First Pass: A Collection of Insights on the world of AI and MSPs

Lemhi Launches the SaaS Platform MSPs Need to Deliver AI as a Recurring Managed Service

Mon, 01 Jun 2026 13:43:38 GMT

Exiting stealth with pre-seed funding from Top Down Ventures, the company is giving Managed Service Providers a SaaS offering and playbook to sell AI as a Managed Service alongside existing Managed IT and Managed Cybersecurity offerings.

INDIANAPOLIS — June 2, 2026 —

Lemhi , the AI Transformation-as-a-Service platform for Managed Service Providers (MSPs), launched out of stealth today. The company closed a pre-seed round led by Top Down Ventures and backed by Lookout Ventures and Start Something Ventures. Lemhi gives MSPs the tools to deliver AI to their customers at scale and turn that work into a recurring book of business.

Today, MSPs are fielding AI questions in every customer conversation, but they can't package the work the way they package everything else they sell. It keeps landing as one-off projects, consulting hours, and tool deployments that never make it onto a monthly invoice.

The demand isn't a problem. Turning it into a repeatable managed service is. Lemhi is built for that, giving MSPs a consistent motion to sell, deliver, govern, and measure AI as part of the same monthly agreement they already run with their customers.

"I've spent 17 years watching MSPs turn every major technology shift into recurring revenue," said John Harden, Founder and CEO of Lemhi. "AI is next, and this time the tools are already in our customers' hands. That makes it an operating model problem, not an AI tools problem. Lemhi gives MSPs the operating model and the software to execute it. The MSP earns a recurring revenue line that doesn't exist today, and the customer finally gets real value out of the AI they're already paying for."

Lemhi Engage, the company's first product, will be available for design partners beginning next week at Pax8 Beyond. It gives MSPs a way to run every AI engagement the same way every time, producing a workforce readiness score, an ROI plan, an M365 tenant assessment, and a phased rollout plan with owners, guardrails, and success metrics already built in. Engage is the front of the operating model: where the MSP scopes the work, sets expectations, and earns the right to run AI as a managed service.

Lemhi's approach supports an emerging category: AI Transformation-as-a-Service. The average MSP can only build custom AI workflows for about 10% of their clients each year, leaving the other 90% untouched. Lemhi flips that ratio. Engage gives MSPs a structure to deliver adoption, governance, strategy, and policy around the AI tools customers already pay for, so every client gets a roadmap, not just the top accounts. “This is the motion that can reach 100% of customers, making sure there are no ‘have nots’ in the client base,” says John Harden.

These “have nots” are a risk to the MSP as they represent future churn or share-of-wallet loss to competitors. Gartner reports that only 28% of AI initiatives meet ROI expectations without a structured, phased program. Lemhi is built to close that gap on a single bet: AI should augment the next generation of workers, not replace them.

Beyond the pre-seed round, Lemhi has been selected into Top Down Ventures' Vibe Studio and ConnectWise's PitchIT accelerator, two of the more selective programs vetting early-stage companies built for the channel. The company is a member of GTIA, Microsoft for Startups, and the International Association of Microsoft Channel Partners (IAMCP), where co-founder and CEO John Harden as the representative for Americas on the International Innovation Committee.

"Every MSP we talk to is feeling the pressure, but most of the activity in the market today is still ad hoc: licenses, pilots, disconnected projects,” said Chris Day, Founder & Chairman of Top Down Ventures. “There’s no durable business model behind it yet. Lemhi is one of the first software platforms we’ve seen that turns this into a structured, repeatable service MSPs can actually scale. That’s why Top Down is excited to invest."

This perspective is shared by the early design partners who have helped craft Lemhi’s product vision.

“AI is creating a new category of business conversation that many MSPs are not naturally equipped to lead,” said Robert Cioffi, Co-Founder of Progressive Computing. “As clients look for guidance on strategy, analytics, and operational impact, there’s a real risk they’ll turn elsewhere for that expertise. Lemhi empowers our team to lead those higher-value conversations and strengthen our role as a trusted business advisor.”

