Field notes from the road, kept like a journal.

№ 05

Sep 11, 2026

CanITCon2026 · Calgary, CA

For two years, the MSP playbook for AI has been the readiness assessment: a one-time $5,000 to $15,000 project that scans a tenant, produces a deck, and then goes quiet. It sells once. It sounds like 2005-era consulting. And it leaves the client exactly where the MSP found them, aware they have a problem but no closer to an outcome. The uncomfortable truth is that nobody actually wants to buy AI readiness. Readiness is a status, not a result. The real problem is not a tools problem. MSPs are surrounded by tools: Copilot, Claude, ChatGPT, an entire Agent Store. The problem is an operating-model problem: how to sell, deliver, govern, and measure AI as a managed service across an entire client base, the same way every time. Building custom agents for every client does not scale. The average MSP believes it can meaningfully serve maybe 20 percent of its clients with AI in a given year, which leaves 80 percent of the book untouched while shadow AI spreads inside those same companies unmanaged and ungoverned. This session makes the case for the shift from a project to a practice. We will walk through what an AI operating model actually looks like inside an MSP: a repeatable motion that discovers demand and shadow AI, plans the managed adoption of generative AI, then runs deployment, governance, and observability on an ongoing basis. We will cover the recurring rhythm that keeps it alive, a virtual Chief AI Officer accountable for the roadmap, a monthly working session where decisions get made and usage gets reviewed, and a quarterly segment that proves value back to leadership. The through-line is measurement: training completion as the leading indicator, actual observed usage as the truth, and adoption tracked against a baseline rather than assumed. Attendees will leave able to answer the question every client is really asking, which is not "are we ready" but "what do we do now, and how do we know it worked." The economics follow the model. A one-time assessment is a $10,000 event. An AI operating model delivered as a managed service is recurring revenue that gets measured, managed, and renewed. Readiness gets you in the room. The operating model is what you actually sell.

№ 04

Jun 8, 2026

Pax8 Beyond · Salt Lake City, UH

This document is a field-level perspective on what’s actually happening with AI inside MSP environments right now. The core premise is simple: clients have already adopted AI, but without structure, ownership, or visibility. The opportunity for MSPs isn’t introducing AI, it’s stepping in to manage what already exists. The document reframes AI from a tool or license decision into something that must be governed, measured, and continuously improved if it’s going to generate real business value. It introduces Transformation-as-a-Service (TaaS) as the model to make that shift. Most MSPs today operate in a project mindset, running workshops, assessments, and deployments that create activity but not outcomes. This leads to the “trough of no value,” where AI is implemented but not tied to workflows or ROI. TaaS replaces that with a managed, repeatable motion that includes ongoing enablement, governance, and reporting, turning AI into something that evolves over time instead of stalling after initial setup. The execution model is built around operational discipline. MSPs start by establishing a real baseline of AI usage across client environments, then translate those insights into targeted plans and small, repeatable workflow wins. Frameworks like MAGIC reinforce that AI must be owned as an operating layer, not treated as a one-off initiative. The end state is clear and practical: AI becomes boring in the best way possible, consistently managed, governed, measured, and improved, creating a durable, scalable service line for MSPs.

№ 03

May 28, 2026

RejectionCon 2026 · Virtual

Presentation in support of RTF! https://secure.givelively.org/donate/rural-technology-fund/rejectioncon-2026 Have you given to the Rural Tech Fund? This document is a direct, no-nonsense breakdown of why most AI initiatives inside businesses are stalling and what to do about it. It challenges the typical way AI is introduced, often as tools, experiments, or one-time projects, and instead reframes the problem as an execution gap. Companies are already buying and using AI, but they are not seeing consistent, measurable value from it. The result is a growing disconnect between investment and impact, where costs show up immediately and value does not. What makes this worth downloading is how clearly it defines the root cause of that gap. Most approaches either over-index on custom work that does not scale or stop at assessments that never turn into operational outcomes. The document lays out why those motions fail and shifts the focus toward running AI as an ongoing, managed capability inside a business. It introduces a more practical model where progress is measured continuously, adoption is owned, and value is made visible over time rather than assumed at the end of a project. It also gives a concrete path forward. Instead of abstract frameworks, it outlines a structured way to turn AI into a repeatable, governed, and measurable practice across an organization. That includes where to start, how to standardize tooling, how to align leadership, and how to build momentum through real usage, not demos. The takeaway is simple: the opportunity is not in deploying AI, it is in owning how AI actually gets used and delivers outcomes inside a company.

№ 02

May 19, 2026

MSPGeekCon 2026 · Orlando, FL

This deck reframes the entire AI conversation for MSPs around a simple shift: you don’t sell AI, you operationalize it. The core insight is that clients are already using AI without structure or oversight, often running multiple unsanctioned tools in the background. The opportunity isn’t introducing AI, it’s stepping in to manage the chaos. Just like previous waves like cloud and cybersecurity, the pattern repeats. Fear shows up first, but the MSPs who lean in early expand their role and capture more value. The document makes it clear that most current approaches are broken. Custom AI projects and “AI readiness” engagements are framed as consulting work that doesn’t scale, serving a small percentage of clients with high effort and little compounding value. This leads to what’s called the “trough of no value,” where licenses are purchased and money is spent, but there is no training, governance, or real adoption. The result is predictable: customers feel the cost immediately, but the value remains invisible, creating pressure from leadership asking where the ROI actually is. The path forward is operationalization as a managed service. This means moving from one-time projects to a recurring motion built on governance, training, and measurable outcomes. The shift from “AI ready” to “AI enabled” is central. Ready is a future state and a project, while enabled is something that happens now and is sustained over time. By establishing policies, enabling users, driving adoption through cadences like QBRs, and continuously improving workflows, MSPs turn AI into a repeatable, scalable service that delivers visible ROI and compounds over time.

№ 01

May 18, 2026

MSPGeekCon 2026 · Orlando, FL

Your professional growth depends less on linear moves and more on the intentional collection of diverse experiences. This session explores how compounding curiosity transforms a standard technical path into a resilient, high-value career. Learn to identify the hidden patterns in your work history that signal when to pivot and how to deepen your expertise.