully understand each platform’s data privacy policies. Even in a lab environment, don’t feed confidential client data into third-party tools, especially anything hosted overseas or that lacks enterprise-level protections.
Create a Feedback Loop with Your Team
An AI lab isn’t just about testing tools. It’s about training minds. Encourage your team to share what’s working, what feels clunky, and what’s just plain annoying.
Set regular internal “AI lab reviews.” Here, your engineers, help desk staff, and even account managers can bring new ideas, report on experiments, or flag issues. You’ll get better insights when the team takes ownership of the process. The more collaborative the culture, the faster the learning curve.
Build Use Cases and Document Learnings
As you test tools, capture the impact. Did an AI summary save 10 minutes per ticket? Did a script auto-fill fields faster than a tech could click through them? Was the chatbot more helpful or just more confusing?
Then, start building internal case studies. These will be priceless when it’s time to:
- Pitch new services to clients
- Train junior techs or sales staff
- Justify budget for premium tools
Your AI lab should evolve into a knowledge base of lessons learned, not just a collection of half-tested plugins.
When You’re Ready, Start Small with Clients
Once you’ve seen solid results internally, you can roll out carefully selected tools to clients — but with a strong framework. Set clear expectations, provide human oversight, and monitor closely.
Remember, what works for your team won’t always work in a client environment, especially if they’re not as tech-savvy or process-driven. Since you have tested it yourself, you’ll be much better equipped to handle hiccups, objections, and adoption challenges.
Final Thought: An AI Lab is a Muscle, Not a Project
This isn’t a one-and-done experiment. It’s an ongoing commitment to innovation. AI will keep evolving, and new tools will keep emerging. Having a dedicated place to safely test them—and a team excited to try—means you’ll always be ahead of the curve.
Even if some experiments flop (and they will), the process builds a smarter, more agile MSP. Now, you’re ready to lead your clients into the future.
Next Steps
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- Have a question for our experts? Send it to editors@channelpronetwork.com
ChannelPro has created this resource to help busy MSPs streamline their decision-making process. This resource offers a starting point for evaluating key business choices, saving time and providing clarity. While this resource is designed to guide you through important considerations, we encourage you to seek more references and professional advice to ensure fully informed decisions.
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