Lemhi is onboarding select cohorts of forward-thinking MSPs over the coming months, giving early partners a head start on a new AI service line before the rest of the channel does. Each cohort works directly with Lemhi's team to scope, price, and roll out the offering across their existing customer base. MSPs interested in joining can add their name to the waitlist at Lemhi.com .

About Lemhi

Lemhi is the AI Transformation-as-a-Service platform for managed service providers. The company gives MSPs a repeatable operating model to sell, deliver, govern, and measure AI as a recurring service line, the same way they run vCIO and vCISO offerings today. Built by MSP operators for MSP operators, Lemhi is defining AI Transformation-as-a-Service as the next recurring selrvice line for the channel.

Visit www.lemhi.com for more information.

AI Transformation as a Service (TaaS): The Managed Service Line MSPs Have Been Missing

Thu, 28 May 2026 01:26:24 GMT

What Is AI Transformation as a Service (TaaS)?

A Guide for MSPs

AI Transformation as a Service, or TaaS, is a monthly managed retainer that delivers AI strategy, governance, enablement, and observability inside an MSP's existing service motion. Instead of selling AI as a one-time readiness project that ends the day the assessment is delivered, MSPs sell TaaS as an ongoing practice that absorbs the up-front work into the retainer and stays accountable for outcomes month after month. In short: TaaS is what AI looks like when you stop billing for it like consulting and start delivering it like managed services.

How Transformation as a Service Reshapes the MSP Revenue Model

For most MSP owners, AI has felt like a category they are losing money on. Clients ask for Copilot. They ask for ChatGPT governance. They ask for "an AI strategy." The MSP scopes a project, sells a fixed-fee assessment, delivers a roadmap document, and then watches the relationship go quiet until the next renewal conversation. The hardest, most valuable work — turning Copilot on, training employees, writing the AUP — gets billed once and never compounds.

TaaS rewrites that motion. The same playbook still runs through the four onboarding phases — pre-contract & ROI, strategy & governance, technical readiness, AI roadmap kickoff. But under TaaS, the MSP absorbs those phases into a retainer. The first three to four months are net negative on margin. From month five forward, the retainer turns into strong recurring revenue — the same shape as a VCISO or Managed IT engagement.

There are three operational reasons MSPs are moving to TaaS:

AI value compounds when the practice stays in place. Microsoft's 2026 Work Trend Index found that 67% of AI's real impact comes from organizational factors — culture, manager modeling, talent practices — not from the tool itself. A one-time project cannot change the organizational factors. A managed practice can.
The bill of materials is repeatable. TaaS standardizes the deliverables across every client: an AI policy first draft in Phase 1, the AI Readiness Assessment in Phase 2, Copilot Quick Wins in Phase 3, then the Monthly AI Council and QBR AI Segment recurring. The MSP isn't reinventing the engagement each time.
It maps cleanly onto MSP economics. TaaS works because MSPs already understand absorbing onboarding cost in exchange for the long tail. The commercial logic is the same as a Managed IT rollout — and the channel knows how to price it.

For an MSP, the most concrete way to think about TaaS is: it's the commercial wrapper that lets you put a VCAIO on the org chart and a Monthly AI Council on the calendar without billing your client for the strategist's existence.

Why SMBs Get More Value from an AI Managed Service Than an AI Project

For an SMB executive, the version of this story is shorter and more honest. You probably already paid for an AI workshop or assessment in 2024 or 2025. You may have a roadmap document somewhere. Your team is still using ChatGPT in browser tabs you don't manage. Copilot licenses sit underused. The ROI you were promised hasn't shown up.

That is the Trough of No Value — the flat customer-value curve that follows any AI readiness project once the consultant walks away. It is not your fault. It is the shape of the engagement.

