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AI and automation are the service area managed service providers (MSPs) say clients want most, yet relatively few providers are earning meaningful revenue from it. Meanwhile, large annual customer contracts have become less common, customer acquisition remains a major challenge and hiring skilled technicians is getting harder. Those findings come from Kaseya’s 2026 State of the MSP Report, released April 14, 2026, and based on a survey of more than 1,000 MSPs worldwide.
What Kaseya’s 2026 MSP survey found
The report points to a mismatch between what customers request and what many MSPs can sell profitably: AI and automation are emerging as a leading client need, while the traditional large annual deal is less prevalent.
| Finding | Reported result |
|---|---|
| Typical annual customer spending above $25,000 | Fell from 75% to 41% year over year, according to Kaseya’s 2026 report. |
| Acquiring new customers | 71% of respondents named it their top challenge. |
| AI and automation as a client need | 48% ranked it as the top client need for 2026. |
| Meaningful revenue from AI services | 13% said they were generating meaningful revenue from those services. |
| Internal AI use | 53% said they already used AI to automate ticketing, patching and monitoring. |
| Difficulty hiring skilled technicians | Rose from 9% to 16%. |
These figures describe survey responses, not audited market-wide revenue or a guarantee that AI offerings are profitable. The gap between client interest and meaningful revenue is the central business issue: demand creates an opening, but MSPs still have to define a useful service, deliver it reliably and price it to cover the work.
Why smaller deals make the AI gap more important
When fewer customers spend above $25,000 annually, providers may have less room to absorb labor-intensive delivery within a broad, high-value contract. Smaller contracts can make a modular offer easier to adopt, but they also raise the importance of clear scope and repeatable delivery. An AI feature is not itself proof of customer value; the offer should connect to an outcome the customer can recognize, such as faster response, more useful reporting or earlier detection of a problem.
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Acquisition adds pressure. Kaseya found that 71% named winning new customers as their top challenge. ITPro reports that 33% of new clients were switchers moving from an incumbent MSP, adding a competitive dimension: some opportunities may depend on demonstrating a concrete reason to change providers, rather than simply offering AI capabilities.
How MSPs can turn AI interest into a service
Start with an outcome and a bounded offer
Choose a specific client problem and define what the service will do, what it will not do and how success will be assessed. A smaller, clearly scoped entry point can make the value easier to evaluate than an expansive promise to “add AI.” Potential outcomes include reducing routine ticket handling, improving response times, producing more consistent reporting or surfacing issues proactively. The provider should set expectations appropriate to its actual tools and delivery model.
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Check the delivery economics before selling broadly
Estimate the technician time required to configure, supervise and support the service, then compare it with the recurring revenue and workload it is expected to generate or reduce. Demand figures show interest, not willingness to pay at a particular price or a provider’s margin. Track technician hours saved and service workload alongside client adoption and revenue; otherwise, a growing feature set can conceal a costly implementation.
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Use automation inside the MSP first
Kaseya says 53% of respondents were already using AI to automate ticketing, patching and monitoring. These internal workflows can help providers manage capacity when hiring is difficult, but automation needs oversight: exceptions, errors and security-sensitive changes still require defined handling. Measure whether a workflow actually reduces time or improves service quality before expanding it.
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Fit the offer to the existing service stack
Before committing to a tool or building a package, assess the client outcome it supports, time to deploy, compatibility with the existing PSA and RMM stack, workload coverage, security and data-governance controls, technician hours saved and recurring-revenue potential. These criteria help distinguish a useful operational improvement from an isolated AI feature that adds integration or support burden.
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AI does not replace the core responsibilities customers still depend on MSPs to deliver. Kaseya identifies security and backup/business continuity and disaster recovery (BCDR) as continuing revenue anchors. Providers can position AI-related services alongside those foundations, while making clear how each service is scoped and what operational outcome it supports.
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What the report does—and does not—show
Kaseya’s report is a worldwide survey of more than 1,000 MSPs, released April 14, 2026. Its reported percentages describe respondents; they do not establish that every region, provider size or customer segment faces the same conditions. The results also do not show that AI services will be profitable for every MSP. They indicate a market opportunity alongside a monetization gap, making proof of value and disciplined delivery essential.
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