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The Future Of Automation For MSPs

A few weeks ago, I had the chance to attend a dinner where Lee Silverstone, CEO & Co-Founder of ZofiQ, gave a talk on AI. One of the most memorable moments was his reference to an article by Kimberly Tan of a16z titled “RIP to RPA.” That headline hit me hard, and for good reason. As Lee unpacked it; the age of static, rule-based automation is over, and AI is completely rewriting the playbook.

That dinner led to a series of great follow-up conversations about what the death of robotic process automation could mean for MSPs in particular and how they will automate their businesses going forward. From the obvious limitations of static workflows to the lofty promise of agentic AI, Lee makes a strong case for why traditional automation tools have failed the MSP market and how AI is finally poised to deliver on automation’s long-overdue potential.

RPA vs. AI: Understanding the Difference

When it comes to automation, not all tools are created equal. As Silverstone puts it, “The core difference is that with RPA, you’re building deterministic workflows manually. Every single step has to be defined. But AI, especially agentic AI, can observe and learn from how tasks are done and begin to build those workflows itself. That’s a huge unlock. You don’t need to predefine every single action anymore.”

That flexibility matters a lot more than it might sound. Traditional RPA systems struggle in MSP environments because the processes are both sophisticated and dynamic. Things shift, exceptions occur, and edge cases are the norm. AI, on the other hand, thrives in that type of environment. It doesn’t need rules, as it can reason and make decisions in real-time. 

This is why Lee believes that; “RPA has failed most people. It’s failed to deliver on its promises of automation and ROI, especially for MSPs, because their workflows are so complex. As soon as the process hits a bottleneck, it breaks. AI, on the other hand, doesn’t require people to build out every step. It can infer what the workflow should be by learning from input and output data.”

The RPA Dilemma for MSPs

According to Lee, the problem isn’t just that RPA underdelivers, it’s that it was never designed for the kind of dynamic environments MSPs operate in. “With RPA, the automation only works if the world around it stays the same. But AI doesn’t have that same rigidness. It adapts. You can poke it, prod it, talk to it, and it will respond. That flexibility is what makes it viable for the type of real-world complexity MSPs deal with.”

While some MSPs have been building these RPA-based automation workflows themselves, others rely on product vendors who are now scrambling to adapt. As Silverstone puts it, “A lot of these RPA vendors have tried to jam AI into their platforms after the fact, like it’s an add-on. But it’s like putting a motor on a horse-drawn carriage. At the end of the day, it’s still a carriage and not a car. You need to rethink the design from the ground up if you’re building around AI.” This is exactly why being an RPA automation product during the dawn of the AI era is not the head start you’d think it would be.

The Second-Order Effects of AI

Even if AI weren’t directly replacing RPA, it would still render it obsolete by speeding up the very environments RPA depends on. “With the pace that AI is enabling (faster software development cycles, quicker releases) RPA just can’t keep up. If a button in the UI shifts slightly, the automation breaks. And now that those changes are happening every two weeks instead of every two months, that breakage is constant.”

The problem here is foundational. RPA depends on static conditions: a button has to live in the same 50-pixel box, the menu must open the same way, and the layout must remain unchanged. But AI-powered development tools are accelerating product cycles to such a degree that this inflexibility just can’t keep up. Silverstone reflects, “I’ve been in tech my whole career, and I’ve never seen things move this fast. Companies are shipping product faster, market share is shifting faster, and new tools are emerging every day. RPA just wasn’t built for this level of velocity. It’s fundamentally too brittle.”

Lessons From Tesla And FSD

As we were discussing the differences between RPA and AI, one of the things that came to mind was Tesla’s Full Self-Driving system. When Tesla released FSD v12, they made a radical shift from a traditional, rules-based system to one powered by a large language model. Instead of relying on thousands of lines of code to dictate behavior, the new version is trained on billions of video frames from real-world driving incidents. Put simply; it’s not programmed, it’s taught. And that shift perfectly mirrors what’s happening with AI in workflow automation for MSPs.

Lee went on to say, “I like the analogy of the self-driving car. Just like in Tesla’s FSD, you’re moving from a system that says ‘when you see a stop sign, do this,’ to something that thinks like a human and makes dynamic decisions. That’s a good way to understand how AI differs from RPA. It uses judgment and adaptability. It’s not just following a static script.” 

And just like in the case of autonomous vehicles, great AI automation shouldn’t eliminate humans, it should augment them. “There’s still someone sitting behind the wheel in most self-driving cars, and I think that’s true here too. Even with agentic AI, you still want oversight. But the more you combine the freedom of AI with the structure of workflows, the more powerful the outcome. Not one or the other, but both working in unison.”

Rethinking PSA Workflows & Customization

One of the biggest automation challenges that MSPs face is just how much manual customization their PSA requires. It’s not uncommon for MSPs to have a full-time employee dedicated solely to building workflows and managing PSA adoption internally. Over time, these platforms have ballooned in complexity, trying to satisfy every possible variety of MSP that exists in the market.

I asked Lee whether AI might finally be the key to removing that barrier and fixing the problem that nearly every PSA product eventually runs into. His answer; “Yes, but it requires a different approach altogether. Some MSPs build super complex workflows directly in their PSA, and I get that. It’s the hub for everything. But we’re moving toward a world where AI can just understand what needs to happen and act. It doesn’t mean PSAs go away, but it does mean the way we interact with them fundamentally shifts.”