TaaS is what SMBs get when their MSP runs AI like a service rather than a project:

A dedicated AI strategist (the VCAIO) who shows up every month, owns the roadmap, and reports to your leadership team in language you understand.
A monthly leadership working session (the AI Council) where decisions get made, not just status reported.
Continuous M365 hygiene — permissions, sensitivity labels, shadow AI detection, AUP enforcement — instead of a one-time assessment that's stale within a quarter.
Quarterly visibility into ROI via the QBR AI Segment: maturity-score movement, hours saved, dollars returned, what shipped, what's next.

For SMB leadership, the test of whether your MSP is selling you TaaS versus a glorified consulting engagement is simple: ask them what is included next month. If they can answer in specifics — Council agenda, training module, observability metric, AUP review — you are in a managed practice. If they shrug, you are in a project.

How Lemhi Operationalizes Transformation as a Service for MSPs

Lemhi is a SaaS platform built specifically to help Microsoft-centric MSPs sell, deliver, govern, and prove AI outcomes — without reinventing the playbook for every client. We translate TaaS from a thesis into an operating system.

Engage (GA June 2026) is the sales-enablement module. It runs the AI Leadership Survey, calculates the ROI, builds the readiness score, and produces a packaged proposal the MSP can take into a client meeting and close.
VCAIO tooling automates the prep work — observability pulls, training reports, maturity scoring, use case pre-scoring — so the VCAIO at the MSP can carry 12–18 Council clients (or 25–35 Compass Module clients) without the engagement drowning in manual hours.
The Continuous Scanner runs in the background across every client's M365 environment, surfacing permissions risk, shadow AI, and sensitivity-label gaps to the PSA ticket queue.
Standardized artifacts — AUP templates, Council agendas, QBR slide segments, roadmap formats — mean that the practice ships the same way to every client.

The thesis is straightforward: MSPs don't have an AI tools problem. They have an AI operating-model problem. Lemhi sells the operating model.

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The Trough of No Value: Why AI Readiness Projects Fail

Wed, 27 May 2026 01:40:17 GMT

The Trough of No Value: Why AI Readiness Projects Fail

The Trough of No Value is the flat customer-value curve that follows an AI readiness project when no managed service is in place. The MSP runs an assessment, turns Copilot licenses on, delivers a roadmap document — and then the engagement closes. The client paid for readiness and never captured the ROI. The trough is the gap between turning AI on and someone keeping it on. It is the single most common failure pattern in SMB AI work in 2026 — and it is structural, not accidental.

Why the Trough of No Value Is the Margin Killer in MSP AI Projects

For MSPs, the Trough of No Value is the reason AI revenue has been hard to convert into AI margin. Most MSPs we talk to have done at least one of these motions in the last 18 months:

An AI readiness assessment scoped as a fixed-fee project.
A Copilot enablement workshop delivered as a half-day session.
A "custom AI strategy" engagement with a six-figure price tag.

Every one of those motions runs into the same trough — because the readiness, the workshop, and the strategy are all front-loaded work. The hardest, most billable hours are at the start. The ROI for the SMB doesn't materialize until months later, and only if someone keeps the practice in place. If the engagement ends at delivery, the value never compounds.

Gartner found that only 28% of AI initiatives meet ROI expectations without a structured, phased delivery model. Prosci's 25-year change-management benchmarking study found organizations are 3.5x more likely to meet transformation objectives when executive sponsorship is active and visible. BCG's 2025 research found that AI success is 70% people and change management, only 10% technology. None of these factors get addressed by a project. All of them get addressed by a practice.

The TaaS reframe is structural:

Same playbook. Pre-contract & ROI (Phase 0), Strategy & Governance (Phase 1), Technical Readiness (Phase 2), AI Roadmap Kickoff (Phase 3) — the same four phases the MSP was already running as a project.
Different commercial wrapper. Phase 0–3 is delivered as onboarding inside a managed retainer, not as a billable kickoff. The MSP absorbs the front-loaded hours, the same way Managed IT or VCISO rollouts absorb initial deployment work.
Recurring cadence after Phase 3. The Monthly AI Council, the QBR AI Segment, the Continuous Scanner, ongoing training rollout, AUP enforcement, use case sequencing — all of it continues every month.

