Part 2 of this story picks up where we left off: because once AI curiosity is high, the next step is building AI confidence through structure.
AI adoption for MSPs isn’t limited by tools – it’s limited by process clarity. If you want AI to deliver predictable outcomes, you need documented workflows and structured data first.
When I spoke with Dean Lause, the conversation wasn’t about prompts, platforms, or shiny demos.
It was about something far more foundational:
If you can’t document it, you can’t automate it.
Why Most AI Automation Fails Before It Starts
AI is already inside most SMB environments.
Quietly.
Informally.
Without guardrails.
Teams are experimenting. Leaders are curious. Everyone’s hoping for faster results.
But curiosity isn’t confidence.
Confidence comes when AI stops being “interesting” and starts being repeatable.
And repeatability requires structure.
Dean said it plainly: if you don’t have good documentation on your process, there’s no way you’ll effectively utilize AI – especially if your goal is automation.
That line matters because it separates two very different outcomes:
- Using AI to “fix something quickly”
- Using AI to run a process consistently
One is reactive.
The other is operational.
How to Get Predictable AI Outcomes: Start with the Use Case
Here’s what I’ve learned: the best AI results don’t come from better prompts.
They come from better process definition.
Because if the process isn’t documented, you can’t define:
- what the inputs are
- where the data comes from
- what “done” looks like
- what exceptions exist
- what the expected outcome should be
And without that, AI becomes unpredictable.
You may get an answer.
But you won’t get an outcome you can trust.
Why Structured Data is the Real Foundation of AI
When I responded to Dean, I emphasized something every MSP and SMB needs to hear:
AI requires structured data.
If you’re going to pull data, push data, or connect systems into an AI workflow, you need clean and consistent inputs.
Otherwise, the “AI transformation” becomes the oldest truth in technology:
garbage in, garbage out.
This is why AI adoption doesn’t start with automation.
It starts with understanding:
- where the data lives
- whether it’s structured
- whether it’s accurate
- whether it’s usable across the workflow
How MSPs Monetize AI the Right Way
This is where the opportunity becomes real.
Dean made a powerful point: once you go through the work internally – documenting your systems and defining what you want AI to produce – you can turn around and bring that same framework to your customers.
Not as a tool pitch.
As a business conversation.
A better question MSPs can ask is:
“What are you doing every day that’s repetitive, and doesn’t need a human brain to do?”
That’s how you uncover high-value use cases that can evolve into agentic workflows.
And that’s how MSPs become AI specialists: by delivering outcomes, not hype.
From there, you can monetize it as a service: whether you build it in n8n, on a low-code platform, or through a custom automation stack.
This is also where AI consulting for MSPs becomes a real growth lever: helping clients define use cases, structure data, and operationalize automation in a controlled way.
AI By Design Means Discipline Before Scale
If you’re an SMB, the fastest path to AI value is not “more tools.” It’s better structure.
If you’re an MSP, the biggest differentiator isn’t being the first to sell AI.
It’s being the one who can make it work predictably.
Because when processes are documented and data is structured, AI becomes something you can scale with confidence – supported by the right operational foundation, like Managed IT Services.
And that’s the real message Dean and I landed on:
AI doesn’t start with automation. It starts with clarity.
More AI By Design conversations coming your way, stay tuned.
