Managed service providers (MSPs) face a widening gap between what customers want and what providers are yet earning from it: Kaseya’s 2026 survey says AI and automation are clients’ top need, but only a small share of MSPs report meaningful revenue from those services. At the same time, fewer respondents report customers spending more than $25,000 a year, making efficient delivery and clearly packaged services more important.
What Kaseya’s 2026 survey found
Kaseya released its 2026 State of the MSP Report on April 14, 2026, drawing on responses from more than 1,000 MSPs worldwide. The results point to simultaneous pressure on sales, contract size and staffing—not simply a technology trend.
| Finding | What respondents reported |
|---|---|
| Customer spending | The share reporting typical annual customer spending above $25,000 fell from 75% to 41% year over year, according to Kaseya’s 2026 report. |
| Customer acquisition | 71% named acquiring new customers as their top challenge. |
| AI demand and revenue | 48% ranked AI and automation as clients’ top need for 2026, while 13% said they were generating meaningful revenue from those services. |
| Internal AI use | 53% said they were already using AI to automate ticketing, patching and monitoring. |
| Hiring | The share reporting difficulty hiring skilled technicians rose from 9% to 16% year over year. |
These are survey findings, not a census of every MSP or a measure of total industry revenue. The figures describe what respondents reported; they do not establish that AI caused smaller contracts, hiring problems or difficulty winning customers.
Why smaller contracts make the AI gap more important
A customer asking for AI-enabled service is not automatically a customer willing to pay enough to make that service profitable. The gap between 48% identifying AI and automation as the leading client need and 13% reporting meaningful revenue suggests that interest has not translated into substantial monetization for most respondents.
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Smaller annual customer spending can make labor-intensive service delivery harder to support. For an MSP, the practical question is whether an AI offering produces a customer-visible result while keeping the work required to deliver it within the contract’s economics. Packaging a defined outcome—such as faster response or automated reporting—can make value easier to explain than selling a list of AI features. Modular entry points may also suit customers who are not ready for a large annual commitment.
How AI can help MSPs address capacity constraints
AI and automation have two roles in the survey findings: a service customers want and a way to increase internal capacity. More than half of respondents said they already use AI to automate ticketing, patching and monitoring. Those operational uses may help a provider handle work with existing staff, but the survey figures do not quantify hours saved or prove that every implementation improves margins.
MSPs evaluating automation should measure operational outcomes alongside sales:
- Track technician time spent on covered tasks before and after deployment.
- Measure changes in ticket handling time, repeat work and workload volume, using consistent definitions.
- Check how well a tool fits the provider’s existing professional services automation (PSA) and remote monitoring and management (RMM) systems.
- Assess which workloads it actually covers, how long deployment takes, and what security and data-governance controls apply.
- Compare the customer outcome and recurring-revenue potential with the ongoing labor and support required.
A count of deployed AI features is not a substitute for evidence that technicians gained capacity or customers received a measurable benefit.
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Winning customers in a more competitive market
Kaseya’s finding that 71% of respondents named customer acquisition as their top challenge puts the AI opportunity in a difficult sales context. ITPro reports that 33% of new clients were switchers moving from an incumbent MSP, indicating that some new business involves winning customers away from existing providers. That adds competitive context, but does not establish why those customers switched or describe all new-client activity.
For an MSP pursuing a switcher, a specific service outcome and a credible transition plan may be more persuasive than a broad promise to use AI. Providers can distinguish an AI-enabled offer by explaining which tasks it changes, what customers should expect to improve and how results will be assessed. The evidence in Kaseya’s survey does not identify a single sales approach as the cause of success.
Keep security and continuity services in view
Kaseya identifies security and backup, disaster recovery and business continuity (BCDR) as continuing revenue anchors. That is a reminder not to treat AI as the only growth opportunity. MSPs can consider how AI-related work fits alongside established security and recovery services, while keeping each offer tied to a defined customer need and delivery model.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the report does—and does not—show
The report provides a timely snapshot of more than 1,000 MSPs worldwide, including reported customer-spending patterns, business challenges and AI adoption. It does not, from the figures summarized here, establish the size of the overall MSP market, explain why spending above $25,000 declined, or demonstrate that AI services are profitable. Nor does it show that the same conditions apply equally across regions, provider sizes or customer segments.
The clearest business implication is therefore a testable one: make AI offers specific enough for customers to value, and use internal automation where it can demonstrably reduce workload or expand technician capacity. Demand is a signal to develop and validate services—not proof of revenue or return on investment.
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