The MSP is net negative on margin for months 1–4. From month five forward, the retainer is strong recurring revenue. The customer value line — flat under a project — climbs every month under TaaS.

The MSP question to ask honestly: *if our last AI engagement at this client ended, what's the next billable interaction?* If the answer is "they have to come back for another project," you're shipping the Trough of No Value.

How SMBs Recognize the Trough of No Value in Their Own AI Investments

For SMB executives, the Trough of No Value usually shows up six months after the project ended. You paid for an assessment. You got a slide deck. Maybe you signed up for some Copilot licenses. Then the IT team got pulled into something else, the champions you identified moved on or got busy, the AUP draft never got finalized, and ChatGPT use crept in through browser tabs nobody is monitoring.

The trough is not a sign that your MSP is bad or that you wasted money. The trough is the shape of every AI readiness project in the market today. It happens because:

The hardest work — getting employees to *actually use* the tools in their day-to-day — happens after the project ends.
Microsoft's 2026 Work Trend Index found 67% of AI's real impact comes from organizational factors (culture, manager modeling, talent practices) — and those factors take months of consistent leadership attention to move.
70% of AI success is people and change management, according to BCG. No project delivers ongoing people work.
Without an executive sponsor pulling the rope every month, employee AI positivity sits around 15%. With active leadership support, it rises to 55% (BCG 2025).

The TaaS answer is to convert the project into a practice. You still get all the same artifacts — the assessment, the roadmap, the policy, the training. But you also get a VCAIO who shows up every month, a Monthly AI Council where leadership decisions get made, and a QBR AI Segment where ROI gets reported. The value line keeps climbing instead of flatlining.

The honest test for SMB leadership: *do you know what your AI practice is delivering next month?* If yes, you're in a managed practice. If no, you're in the trough.

How Lemhi Helps MSPs Close the Trough of No Value with Managed Practice Delivery

Lemhi built the TaaS framework specifically because the founding team — 45+ years of combined MSP and SaaS experience, with two prior MSP SaaS exits — kept seeing the Trough of No Value in their own MSP partners' client books. The pattern was too consistent to be an execution issue. It was a model issue.

Engage (GA June 2026) runs the Phase 0 motion that lets MSPs convert an existing AI conversation into a TaaS retainer instead of a one-off assessment. The product runs the AI Leadership Survey, calculates ROI, builds the Plan + Readiness Score, and produces a packaged proposal.
VCAIO tooling automates the prep so the Council and QBR motions are sustainable across the MSP's full client book. The VCAIO walks into every meeting with curated observability, training, and survey data.
Standardized recurring artifacts — Council agenda, QBR slides, Maturity Score, AUP review cadence — mean the practice keeps shipping the same way every month, every client, every VCAIO.
The Continuous Scanner keeps Copilot and M365 hygiene moving in the background. Permissions, sensitivity labels, shadow AI, sharing risk — all of it surfaces to the PSA ticket queue instead of accumulating as silent debt.

Lemhi's whole product thesis is: the trough doesn't close itself. You have to wrap the project work in a practice. We sell the practice.

What Is a VCAIO (Virtual Chief AI Officer)? The MSP Guide

Tue, 26 May 2026 01:31:19 GMT

What Is a VCAIO (Virtual Chief AI Officer)? The MSP Guide

Why Every MSP Needs a Named Virtual Chief AI Officer

The VCAIO solves the question every MSP owner is being asked in 2026: *who owns AI at this client?* If you can't answer that, you have an account vulnerability. Somebody else will eventually walk in, name a VCAIO, and absorb the strategic relationship.

A VCAIO is not a help-desk role and not a data scientist. The Lemhi VCAIO profile is built on five required competencies:

Executive presence and facilitation — the VCAIO operates the Monthly AI Council and presents in the QBR. Comfort in the leadership room is mandatory.
Microsoft 365 and Copilot fluency — licensing, agent surfaces, governance levers, deep enough to hold a credible conversation with the technical team and the executive sponsor in the same hour.
Change management instinct — AI absorption is 70% people. The VCAIO has to recognize which lever (training, manager modeling, AUP, incentives) to pull when adoption stalls.
Use case judgment — reads the pipeline, scores fit, sequences pilots. Knows when to greenlight a custom agent vs. recommend out-of-the-box.
Governance and risk literacy — articulates the AUP, recognizes shadow AI patterns, coordinates with the VCISO on data classification.

