In the first part of my conversation, we spent time on how MSPs can bring AI to market in a structured way. That conversation was largely about strategy.
This part is different.
It’s about what happens once those conversations start happening with customers.
And very quickly, you realize something has shifted. AI is no longer sitting in the “interesting idea” category. Customers are trying to figure out how to actually use it in their business.
What Questions Are Customers Asking About AI Today
One of the first things I wanted to understand was simple. What are customers actually asking right now?
Not in theory, but in real conversations.
The answer was pretty consistent. Training keeps coming up.
Teams want to know how to use AI in a way that actually helps them do their job better. Not just what buttons to click, but how to think differently about their work.
That’s where the real gap is.
At the same time, many of them are looking at tools they already have. In a lot of cases, that starts with something like Microsoft Copilot. But the conversation isn’t about adding more tools. It’s about figuring out how to make better use of what’s already there.
How do we connect information across systems?
How do we get insights faster?
How do we make better decisions with what we already have?
That’s where customers are looking for help.
Why Customers Feel Uncertain About AI Adoption
What’s interesting is that most customers are not resistant to AI. They’re just unsure where to begin.
They know it matters. They’re experimenting in small ways. But there’s no clear sense of direction.
That’s where a more consultative approach starts to matter.
Before anything gets implemented, there needs to be a clear understanding of how the business is operating today. Where are the inefficiencies? Where is time being lost? What are the priorities?
From there, it becomes much easier to define where AI can actually help.
Without that clarity, it’s very easy to get stuck in experimentation without real progress.
How AI Adoption Should Move from Assessment to Execution
The pattern that’s starting to emerge is consistent.
First, understand where you are today.
Then define where you want to go.
And then figure out how to get there.
It sounds simple, but it’s often skipped.
The most effective way to approach AI is not to start with the technology. It’s to start with the problem. Where is the business struggling? What is taking too much time? Where are resources being stretched?
When you work backward from that, AI starts to make a lot more sense.
It becomes less of a tool and more of a way to rethink how work gets done. In some cases, it helps identify better approaches. In others, it helps execute them faster.
This is where MSPs have a real role to play. Not just in advising, but in actually helping bring those ideas to life.
Where AI Is Delivering Immediate Business Impact
When we talked about impact, a few areas came up right away.
One of them is identity and verification. With everything happening around deepfakes and spoofing, this has become a real concern. Businesses need to be sure that people are who they say they are, both on the customer side and on the support side.
Another area is data protection. This comes up in almost every conversation. People are using AI tools, but they’re also trying to understand what that means for their data. Where is it going? How is it being used? What are the risks?
And then there’s workflow improvement.
Most businesses already know where things are inefficient. The challenge is figuring out how to fix it. AI can help simplify those workflows, reduce manual work, and free up time, but only if it’s applied in the right way.
That’s where the opportunity becomes real.
How AI Agents Fit into Real Business Execution
As the conversation evolves, AI agents naturally come up.
But what stood out is how customers think about them. They’re not asking how to build agents. They’re asking how to solve problems.
That’s an important difference.
Because it shifts the focus away from the technology and back to the outcome.
Most customers don’t want to build anything from scratch. They want something that works. Something that takes a process that’s currently inefficient and makes it better.
That’s where MSPs can step in. Not by explaining how agents work, but by delivering solutions that improve how the business runs.
Why Governance Is Critical for AI Adoption
Another topic that kept coming up was governance.
There’s a lot of noise in the AI space right now. A lot of advice that encourages people to move fast, try everything, and figure it out later.
In a business environment, that approach doesn’t always work.
Without some level of structure, AI can introduce risk just as quickly as it creates value.
That’s why governance matters.
It’s about setting clear expectations. What tools are approved? What data can be used? How should AI be applied?
For MSPs, this is familiar territory. Security and compliance have always been part of the job. The difference now is that those same principles need to be applied to AI.
The Growing Challenge of Shadow AI
Something else that came up is what many are already seeing in their environments.
People are using AI tools on their own.
Downloading applications. Testing things out. Trying to be more productive.
All of that is happening outside of any formal structure.
The challenge is that when something goes wrong, the expectation doesn’t change. The MSP is still expected to fix it.
That’s why taking a proactive approach is important.
Setting guidelines. Educating users. Making sure there’s a shared understanding of how AI should be used.
Without that, it becomes much harder to manage.
What Is the Simplest Way to Start AI Adoption
With all of this, it’s easy to feel like you need a complex plan before getting started.
But one of the most practical suggestions from the conversation was simple.
Start with an acceptable use policy.
Define what’s allowed.
Set clear boundaries.
Give people a starting point.
It doesn’t have to be perfect. It just needs to exist.
From there, it can evolve as the organization learns more.
The key is to get started in a way that is controlled and intentional.
Key Takeaways for the Part 2
- AI is no longer something teams are just experimenting with. The real challenge now is figuring out how to actually make it work inside the business.
- Customers are not looking for another tool to try. They are looking for someone to help them make sense of where to start and what to do next.
- Training is becoming critical, not just to understand AI, but to help teams rethink how they approach their day-to-day work.
- Most businesses already have powerful tools in place. The real opportunity is learning how to use them better, not adding more to the stack.
- The fastest wins with AI are showing up in areas where there is already friction, especially in workflows, security, and verification.
- Governance is starting to come up in almost every conversation. Without clear guardrails, AI can create as many problems as it solves.
- The role of the MSP is shifting. It is less about delivering services and more about helping customers turn AI ideas into something that works.
Closing Thoughts
The most significant thing observed as this conversation progressed has been how rapidly businesses move from talking about AI to putting it into practice.
The other area of note has been that customers are continuing to ask more actionable/implementation-based questions where they want to know how would you implement AI, what is good structure regarding its implementation and so on.
From an MSP perspective, there is an opportunity not only to get these customers ‘on board’ with AI and have them experience its use, but also to assist the customers in getting on board, so that they can experience the benefits of this technology appropriately versus just adding it.
Because AI is not just a tool.
It is becoming a core part of how businesses operate.
And the MSPs who can guide that shift with clarity and responsibility will be the ones leading what comes next.