AI Integration into MSPs Requires Human-Centric Service Models

The Managed Service Provider market, a $100 billion behemoth often overlooked by Silicon Valley, stands at a critical juncture. While AI promises to revolutionize productivity, its integration into the deeply entrenched, decade-old IT and security services infrastructure is proving far more complex than simply selling another SaaS tool. This conversation with Peter Doyle, CEO of Treeline, reveals that the true opportunity lies not in replacing humans with AI, but in building AI into the human-led service model. The hidden consequence? A widening gap between the capabilities of modern tech and the reality of IT services delivery, creating a fertile ground for new business models that embrace, rather than discard, human expertise. This analysis is crucial for founders, investors, and IT leaders seeking to understand the non-obvious path to innovation in a market ripe for disruption.

The Unseen Complexity: Why "SAS is Dead" for the $100 Billion MSP Market

The managed service provider (MSP) market, a foundational pillar of the global economy responsible for keeping IT and security running for small and mid-sized businesses, is a decade behind the curve of modern technology. This isn't a failure of ambition, but a consequence of deeply ingrained operational realities. Peter Doyle, CEO of Treeline, argues that the conventional approach of simply layering more Software-as-a-Service (SaaS) solutions onto existing, often fragmented, toolchains misses the fundamental challenge: the inherent messiness of service delivery.

The core issue, as Doyle explains, is that MSPs are not just selling software; they are managing complex human workflows, diverse customer needs, and a legacy of processes. The average MSP juggles 30 to 35 disparate software tools, creating a system where technicians are often reactive, waiting for tickets rather than proactively driving solutions. This is where the "SAS is dead" provocation gains traction. It’s not that SaaS itself is failing, but that applying a pure SaaS model to a fundamentally service-oriented category, which requires deep human integration, is insufficient. Doyle's insight is that true transformation requires embedding software, automation, and AI directly into the operational fabric of the service delivery itself, working alongside, not in place of, human technicians.

"The interesting thing there is that like at first when we set out to start Treeline with my co-founder Hussein the initial idea for only a couple weeks was oh let's build a sas tool we think that automation and now ai is going to be important for this category let's sell it into the category and like the funny thing with that is very quickly we just realized that we didn't want to be the 36th software tool and actually redefine and try to reinvent this space from the ground up with all of these modern technologies in mind we kind of needed to go into like the guts of the offering and the guts of how these technicians operate to actually try to build a new model that services customers better."

-- Peter Doyle

This requires a fundamental shift from selling point solutions to building a cohesive, integrated operational model. The downstream effect of this approach is a compounding advantage: as the software and AI capabilities mature, they enhance the human element, leading to greater efficiency, proactivity, and ultimately, a superior customer experience. This iterative, compounding growth is precisely what provides a durable moat against competitors who might only offer incremental improvements through additional SaaS tools.

The Forward Deployed Engineer: A Confession of AI's Implementation Challenge

The rise of the "forward deployed engineer" (FDE) trend, championed by leading AI labs like Anthropic and OpenAI, offers a critical clue to the adoption challenges of AI. Doyle points out that even the most advanced AI companies recognize that their powerful software requires human expertise for effective implementation and use, particularly within complex enterprise environments. This isn't a temporary need; it's an admission that pure software solutions often struggle to penetrate and integrate with existing critical systems.

Treeline’s model embraces this reality from day one. By starting as a services company and iteratively building software and AI into its operations, Treeline leverages human expertise as a core component of its product. This approach contrasts sharply with traditional software companies that might offer a platform and expect customers to figure out the implementation. The FDE model, and by extension Treeline's strategy, acknowledges that the "trillions of dollars of software infrastructure" underpinning the global economy is not easily disrupted by standalone AI agents. Integrating with these legacy systems requires a deep understanding of operational workflows and a willingness to work within the existing human and technical ecosystem.

The consequence of this approach is a more robust and adaptable model. While pure SaaS plays might struggle with adoption in the messy reality of services categories, Treeline's human-centric AI integration allows for a more gradual, yet more profound, transformation. This iterative process, where new AI capabilities are folded in as they become reliable, ensures that the company doesn't need to rebuild its entire offering every six months. Instead, it builds a compounding advantage, where each technological advancement enhances the core service delivery, creating a durable competitive moat.

The Compounding Advantage: Why Immediate Pain Fuels Long-Term Moats

The IT and security services market has historically presented a false dichotomy: either manage complex internal IT infrastructure or outsource to a traditional MSP. Doyle explains this as a "vestigial trait" of the industry's evolution, where physical proximity to servers was once paramount. While cloud and remote work have modernized many aspects of IT, the fundamental reactive, call-and-response model of many MSPs has persisted. This inertia creates a significant gap between current capabilities and the potential offered by AI and modern software.

Treeline's strategy directly addresses this gap by meeting the industry where it is--often still operating with traditional provider models--while embedding advanced technology behind the scenes. This requires a willingness to embrace immediate discomfort. For instance, integrating AI to augment technician workflows, making them more efficient and proactive, might initially require significant groundwork and training. However, the payoff is substantial. This investment in internal tooling and AI integration allows Treeline to move beyond reactive ticket handling to proactive issue resolution, often solving problems before the customer even realizes they exist.