Capacity matters. A VCAIO running full AI Councils carries 12–18 clients. A VCAIO running mostly Compass Module engagements carries 25–35. A mixed book averages around 18–24. These numbers assume the VCAIO is supported by tooling that automates the prep work — observability pulls, training reports, maturity scoring, use case pre-scoring. The judgment work — sequencing, sponsor relationship, in-room facilitation — is the irreducible human layer.

Authority is the second thing MSPs get wrong. Lemhi separates VCAIO authority into three levels:

Operational (Council agenda, QBR content, training cadence, observability config): the VCAIO decides alone.
Tactical (use case pipeline prioritization inside an approved roadmap, agent activation, AUP enforcement consistent with policy): the VCAIO decides; the Council reviews.
Strategic (roadmap revisions, AUP rewrites, executive sponsor changes, spend outside the retainer): the VCAIO recommends; the Council and sponsor decide.

Write these into the engagement charter at TaaS kickoff. Embedded strategists fail when their authority is fuzzy.

What the VCAIO Delivers to SMB Leadership Each Month

If you are an SMB executive, you have already met your VCAIO whether you call them that or not. They are the person on your MSP team who shows up to talk about Copilot, AI policy, and ChatGPT — but until now, that conversation has been informal, occasional, and easy for both sides to deprioritize.

Naming the VCAIO is what makes the conversation real. The VCAIO is your single accountable strategist for AI. They own:

The AI roadmap — what gets rolled out, in what sequence, with what success metrics.
The AI policy — the Acceptable Use Policy gets drafted in Phase 1, revised in Phase 3 once real usage data exists, and reviewed every Council meeting.
The Monthly AI Council — a 60-minute leadership working session that the VCAIO facilitates and your executive sponsor chairs. Decisions made, owners named, observability reviewed.
The QBR AI Segment — a 10–15 minute block inside your existing quarterly business review. Maturity-score movement, Copilot adoption, ROI metrics, next-quarter priorities.
The sponsor relationship between Councils — the VCAIO is available for unblock conversations when a decision can't wait a month.

The fastest test of whether you have a real VCAIO: can you name them? If yes, you have a strategist. If no, you have an AI vendor.

How Lemhi Helps MSPs Stand Up a VCAIO Practice at Scale

Standing up a VCAIO practice is hard from scratch. Lemhi's platform is designed to make it standardized and repeatable across an MSP's book of business.

Pre-built role artifacts. Engagement charter templates, authority-level boundaries, capacity standards, and the five-competency profile — all standardized so that every VCAIO at your firm delivers the same way.
Automated prep. Observability pulls, training reports, AI Maturity Score updates, and use case pre-scoring are continuous in the platform. The VCAIO walks into Council with the data already curated.
Council and QBR runbooks. The 60-minute Council agenda, the QBR AI Segment slide structure, and the Compass Module 30-minute format are built in.
The Compass Module. For clients too small for a full Council, Lemhi runs the lighter Compass cadence — same VCAIO, same roadmap, fewer hours, lower cost to serve.
Career path for the VCAIO. Because the platform handles the repetitive prep, your VCAIOs can specialize on the parts of the role that compound — sponsor relationships, in-room judgment, use case sequencing.

Adding a VCAIO is how MSPs go from "we do Copilot rollouts" to "we own the AI relationship." Lemhi is how MSPs do it at portfolio scale.

AI Practice vs. AI Program: The Distinction That Makes MSPs Profitable

Wed, 20 May 2026 01:58:48 GMT

AI Practice vs. AI Program: The Distinction That Makes MSPs Profitable

The AI Practice is the commercial offering an MSP sells — the packaged, repeatable service the MSP delivers across its client book. The AI Program is the portfolio of AI work, owners, outcomes, and policies that the client runs internally. The MSP's AI Practice supports the client's AI Program. Lemhi's vocabulary deliberately separates the two because mixing them is the most common cause of scope creep, margin erosion, and accountability confusion in MSP AI work.

