Jessica Davis: How AI Usage Models Are Disrupting MSP Revenue Predictability
The central structural shift addressed is the fracture of the longstanding per-user, per-month MSP pricing model due to AI-enabled consumption-based (tokenized) billing, which introduces variable costs previously absent from MSP contracts. This shift is being reinforced by vendor strategies from firms such as Microsoft, Atera, ConnectWise, N-able, and Pax8, each proposing different mechanisms for channel partners to integrate and manage AI costs and capabilities. Recent research from Omnia, highlighted by Jessica Davis, underscores the pace and fragmentation of this evolution, creating new exposure for MSPs to vendor-driven pricing and value capture.
Data from an Omnia poll of 255 MSPs found 40% are maintaining traditional per-user pricing, while 60% are reevaluating or transitioning toward hybrid, outcome-based, or true consumption models. Business of Tech research shows that two-thirds of MSPs have not referenced AI at all in their customer-facing positioning, and those that do overwhelmingly reference Microsoft as their AI provider. According to Jessica Davis, much of the 40% maintaining legacy pricing may not be doing so out of clear strategy or discipline, but because they have yet to encounter the practical or financial impacts of AI usage patterns.
Secondary developments discussed include vendor-driven channel consolidation in the form of proprietary control planes: Kaseya, ConnectWise, N-able, and Pax8 are all positioning their platforms as the central operational layer for AI services, but with divergent models—ranging from bundled internal use to open orchestration. Dave Sobel and Jessica Davis note that this fragmentation and experimentation by vendors creates substantial complexity for MSPs, who face real risk of shifting from managed service models to a lower-margin reseller role, particularly as vendors seek to capture value through consumption pricing. Additionally, the rapid pace of AI tool development is enabling some MSPs, particularly advanced or less-regulated firms, to bypass vendors and build custom integrations or internal automations.
For operators, the practical implications are increased operational risk and pricing uncertainty, coupled with the challenge of balancing internal efficiency gains against eventual client demand for AI-driven services. Vendor dependency is deepening as MSPs must choose whether to commit to a control plane and cede elements of value and data custody, or attempt to differentiate through custom service layers. The most immediate risk is margin compression from ill-managed or misaligned pricing models—a threat compounded if MSPs fail to map their AI cost and value flows. According to Jessica Davis, MSPs who closely monitor their actual AI-related costs and value delivered, rather than reacting prematurely or simply holding the line, will be better positioned to adapt to ongoing changes in both technology and vendor strategy.
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