The MSP Agentic AI Execution Gap in Service Delivery
By Sophie Danby
Seven out of ten managed service providers (MSPs) say they’re using agentic AI. But dig into where they’re actually using it, and the MSP agentic AI adoption picture is far less impressive. Only 10% have primarily deployed it in IT service desk or security and compliance operations. The rest are tinkering internally while their customers, in many cases, are already further ahead.
That’s the headline finding from recent data published in “The Autonomy Advantage,” a white paper from Omdia (commissioned by SuperOps) that draws on multiple polls of MSPs and IT decision-makers conducted through the Candefero and Canalys databases in mid-to-late 2025 to better understand MSP agentic AI adoption.
MSP Agentic AI: The gap between experimentation and execution
In my view, the report’s headline claim that agentic AI will be the biggest operating model shift since cloud isn’t really the story here. It’s about where agentic AI is and isn’t being used.
As already mentioned, according to Omdia’s data, 70% of MSPs report using agentic AI internally.
This sounds impressive until you dig into where: only 10% have primarily extended agentic AI to their IT service desk or to security and compliance operations.
Meanwhile, 30% of MSPs report no use of agentic AI at all.
That’s a significant execution gap. Most MSPs appear to be in a holding pattern: aware of the technology, possibly experimenting with it, but not deploying it in the workflows that would benefit most.
The report calls this a divide between “the agentic AI vanguard and the agentic AI delayers,” which is a bit buzzwordy for my taste, and it misses the larger middle group. A large number of organizations are stuck in experimentation without a clear path to production use.
On the end-user side, the picture is slightly more advanced. Omdia found that 13% of IT organizations’ customers are primarily using agentic AI for customer experience, another 13% for productivity, and 9% for business process automation. Only 17% aren’t using it at all.
So customers are arguably moving faster than the MSPs serving them, which creates its own set of pressures.
The cost argument is getting harder to ignore
Omdia’s modelling suggests that enterprise deployments of agentic AI for employee support (including IT services) could deliver annual cost savings of $250–1,200 per employee.
That’s a wide range, and the report doesn’t break down exactly what drives the variation. Still, even at the lower end, the savings add up quickly across a large workforce.
The cost case is most obvious for MSPs, where Omdia estimates around 40% of costs are spent on labor.
The report points to Level 1 ticket resolution as the obvious starting point: password resets, basic software installations, account unlocks, and printer issues.
These are the repetitive, manual tasks that eat up technician time without moving the needle, and they’re precisely the kind of work that agentic AI can handle autonomously or semi-autonomously.
For internal IT teams, the main benefit is faster ticket resolution and the ability to offset headcount constraints.
Automating components of multi-factor
The MSP Agentic AI Execution Gap in Service Delivery
By Sophie Danby
Seven out of ten managed service providers (MSPs) say they’re using agentic AI. But dig into where they’re actually using it, and the MSP agentic AI adoption picture is far less impressive. Only 10% have primarily deployed it in IT service desk or security and compliance operations. The rest are tinkering internally while their customers, in many cases, are already further ahead.
That’s the headline finding from recent data published in “The Autonomy Advantage,” a white paper from Omdia (commissioned by SuperOps) that draws on multiple polls of MSPs and IT decision-makers conducted through the Candefero and Canalys databases in mid-to-late 2025 to better understand MSP agentic AI adoption.
MSP Agentic AI: The gap between experimentation and execution
In my view, the report’s headline claim that agentic AI will be the biggest operating model shift since cloud isn’t really the story here. It’s about where agentic AI is and isn’t being used.
As already mentioned, according to Omdia’s data, 70% of MSPs report using agentic AI internally.
This sounds impressive until you dig into where: only 10% have primarily extended agentic AI to their IT service desk or to security and compliance operations.
Meanwhile, 30% of MSPs report no use of agentic AI at all.
That’s a significant execution gap. Most MSPs appear to be in a holding pattern: aware of the technology, possibly experimenting with it, but not deploying it in the workflows that would benefit most.
The report calls this a divide between “the agentic AI vanguard and the agentic AI delayers,” which is a bit buzzwordy for my taste, and it misses the larger middle group. A large number of organizations are stuck in experimentation without a clear path to production use.
On the end-user side, the picture is slightly more advanced. Omdia found that 13% of IT organizations’ customers are primarily using agentic AI for customer experience, another 13% for productivity, and 9% for business process automation. Only 17% aren’t using it at all.
So customers are arguably moving faster than the MSPs serving them, which creates its own set of pressures.
The cost argument is getting harder to ignore
Omdia’s modelling suggests that enterprise deployments of agentic AI for employee support (including IT services) could deliver annual cost savings of $250–1,200 per employee.
That’s a wide range, and the report doesn’t break down exactly what drives the variation. Still, even at the lower end, the savings add up quickly across a large workforce.
The cost case is most obvious for MSPs, where Omdia estimates around 40% of costs are spent on labor.
The report points to Level 1 ticket resolution as the obvious starting point: password resets, basic software installations, account unlocks, and printer issues.
These are the repetitive, manual tasks that eat up technician time without moving the needle, and they’re precisely the kind of work that agentic AI can handle autonomously or semi-autonomously.
For internal IT teams, the main benefit is faster ticket resolution and the ability to offset headcount constraints.
Automating components of multi-factor