How the AI Practice vs. AI Program Distinction Protects MSP Margin

For MSPs, the Practice/Program distinction is a survival tool. Without it, every client conversation drifts into questions like "can you also help us with this internal initiative" — and the MSP either says yes and loses margin or says no and damages the relationship. With it, the boundary is clear: the MSP delivers the Practice, the client owns the Program, and the VCAIO is the connective tissue.

What the Practice owns:

The commercial structure. Pricing, scope, SLAs, escalation paths.
The named role. The VCAIO who carries the relationship.
The recurring artifacts. Monthly AI Council, QBR AI Segment, AI Maturity Score, AUP, Continuous Scanner output, training rollout.
The standardized playbook. Phase 0–3 onboarding, ongoing cadence, exit criteria.

What the Program owns:

The strategic intent. Why the client is investing in AI, what outcomes they want, what risks they will not accept.
The internal owners. The executive sponsor, the department heads, the AI Champions inside the client.
The decisions. What the Council decides goes into the Program — but the Program is what executes against those decisions.
The accountability. When AI helps or hurts the business, the Program is where the consequences land.

The VCAIO is accountable to the MSP for delivering the Practice and accountable to the client for advancing the Program. Both lines of accountability are visible in the Monthly AI Council and the QBR AI Segment.

Where MSPs lose money: when they accept Program-side work (custom agent builds, departmental AI projects, internal change-management initiatives) inside the Practice retainer without re-scoping. The fix is not refusing the work — it is expanding the retainer or selling a separate project alongside the Practice.

What SMB Leaders Should Know About AI Practice vs. AI Program Ownership

For SMB executives, the distinction is the answer to a question your team is probably asking quietly: *who owns AI here, us or the MSP?* The answer is: you own the Program, the MSP runs the Practice that supports it. Both roles are real.

What this means concretely:

You own the strategic outcomes. AI is your investment, your risk, your competitive advantage. Your executive sponsor signs the budget; your department heads commit owners; your employees do the work.
The MSP owns the operating discipline. The VCAIO shows up every month. The Council convenes. The QBR reports ROI. The AUP gets revised. The Continuous Scanner runs. The training ships.
The handshake is the Monthly AI Council. That is where the Practice and the Program meet — where the MSP's standardized motion produces decisions the client's leadership team commits to.

If your AI relationship feels confused — if you can't tell whether you or the MSP "owns" a particular decision — the fix is to articulate the boundary in the next Council. Most ambiguity disappears once Practice and Program are named separately.

How Lemhi Enforces the AI Practice and Program Boundary in MSP Delivery

Lemhi enforces the Practice/Program distinction inside the platform — which keeps MSPs profitable and clients aligned.

Standardized Practice artifacts. The Council agenda, the QBR AI Segment slides, the Maturity Score rubric, the AUP template — all standardized so every Practice ships the same way.
Engagement charter. Phase 0 produces a written charter that names the boundary between Practice and Program. Owners on both sides are documented at kickoff.
VCAIO authority levels. Operational, tactical, and strategic decisions are pre-delegated or escalated based on the charter — so the VCAIO never has to negotiate authority mid-conversation.
Scope-creep guardrails. When Program-side work emerges (custom agent builds, large training initiatives, departmental projects), the platform flags it as outside Practice scope and prompts a re-scope conversation.
Compass Module for the Practice/Program edge case. At very small clients, the Practice/Program distinction collapses into the owner-operator. The Compass Module is designed for that reality.

The distinction is not pedantic. It is the operating discipline that makes the difference between an MSP that scales AI revenue and one that bleeds margin on every engagement.

