AI is no longer an experimental add-on for managed service providers. In 2026, it’s becoming a baseline expectation for small and midsize businesses looking to scale, improve efficiency, and stay competitive.
For MSPs, that shift creates a clear opportunity, and a growing challenge. Many providers understand AI’s potential but still struggle to turn it into a repeatable, defensible offering that delivers measurable value for SMB clients rather than just internal experimentation.
This guide outlines a practical framework for building an AI strategy tailored to SMB customers, helping MSPs move past uncertainty and position AI as a secure, revenue-driving service—not a source of risk or hype.
What your SMB clients already know about AI
Before you build the full strategy, let’s start by zeroing in on what SMBs already understand about AI. This gives you a clear runway for what to prioritize in your AI offering.
AI is a growth requirement for SMBs
With AI now so widespread, most businesses understand it’s shifted from a novelty to a practical way to drive growth. They know it can improve productivity and overall efficiency. They also know competitors are adopting it, which increases pressure on them to do the same.
AI can help teams move faster, with fewer resources
SMB owners already see AI as a way to get more done without adding headcount. It can automate repetitive tasks, handle time-consuming legwork fast, and augment human capacity across workflows — all at a lower cost.
AI use is already happening without clear visibility or guardrails
Most businesses know their teams are already using AI, whether it’s officially approved or not. The real challenge is enabling that use without compromising data security, confidentiality, or compliance.
Most SMBs still don’t know where AI truly fits
Even with this understanding, many SMB customers are still figuring out where AI fits in their business. That means they need help identifying which workflows are good candidates for AI and whether the technology will improve efficiency or just create more rework for teams.
With that in mind, here are five steps to build a meaningful AI offering for SMB clients.
5 Steps to Building an AI Offering for SMB Clients
1. Look to infuse AI throughout your current offerings
A strong way to start shaping your AI offering is to look for places in your existing services that can be “AI optimized.” Start with what you already deliver today, then identify where AI can enhance outcomes or improve speed and consistency for clients.
The good news is the market is packed with AI-first vendors across nearly every category. For MSPs, that means there’s no shortage of options to layer AI into your portfolio.
2. Consider common verticals or business types you already serve
Next, look at the industries you serve most often and use that as a guide for what effective AI adoption should look like.
If your SMB clients are in regulated verticals like financial services or legal, prioritize AI capabilities that enhance data security, confidentiality, and compliance.
If you support HR, healthcare, or customer-service-heavy businesses, you can look into AI that reduces response times and improves customer experience.
3. Understand SMB business pain points, not just technical needs
After you’ve looked at your current offerings and client mix, the next step is to be intentional about where AI fits and what business problem it solves, not just the technical implementation.
In theory, AI can touch every part of an SMB. In practice, it should map to clear business goals that translate into measurable ROI or address meaningful pain points the business is actually trying to solve.
Are SMB customers trying to scale output and production to drive revenue growth? Or are they focused on making sure AI use is responsible, secure, and consistent across the organization?
Focusing on the specific business goals your clients are trying to achieve helps narrow down which AI offerings will actually serve them best.
4. Work with your vendors to determine best practices
Once you have a clearer idea of which AI capabilities you want to offer, make time to meet with your vendors and hash out best practices.
You can do plenty of research on your own, but the fastest way to understand how to deploy an AI solution well is to hear directly from the provider responsible for it.
Use those conversations to get specific:
- How to roll out the service
- What requirements and limitations should be planned for
- How to tailor features by customer type
- What it takes to maintain reliability and consistent results over time
It’s also worth comparing notes with other MSPs who’ve gone down a similar path, as this could save you a lot of time and trial-and-error with the whole process.
5. Establish clear expectations and potential ROI early
Once your AI offerings are defined, the final step is to set clear expectations and outline potential ROI from the start. This helps manage assumptions on both sides and gives you concrete metrics to measure success over time.
While there’s no shortage of hype around AI investment, SMBs are increasingly focused on what it actually delivers. Being upfront about expected outcomes and business impact gives your AI offering more credibility and an edge over competitors.
Why you need to bolster security services alongside AI in your portfolio
Alongside these five steps, it’s just as important to strengthen your security offerings as you roll out AI services. AI can deliver real productivity gains, but it also introduces new risks that can weaken an SMB’s security posture if they’re not addressed up front.
In particular, AI adoption can accelerate:
- Shadow AI – As employees use unmanaged AI tools outside approved workflows
- Data leakage – When sensitive information is pasted into chatbots or AI assistants without realizing the risk
- AI sprawl – As AI features get embedded into tools already in the tech stack, often without clear ownership or oversight
Bolstering your security services alongside your new AI offerings will help you deliver a more complete and defensible product to clients, so they don’t have to choose between innovation and security.
Best Practices for MSPs in the Age of AI
To navigate AI without getting lost in the hype, we advise anchoring your approach on the following recommendations:
Continuously educate yourself and your team on the latest trends, developments, and advancements in AI. As an MSP, it’s your responsibility to stay ahead of the curve so you’re ready with answers when your clients come to you with questions.
Start small, then iterate
Rather than attempting to tackle large problems, identify the use cases for your AI project by starting with small-scale pilots and proof-of-concept initiatives.
This allows you to iterate, learn, and refine your approach before expanding to higher-stakes initiatives.
Lean on strategic partnerships
Don’t try to build everything alone. Foster strong vendor relationships as they help you move faster, avoid dead ends, and stay aligned with AI best practices.
Ensure data hygiene and governance
High-quality data is essential for AI applications to deliver accurate and reliable results. Implement robust data hygiene processes, ensure data governance and regulatory compliance, and establish mechanisms for data security and privacy protection. This includes developing ethical and responsible governance for AI use.
Focus on delivering customer value
Focus on understanding customer needs and preferences, tailoring AI solutions to address specific pain points and challenges, and continuously seeking feedback to refine and improve their offerings.
Establish clear KPIs and metrics to measure the effectiveness and impact of your AI initiatives, and ensure you receive regular feedback from your clients and partners.
Avoid common AI pitfalls
While there is no one-size-fits-all approach to successfully offering AI in your portfolio, there are common pitfalls you’ll want to avoid to ensure successful integration for your customers:
- Failing to define clear objectives and use cases for AI initiatives upfront
- Underestimating the implementation challenges of AI solutions results in delays, cost overruns, and suboptimal outcomes
- Poor integration with existing systems can disrupt operations and impede the scalability of the AI solution
- Failing to gather and incorporate user feedback into AI systems, particularly around ROI and ease of use
- Neglecting security and privacy considerations can expose sensitive data to breaches and unauthorized access
Bottom line: Unlocking the power of AI, from fear to growth
Incorporating AI into your MSP offerings is a meaningful undertaking that requires strategic planning and disciplined execution.
Even if the technology still feels somewhat unknown at times, AI integration is a real opportunity to modernize your portfolio, stay competitive, and meet client expectations.
If implemented deliberately, AI services can become a new driver of growth. With clear goals, the right vendors and stakeholders, and a focus on high-impact ROI, you can shift AI from a source of uncertainty into a repeatable engine for client value and MSP expansion.
This article was originally published by Pamela Winikoff in February 2024. It was substantially updated by Luis Millares in February 2026, refocusing the piece from helping MSPs build an internal AI strategy to helping MSPs bring AI capabilities to their SMB customers.
