AI is one of the biggest opportunities facing MSPs and IT Service Providers, but for many, turning that opportunity into revenue is still proving difficult.
Customers are interested in AI, and vendors are releasing new capabilities at a rapid pace. Microsoft continues to embed Copilot across its ecosystem, while automation platforms are making it easier to connect systems, remove manual work and improve business processes.
Yet many MSPs are still asking the same question:
What exactly do we sell?
The challenge is that “AI” by itself is not a service. Customers do not necessarily want an AI strategy, an AI workshop or another technology conversation. What they want is a better way to run their business.
They want to save time, reduce administrative effort, improve customer experience, remove repetitive tasks and create more consistent processes.
That means the opportunity for MSPs is not simply to sell AI. It is to identify common business problems and use AI and automation to solve them in a repeatable way.
A practical AI go-to-market motion can be built around five simple steps.
Step 1: Become Your Own First Customer
The best place to start selling AI is inside your own MSP.
Before asking customers to invest in AI and automation, you should be able to demonstrate how you are using it to improve your own business.
There is a credibility gap when an IT provider talks about the transformative potential of AI but cannot point to any meaningful examples within its own operations. It is not dissimilar to visiting a mechanic who never services their own car or a plumber who ignores the leaking pipes in their own house.
MSPs have no shortage of internal processes that are ripe for improvement. Service ticket triage, customer onboarding, meeting summaries, documentation, sales administration, quoting, procurement, reporting and knowledge management are all areas where repetitive work can often be reduced.
The goal does not need to be some groundbreaking AI application. Start with a process that happens frequently, consumes time and can be improved. For example, you might build a workflow that takes the notes from a customer meeting, summarises the key points, generates action items, updates the PSA and drafts the follow-up email.
Individually, each of these activities may only take a few minutes. Across hundreds of meetings each year, however, the efficiency gain can become significant.
More importantly, you now have something tangible to demonstrate to customers.
Instead of saying, “AI could improve your business,” you can say, “We automated this process internally; it is saving our team hours every week, and we can show you exactly how it works."
That creates a very different sales conversation.
Step 2: Pick an Industry Vertical
Once you have started building your own internal capability, the next step is to decide where you want to take it to market. Trying to sell AI solutions to every type of business is likely to make your proposition too broad.
A law firm, accounting practice, construction company and financial services business may all use Microsoft 365, but the way they operate is very different. Their workflows, terminology, compliance requirements and customer journeys are unique to their industries.
This is why choosing an industry vertical is so important.
The best place to start is often within your existing customer base. If you already support multiple accounting practices, law firms or construction companies, you have something extremely valuable: domain knowledge.
You already understand the systems those customers use. You understand their busy periods, the types of requests they make, the administrative challenges their teams face and the processes that continually cause frustration.
That knowledge allows you to move away from a generic message such as, “We can help your business use AI,” and towards a much stronger proposition.
For example:
“We help accounting firms automate client onboarding, document handling and recurring administrative processes.”
That immediately sounds more relevant because it demonstrates an understanding of the customer’s business rather than just the technology.
Step 3: Understand the Common Problems
Once you have selected a vertical, your focus should shift away from the technology and towards the problems businesses in that sector are trying to solve.
This is where many MSPs can gain a significant advantage, as they already have trusted customer relationships; it is just a matter of using them.
Talk to business owners, operations teams, finance teams, administration staff and customer-facing employees. Ask them where time is being wasted and find out which tasks are repetitive. Look for processes where information is manually copied between systems or where staff are still relying heavily on spreadsheets, email and individual knowledge.
The objective is to understand where friction exists.
You may discover that accounting firms repeatedly struggle with gathering information from new clients. A law firm may spend large amounts of time reviewing and summarising documents. A construction business may manually move information between quoting, project management and finance platforms.
The important thing is to recognise that these are not really technology problems. They are business-process problems, and AI and automation simply provide new tools for solving them.
This distinction changes how you sell.
Customers are far more likely to invest in reducing ten hours of administration every week than they are to invest in “an AI solution” without a clearly defined business outcome.
The strongest opportunities will usually be the problems that occur repeatedly across multiple customers. That is when you know you are starting to identify something that can become scalable.
Step 4: Create Solution Accelerators
When you find the same problem appearing across multiple businesses, avoid treating every engagement as a completely custom project. This is where Solution Accelerators become important.
A Solution Accelerator is a repeatable framework that solves a common problem while still allowing some level of customisation for each customer.
For example, imagine you identify that client onboarding is a common challenge across accounting firms.
You could develop a standard onboarding accelerator that captures client information, creates records across the required systems, generates task lists, creates folders, sends documentation, assigns responsibilities and provides automated updates.
The first version may take considerable effort to develop; however, the second deployment should be easier. By the fifth or tenth deployment, you should have refined the architecture, documentation, integrations and implementation process to the point where the solution can be delivered much more efficiently.
This is where the economics begin to change. Rather than selling engineering hours every time, you start building reusable intellectual property.
Your Power Automate workflows, Power Apps, Copilot Studio agents, APIs, prompts, documentation and deployment processes become assets that can be reused across multiple customers.
That repeatability is what turns AI from an interesting consulting exercise into a genuine service offering.
Step 5: Productise the Outcome
The final step is turning the Solution Accelerator into something that your sales team can confidently take to market.
The key here is to sell the outcome rather than the underlying technology.
Most customers do not care whether the solution uses Azure OpenAI, Power Automate, Copilot Studio, n8n or another automation platform. They care about what it does for their business.
Instead of selling a “Power Platform Automation Project", create an offer such as an “Accounting Client Onboarding Accelerator".
That proposition is much easier for a customer to understand. You can clearly articulate the problem being solved, the outcome they can expect and how the engagement works.
The initial deployment may be a project, but there is also a natural recurring revenue opportunity. Once you are responsible for business-critical automations and AI workflows, those solutions need to be monitored, maintained, improved and supported. Integrations can break. Processes can change. AI models evolve. Customers will identify additional opportunities.
This creates the potential for an ongoing managed automation or AI service where the MSP continues to optimise and expand the customer’s environment. That is where the real commercial opportunity starts to emerge.
Start With Problems, Not AI
The MSPs that successfully commercialise AI will not necessarily be the businesses with the most sophisticated AI strategy. They will be the ones that become very good at identifying common business problems and developing repeatable solutions around them.
The motion is relatively straightforward:
Use AI internally. Choose a vertical. Understand the problems. Build repeatable solution accelerators. Productise the outcome.
Then repeat the process.
Start with something small inside your own business and measure the impact it creates.
Take that example to customers in a vertical you understand and look for organisations experiencing the same problem.
Once you see the pattern, build a reusable solution rather than another one-off project.
Over time, the objective should be to create a library of proven solution accelerators that your team can deploy across multiple customers.
That is how an MSP begins to move from selling technology to solving business problems. And it is how AI moves from being another conversation in a customer meeting to becoming a scalable and repeatable revenue stream.
What Should Your MSP Build First?
If your MSP is still trying to work out how to commercialise AI, the answer may not be another sales campaign. Start by looking inside your own business. Identify a process that is manual, repetitive or inefficient and ask yourself:
If we have this problem, how many of our customers probably have it too?
That problem could become your first solution accelerator.
And that Solution Accelerator could become the foundation of your AI go-to-market motion.
Your first scalable AI offering may already be hiding in a process your team solves every day, Let’s start a conversation about where your MSP’s first scalable AI opportunity could be.