The AI-First MSP: From IT Providers to Strategic Partners

According to CRN, PCH Technologies CEO Tim Guim presented a practical 90-day roadmap for MSPs to monetize artificial intelligence at The Channel Company’s 2025 XChange NexGen conference in Houston. Guim emphasized that AI investment and revenue will triple within two years, urging MSPs to transition from being IT providers to AI strategists. The Sewell, N.J.-based CEO revealed his company has been running Hatz AI’s technology internally for over a year across departments from finance to HR to security, creating a foundation for delivering secure, private AI solutions to clients. Guim highlighted that MSPs have a built-in advantage due to existing client data access and customer familiarity with recurring revenue models, positioning them to become trusted AI advisors. This strategic shift represents a fundamental transformation in how managed service providers approach their business models.

The Built-In Advantages MSPs Overlook

What makes Guim’s perspective particularly compelling is how it reframes what many MSPs consider liabilities into strategic assets. Most managed service providers already possess the three critical components for successful AI implementation: established client trust relationships, deep integration into business workflows, and recurring revenue models that align perfectly with AI-as-a-service offerings. The traditional MSP model of providing ongoing IT support creates natural entry points for AI services that enterprise software vendors would struggle to match. This isn’t about adding another service to the catalog—it’s about fundamentally reimagining the MSP’s role from technology maintainer to business transformation partner.

The 90-Day Roadmap: Ambitious but Achievable

While Guim’s 90-day timeline sounds aggressive, it reflects the accelerating pace of AI adoption across industries. The critical insight in his approach is starting with internal implementation before client rollout. This “eat your own cooking” methodology addresses the single biggest barrier to AI adoption: trust. When MSPs can demonstrate tangible results from their own AI implementations, they move beyond theoretical selling into evidence-based consulting. However, this rapid timeline assumes several factors: existing technical competency, clear use case identification, and the organizational discipline to avoid scope creep. Many MSPs will need to assess whether their current team structure can support this accelerated transformation.

Vendor Selection: The Make-or-Break Decision

The mention of Hatz AI highlights a crucial consideration that many MSPs underestimate: the long-term implications of platform selection. Choosing an AI vendor isn’t just about current capabilities—it’s about architectural compatibility, data governance policies, and future roadmap alignment. MSPs must evaluate whether potential AI partners treat them as strategic partners or merely distribution channels. The vendor’s approach to data privacy, model customization, and integration capabilities will determine whether MSPs can deliver truly differentiated solutions versus becoming another reseller of commoditized AI tools. This decision requires technical due diligence that many MSP leadership teams may not be equipped to conduct independently.

Beyond Basic Monetization: The Value Capture Challenge

The real monetization challenge for MSPs isn’t just pricing AI services—it’s capturing the full value they create. Traditional per-user or per-device pricing models may not reflect the transformational impact AI can deliver. Forward-thinking MSPs should consider value-based pricing tied to business outcomes like reduced operational costs, accelerated decision-making, or improved customer satisfaction. This requires deeper business understanding than most MSPs currently possess and represents both the greatest opportunity and most significant capability gap. The MSPs who succeed will be those who can articulate and measure the business impact of AI beyond technical metrics.

The Coming Competitive Shakeout

Guim’s warning that “if you don’t, someone else will” points to an inevitable industry consolidation. We’re likely to see a bifurcation between MSPs who successfully transform into AI strategists and those who remain stuck in traditional IT support roles. The former will command premium valuations and strategic partnerships, while the latter will face increasing margin pressure as basic IT services become increasingly commoditized. This isn’t merely about adding AI capabilities—it’s about fundamentally rethinking the MSP value proposition in an AI-driven business landscape. The CEO leadership challenge involves not just technology adoption but organizational transformation at a pace most MSPs have never experienced.

Implementation Risks Even Successful MSPs Face

While Guim highlights success stories, the path to AI implementation is fraught with challenges that extend beyond technical integration. Data governance becomes exponentially more complex when AI systems process sensitive client information. Model drift—where AI performance degrades over time—requires ongoing monitoring most MSPs aren’t equipped to provide. And the regulatory landscape for AI is evolving rapidly, creating compliance risks that could expose both MSPs and their clients to significant liability. Successful AI adoption requires building new competencies in areas like ethics, explainability, and continuous model evaluation that traditional IT management never demanded.