authentication (MFA) setup, email configuration, and access problem resolution means users experience shorter downtime, which directly feeds into satisfaction scores and overall enterprise efficiency.
MSP Agentic AI: Governance is the real barrier, not technology
One of the more interesting findings is that governance and compliance is the top barrier to implementing agentic AI, cited by 47% of respondents.
Technical expertise gaps accounted for just 16%, with value realization and adoption at 14%, data and security management at 14%, and business integration challenges at 9%.
Think about that for a moment. The technology itself isn’t the problem. The challenge is that organizations don’t yet have the frameworks in place to manage autonomous systems responsibly.
Questions about data access, model transparency, auditability, and when human override should kick in remain unresolved for most.
For an industry that has spent decades building governance structures around ITIL processes and service management frameworks, this should feel like familiar territory. But applying those governance instincts to AI systems that can reason and act independently requires new thinking.
The report recommends establishing governance frameworks early rather than treating them as an afterthought, a sensible approach that too few organizations seem to follow.
MSP Agentic AI: The maturity curve
Omdia outlines a three-stage maturity model for agentic AI adoption: assistive, semi-autonomous, and autonomous.
In the assistive stage, AI classifies issues, recommends actions, and speeds up human-led workflows.
In the semi-autonomous stage, AI executes routine tasks with human approval.
In the autonomous stage, systems coordinate actions across IT, security operations, and cloud operations with minimal supervision.
Most organizations sit firmly in the first stage, with some beginning to move into the second.
The leap to genuine autonomy, where systems make cross-domain decisions and execute end-to-end workflows independently, remains largely aspirational for most.
What’s more useful in practice is the report’s distinction between MSP and internal IT roadmaps.
For MSPs, the starting point is integration: aligning remote monitoring and management (RMM) and professional services automation PSA platforms so that data flows cleanly between monitoring, ticketing, and billing systems.
Without that unified data foundation, agentic AI doesn’t have the context it needs to make reliable decisions.
For internal IT teams, the starting point is an audit: identifying repetitive tasks, recurring incidents, and cross-departmental dependencies that slow resolution times.
Both paths emphasize starting small and building trust.
Pilot programs targeting high-volume workflows such as alert management, ticket triage, and patch automation deliver quick wins that build organizational confidence and justify further investment.
AI maturity and MSP valuations
In my opinion, one finding that deserves more attention than the report gives it is the connection between AI operational maturity and MSP business valuations.
The paper notes that private equity investors and acquirers are beginning to factor AI maturity into their assessments.
For MSPs considering exit strategies or seeking investment, this means delaying agentic AI adoption isn’t just an operational risk; it’s a strategic one as well (and potentially a financial one, too).
The report argues that these differences compound over time, creating a widening gap between organizations that are operationalizing agentic workflows and those that are merely experimenting with them.
Early adopters gain structural cost advantages and faster service delivery, while lagging organizations find it increasingly difficult to catch up.
Reading between the lines
It’s worth noting that this report was commissioned by SuperOps, who naturally have a commercial interest in promoting the adoption of AI-enabled IT management tools.
authentication (MFA) setup, email configuration, and access problem resolution means users experience shorter downtime, which directly feeds into satisfaction scores and overall enterprise efficiency.
MSP Agentic AI: Governance is the real barrier, not technology
One of the more interesting findings is that governance and compliance is the top barrier to implementing agentic AI, cited by 47% of respondents.
Technical expertise gaps accounted for just 16%, with value realization and adoption at 14%, data and security management at 14%, and business integration challenges at 9%.
Think about that for a moment. The technology itself isn’t the problem. The challenge is that organizations don’t yet have the frameworks in place to manage autonomous systems responsibly.
Questions about data access, model transparency, auditability, and when human override should kick in remain unresolved for most.
For an industry that has spent decades building governance structures around ITIL processes and service management frameworks, this should feel like familiar territory. But applying those governance instincts to AI systems that can reason and act independently requires new thinking.
The report recommends establishing governance frameworks early rather than treating them as an afterthought, a sensible approach that too few organizations seem to follow.
MSP Agentic AI: The maturity curve
Omdia outlines a three-stage maturity model for agentic AI adoption: assistive, semi-autonomous, and autonomous.
In the assistive stage, AI classifies issues, recommends actions, and speeds up human-led workflows.
In the semi-autonomous stage, AI executes routine tasks with human approval.
In the autonomous stage, systems coordinate actions across IT, security operations, and cloud operations with minimal supervision.
Most organizations sit firmly in the first stage, with some beginning to move into the second.
The leap to genuine autonomy, where systems make cross-domain decisions and execute end-to-end workflows independently, remains largely aspirational for most.
What’s more useful in practice is the report’s distinction between MSP and internal IT roadmaps.
For MSPs, the starting point is integration: aligning remote monitoring and management (RMM) and professional services automation PSA platforms so that data flows cleanly between monitoring, ticketing, and billing systems.
Without that unified data foundation, agentic AI doesn’t have the context it needs to make reliable decisions.
For internal IT teams, the starting point is an audit: identifying repetitive tasks, recurring incidents, and cross-departmental dependencies that slow resolution times.
Both paths emphasize starting small and building trust.
Pilot programs targeting high-volume workflows such as alert management, ticket triage, and patch automation deliver quick wins that build organizational confidence and justify further investment.
AI maturity and MSP valuations
In my opinion, one finding that deserves more attention than the report gives it is the connection between AI operational maturity and MSP business valuations.
The paper notes that private equity investors and acquirers are beginning to factor AI maturity into their assessments.
For MSPs considering exit strategies or seeking investment, this means delaying agentic AI adoption isn’t just an operational risk; it’s a strategic one as well (and potentially a financial one, too).
The report argues that these differences compound over time, creating a widening gap between organizations that are operationalizing agentic workflows and those that are merely experimenting with them.
Early adopters gain structural cost advantages and faster service delivery, while lagging organizations find it increasingly difficult to catch up.
Reading between the lines
It’s worth noting that this report was commissioned by SuperOps, who naturally have a commercial interest in promoting the adoption of AI-enabled IT management tools.