"The core flow of how traditional service providers operate is they have a bunch of customers and those customers just throw a bunch of issues and tickets and requests to the msp that can be where onboarding multiple people on monday can you get them set up in all of these systems it could be a automated machine generated alert from like crowdstrike or some some security tool that says you need to to look into this vulnerability or this issue or the technicians are kind of sitting there waiting for one of these and it's rather reactive and so even if it stays reactive we can equip them with a lot of really interesting information and context and automation and agentic tools to help them um be really efficient with that but then also even more so be way more proactive like we noticed that this was a problem before you submitted a ticket and so that goes to your question like customers see right off the bat improved service um and just like a better experience like my issue was resolved immediately before i had to say something or it was resolved in one minute whereas historically it took 30."

-- Peter Doyle

This proactive stance, enabled by AI-driven insights and automation, transforms the customer experience from one of merely addressing problems to one of continuous improvement and enhanced visibility. This creates a compounding advantage because it shifts the customer relationship from a transactional one to a strategic partnership, deepening engagement and building trust. It’s this willingness to invest in difficult, behind-the-scenes operational improvements that builds a durable moat, as most competitors remain focused on the more visible, but less impactful, layer of simply selling more SaaS tools.

The Future of Services: Beyond the MSP Acronym

The conversation underscores that the future of IT and security services is not about replacing humans with AI, nor is it about simply optimizing existing fragmented SaaS toolchains. It's about a symbiotic relationship where AI augments human expertise, creating a more effective and scalable service model. Doyle emphasizes that while the "MSP" label might persist, the underlying operational model will fundamentally change. The goal is to become the highest trust third-party partner for businesses, a role that extends beyond traditional IT and security into broader adoption of modern software and AI.

The challenge lies in integrating AI into the "trillions of dollars of software infrastructure" that forms the backbone of the economy. This is a long-term endeavor, requiring new business models and a deep understanding of operational realities. Treeline’s approach of starting as a services company and iteratively building software and AI into its operations is a testament to this understanding. It’s a strategy that prioritizes deep integration and compounding advantage over quick, superficial wins.

"And so our view is that whatever way automation software and ai like fits your market go for it but these services categories like i just fundamentally believe for the most part that it's just going to be really hard to sell pure play software into these categories."

-- Peter Doyle

This perspective suggests that while pure-play software companies may struggle to gain traction in deeply entrenched service categories, companies that blend human expertise with AI-driven automation are poised for significant growth. The "forward deployed engineer" concept highlights this need, demonstrating that even leading AI labs recognize the necessity of human intervention. For Treeline, this means a continuous journey of refining its model, folding in new AI capabilities as they mature, and ultimately, building a scalable, high-trust partnership with its customers, moving as close to a software company as possible without abandoning the essential human element.

Key Action Items

• Immediate Actions (0-3 Months):

  • Audit Existing Toolchains: For IT service providers, conduct a thorough audit of all current software tools, identifying redundancies and gaps in integration. This mirrors Treeline's initial realization of having "30 to 35 stitched together software tools."
  • Identify Technician Augmentation Opportunities: Pinpoint specific, repetitive tasks performed by technicians that could be augmented by AI or automation. Focus on areas that cause immediate frustration or inefficiency.
  • Pilot Internal AI Tooling: Select one or two high-impact technician-facing tools or AI agents to pilot internally. Measure efficiency gains and technician feedback rigorously.
  • Develop Proactive Monitoring Dashboards: Begin building internal dashboards that leverage automation to identify potential issues before they become customer-reported tickets.

• Medium-Term Investments (3-12 Months):

  • Integrate AI into Workflow Automation: Move beyond simple task augmentation to embedding AI agents directly into core service workflows (e.g., onboarding, issue resolution). This requires careful consideration of extensibility and integration with existing systems.
  • Refine Customer Communication & Visibility: Develop customer-facing interfaces or reports that clearly communicate the value and proactive nature of the AI-enhanced services, building trust and demonstrating improved service experience.
  • Train Technicians on AI Collaboration: Invest in training programs to equip technicians with the skills to effectively collaborate with and leverage AI tools, fostering a culture of human-AI partnership.

• Longer-Term Investments (12-18 Months+):

  • Develop Compounding Service Offerings: Design new service offerings that inherently benefit from the iterative compounding of AI and automation, creating a durable competitive advantage. This could involve predictive maintenance, advanced security threat intelligence, or automated compliance reporting.
  • Explore Strategic Partnerships: Identify and partner with best-of-breed software and AI providers whose tools are highly extensible and can be seamlessly integrated into the core operational model, avoiding the "36th tool" trap.
  • Shift Towards Proactive Value Delivery: Fundamentally shift the business model from reactive support to proactive value delivery, where AI-driven insights enable strategic guidance and new service opportunities for customers. This requires patience, as the payoff is delayed but creates significant differentiation.