The managed services industry is entering one of its most significant transformative periods. And not because of AI, but because of what sits beneath it: data architecture.
At Kaseya Connect 2026, CEO Rania Succar and CTO Pratik Wadher spoke with ChannelPro to outline their vision for the future — one where AI doesn’t just assist MSPs, it acts on their behalf. In that future, MSP leaders will be free to focus on what actually drives value: understanding their clients’ needs and mapping them to technology-supported outcomes.
The era of agentic AI is emerging. But whether you’re a multibillion-dollar powerhouse like Kaseya or a small MSP looking to deploy its own AI agents, the challenges are the same. AI developers must apply human intelligence to create the conditions for AI to thrive. And all that depends on structured and connected data.
No one gets excited about data architecture. But MSPs that want to scale cannot afford to ignore it.
Fragmentation is the Real Problem
For years, MSPs have operated with disconnected tools, duplicated tickets, and siloed data. That fragmentation is evident even inside large vendors’ ecosystems.
“What I noticed was that, even within Kaseya, it was taking a lot of time to stitch these products together. Customers were doing it on their own, using APIs to stitch the products and data together,” Wadher said. And the effort it took to get tools working together in real-time drove partner frustration.
“I get customers coming to me saying, we have the same ticket surfacing in so many products. Whether using Vulscan, Autotask, or RMM,” he shared.
That bottleneck prevents meaningful AI adoption. Without unified data, even the most advanced AI models struggle to produce meaningful outcomes.
Succar echoed that by saying, “We want to bring it all together into one platform. That concept is something we want to keep leaning into.”
AI Strategies Fail When Data is a Mess
Many organizations approach AI by dumping data into centralized systems and hoping for insights. Kaseya is deliberately avoiding that path.
“It’ll be very easy to say, let’s just start putting all this data in one place… and then we end up with a very, very dirty data lake,” Wadher said.
Kaseya’s answer is what Wadher calls the “MSP graph,” a unified data model that connects the entire MSP lifecycle.
The company is grounding its AI strategy in a set of foundational principles:
- Data is well-structured from the start
- Specific use cases (like ticket triage) are identified
- Context flows across systems
- Insights trigger actions
- Guardrails allow human oversight
Kaseya’s methodology is one that individual MSPs can imitate as they begin building their own AI strategies. But successful deployments will require well-structured and connected data. That’s what separates AI transformation from failed experimentation.
Rania Succar speaking on AI for MSP automation
AI That Learns From Everyone
One advantage that platform vendors like Kaseya have is the sheer magnitude of the data. Kaseya’s partners get the benefit of “three exabytes of backup data, a billion help desk tickets, and data from 17 million endpoints,” according to Succar.
The result is smarter automation that is learning from everyone’s best practices, not just the individual MSP. For example, in ticket triage, Kaseya found that a majority of the tickets had a lot of missing data.
But by leveraging broader ecosystem data, “We’re able to actually classify it much faster and then apply the classification to the MSP data,” Wadher said.
But the data volume advantage isn’t as insurmountable as some might think. The key is prioritizing end-to-end automation, handling missing or incomplete data intelligently, and connecting systems so context flows between them.
Ultimately, success comes from optimizing for real-world results.
“At the end of the day, AI is an enabler. MSPs still have to focus on the same customer benefit outcomes they always have,” Succar said.
Shift to Autonomous IT is a Multistep Process
Kaseya’s move into agentic AI is slow and deliberate on purpose, but the end goal is clear: autonomous IT and security. Succar described it as an S-curve evolution.
“We don’t think this is overnight. It’s going to take time to get there,” Succar said.
The roadmap she described in her keynote was multistep. First, businesses like Kaseya will work to eliminate routine work with automation. In the coming years, MSP technology will be built with AI at its core, rather than being bolted on as an extra feature. Human capabilities will be augmented. Eventually, a large portion of reactive managed services will be handled by machines, not people.
“Eventually, there will be some components that we can make totally autonomous,” she shared.
The End of Traditional Managed Services
One of the biggest fears in the industry is whether AI will make MSPs obsolete. Both executives rejected that idea but explained how managed IT services have to change to embrace the agentic AI era.
“MSPs have to do the work to clear out all the routine work to create space to partner with SMBs on the next thing,” Succar said.
A large percentage of an MSP’s time today is spent on ticket resolution and reactive support. The future is focused on AI advisory services, business transformation, and building technology-focused strategic partnerships.
“The top MSPs are in transformation mode right now. It’s not business as usual,” she noted.
She went on to highlight the shift from activity-based metrics to impact-based metrics. According to Succar, MSPs will be far more profitable if they focus on KPIs like:
- Mean Time To Resolution (MTTR)
- Ticket volume per employee
- Security incident frequency
- Time to recovery
- Backup success rates
Pratik Wadhar, first from left
From Platform to Ecosystem: The Rise of the MSP AI Marketplace
Perhaps the most forward-looking element of Kaseya’s strategy is an open marketplace it wants to build. At the center of that vision, according to Wadher, is the MSP graph. If it succeeds in developing a shared data model, it creates the conditions for a broader ecosystem where:
- A common industry framework defines how data is structured
- Vendors and developers build on top of that standard
- MSPs choose from competing AI-driven tools and agents
As Wadher puts it, “I could have a password reset agent done by multiple parties. Then you could pick and choose which one you’d want to use.”
This signals a move toward a platform economy, where value is created not just by the core vendor but by a network of contributors building interoperable solutions. Instead of locking MSPs into a single approach, the model encourages flexibility, specialization, and competition at the application layer.
Summing Things Up
The biggest misconception in the MSP industry today is that AI is the differentiator. But it’s only the visible piece of the puzzle. The companies that win will be the ones that unify their data, structure it correctly, and build systems that learn continuously.
Kaseya is hoping to set the standard by which other AI developers in the channel will be judged.
“Our number one focus is developing the technology to unleash MSPs,” Succar said.
The winners won’t be those who deploy the most tools. They’ll be the ones who build the strongest foundations and, to paraphrase Succar, embrace the fear of change and do the hard work to get to the next stage of transformation.
“We need to embrace the potential and then be confident in leading the way in this moment,” she said.
Jonathan Browning is executive director of content and engagement for The ChannelPro Network. He has been a leader in the IT channel for close to a decade. He’s an avid fan and early adopter of technology and believes that the Managed Services industry is the most important driver of economic growth and human innovation.
Images: Jonathan Browning