Rather than scripting out every edge case, MSPs can now leverage AI to interpret the context that already exists and respond dynamically. In that world, the PSA becomes less a place for logic and more a place for structured data. The heavy lifting moves from human-configured workflows to AI-driven orchestration. Most importantly, Lee claims that; “AI doesn’t necessarily replace people. It just changes their role. If you had two people building workflows in your PSA and getting maybe a 10% margin bump, now those same people manage an AI platform and you’re seeing 300% improvements. That’s the shift. It’s not removing the humans; it’s amplifying what they can do.”

The Rise of Forward-Deployed Engineers

If AI is going to live up to its promise in the MSP space, it can’t be built in a vacuum. That’s why Lee and his team at ZofiQ have embraced a new development model (conceptualized by Palantir) that puts engineers side-by-side with the people they’re building for. “There’s this growing concept across industries called a ‘forward-deployed engineer.’ This is someone who builds alongside the end user instead of in isolation. We find this incredibly effective when building for MSPs.”

This boots-on-the-ground approach flips the typical automation model on its head. Instead of trying to code from a distance, forward-deployed engineers get into the weeds with techs and owners, observe their workflows firsthand, and co-create solutions that actually work. It’s part product development, part customer success, and part consulting. As Silverstone recalls, “A big part of how we built ZofiQ was by sitting with MSP technicians and owners, watching how they actually worked. And yeah, [ironically] a lot of them had full-time staff just building workflows in their PSA. But this experience was critical to our development.”

Capturing & Activating Tribal Knowledge

Every MSP has a knowledge problem. Not because they don’t have enough of it, but because so much of it is inaccessible or hidden in plain sight. The most valuable technical and operational insights often exist outside of the PSA, knowledge base, or other structured systems. This is what Lee Silverstone refers to as tribal knowledge. “Traditionally, MSPs store knowledge in four places: best case, in KB articles; next best, in a folder somewhere; sometimes in tickets; and often just in people’s heads. That tribal knowledge is incredibly hard to access and act on. But when you have an AI that can plug into your PSA and pull data from chats, tickets, and notes that changes the paradigm entirely.”

In other words, the goal is no longer to create more documentation. It’s to extract and surface what already exists across all those fragmented layers of data. “I actually think AI changes what knowledge storage even looks like for an MSP. You don’t need to constantly chase down info or write exhaustive KBs. Those days are over. AI can extract tribal knowledge from all the places it lives (the PSA, Teams chats, internal notes) and surface it when needed. That’s a game changer.” For MSPs, this means technicians don’t have to dig for old tickets or bother their Service Leader just to figure out how a weird VPN config was done last year. The knowledge becomes always available, always up to date, and finally freed from the confines of human memory.

Leveraging Value vs. Effort

Silverstone and I later discussed a recent thread on r/MSP where someone floated a seemingly reasonable idea: “There needs to be a direct log of what AI or RPA work is generated, how many human hours it would take to perform the same work, and designate the outcome of the work.” The premise was that MSPs need this kind of breakdown to determine if the investment is actually worth it and to demonstrate the impact to clients. 

But as Lee and I unpacked it, we realized that logic is actually a symptom of a deeper mindset problem. MSPs have historically used effort (not outcomes) as their primary point of leverage with clients. They walk into QBRs with a BrightGauge report and say, “Look, here’s how many hours we spent on your account.” At the same time, if they aren’t resolving tickets, they struggle to prove their value. But in an AI-driven world, that math no longer makes sense.

Lee argues that this shift isn’t just inevitable. It’s essential. Clients don’t care how many hours something took. They only care that it works. “I think MSPs need to rethink what it means to deliver value. It doesn’t matter how many tickets you closed or how long you were on the phone. It matters whether the network is up, whether the client’s secure, whether the outcomes they expect are consistently delivered. That’s what matters, and what customers care about.” 

Internally, of course, MSPs still need to track time and effort to manage profitability. But that doesn’t mean they should lead with it in client conversations. As Lee puts it: “You don’t go to a mechanic and care how many hours the repair took. You just want the thing fixed.” And that’s exactly the mindset MSPs need to adopt if they want to stay relevant in the AI era.

The AI Flywheel Effect

If it feels like the AI leaders in the IT Industry are pulling further ahead, that’s because they probably are. Lee Silverstone calls it the “flywheel effect,” and for MSPs, it’s the new reality. “MSPs that lean into AI are going to have a flywheel effect in their business. They’ll have higher margins, they’ll scale faster, and as they grow, they get access to more data. That data then feeds the model, which drives better automation, which leads to even more efficiency. It becomes a self-reinforcing loop.”

In practical terms, it means the market leaders are not just winning, but they’re accelerating at a faster pace. With each new client, they generate more data. With more data, their AI gets smarter. With smarter AI, their operations become more efficient and cost-effective. And those savings get reinvested into growth, creating a loop that gets harder and harder to catch up to. “The top 10% of MSPs are going to end up owning most of the market, because AI creates this compounding advantage. That’s how you win by letting the flywheel spin faster.”

Conclusion

If there’s one thing this conversation with Lee Silverstone makes clear, it’s that the age of deterministic automation is ending and for MSPs, that’s good news. RPA may have promised efficiency, but it rarely delivered in the messy, variable environments MSPs operate in. AI, especially in its agentic form, brings a fundamentally different promise: automation that adapts, learns, and scales with you.

But it’s not enough to just swap tools. We need to rethink how MSPs define value, how they structure workflows, and how they build for the future. This shift from rule-based to dynamic automation will change not only the software stack but the mindset of the entire industry. Those who embrace this change will move faster and unlock economies of scale that were previously out of reach.