The Managed Intelligence Provider: What Comes After the MSP

Tue, 19 May 2026 01:55:53 GMT

The Managed Intelligence Provider (MIP): What Comes After the MSP

A Managed Intelligence Provider, or MIP, is the next-stage evolution of the MSP. The MIP owns the AI practice for every client at portfolio scale — operating Transformation as a Service, naming a VCAIO inside every relationship, running Monthly AI Councils, reporting AI ROI in every QBR. The MIP category is the natural successor to Managed Services and Managed Security: where the MSP owned uptime, and the MSSP owned security posture, the MIP owns the strategic intelligence layer.

How MSPs Become Managed Intelligence Providers in 2026

For MSPs, becoming a Managed Intelligence Provider is the maturation arc the channel has been working toward since AI demand became unavoidable. The MIP framing answers a question every MSP owner has asked: *what's our category in three years if AI is in everything?*

The arc tracks cleanly with prior generations of managed services.

Managed Services (2000s–present) owned uptime. The pitch was "we keep your IT running so you can focus on your business."
Managed Security (2010s–present) owned security posture. The pitch was "we manage your risk so you can focus on growth."
Managed Intelligence (2026 and forward) owns the AI practice. The pitch is "we own your AI transformation so you absorb AI into how work gets done."

Becoming a MIP is not about adding an AI line item to the SOW. It is about restructuring the business around three operating motions: TaaS as the commercial wrapper, the VCAIO as the named strategist, and the Monthly AI Council as the leadership room. That structural change is what produces the MIP outcome.

Why this matters for MSP economics:

Recurring revenue. TaaS retainers compound. Adoption-as-a-project does not.
Defensible client relationships. Once a VCAIO is named and the Council is on the calendar, displacement risk drops. The competing MSP has to displace a strategist, not just a vendor.
Higher contract value. MIPs sell at higher ACV than traditional MSPs because the practice carries more strategic weight inside the client's leadership team.
Talent retention. MSPs that name a VCAIO role create a career path that retains senior delivery talent. Talent that has nowhere to go inside the MSP eventually goes elsewhere.

The channel context: Pax8 Pulse research found 84% of SMBs trust their MSP to lead their AI direction. CRN reported Microsoft's Judson Althoff calling managed services the partners' AI "superpower." Lansweeper found 90% of MSPs say AI is vital but only 41% report meaningful integration. The market is ready for the MIP transition. The question is which MSPs move first.

Why SMB Buyers Should Look for a Managed Intelligence Provider

For SMB executives, the MIP distinction is most useful as a buying criterion. When you are evaluating an MSP for AI work, the question is no longer "do you support Copilot?" — every MSP does. The question is whether the MSP is operating as a Managed Intelligence Provider.

The MIP test fits on a short checklist:

Do they offer Transformation as a Service (or an equivalent monthly managed retainer that absorbs onboarding), or do they price AI as a fixed-fee project?
Do they name a VCAIO as part of the engagement, or is the AI conversation distributed across whoever picks up the phone?
Do they run a Monthly AI Council with your executive sponsor, or do AI decisions happen in IT tickets?
Do they report AI ROI in your QBR , or is AI invisible to your board?
Do they operate a Continuous Scanner against your M365 environment, or do they assess once a year and call it done?

If the answer to most of those is no, your MSP is still operating as a Managed Services Provider. That may be fine for your IT needs. It is not fine for your AI program.

How Lemhi Powers the Managed Intelligence Provider Transition

Lemhi is the operating-model SaaS that makes the MIP transition possible at portfolio scale. The platform was built specifically because the Lemhi founding team — 45+ years of combined MSP and SaaS experience across two prior MSP SaaS exits — kept seeing MSPs try to make the transition without the operating system underneath.

Lemhi Engage (GA June 2026) runs the Phase 0 sales motion that converts a traditional MSP-client AI conversation into a TaaS retainer. The output is a packaged proposal the MSP closes as Managed Intelligence revenue, not as a one-time consulting bill.
VCAIO tooling makes the named strategist role sustainable across 12–35 clients depending on engagement mix. Without automation, the VCAIO role does not scale.
Standardized Practice artifacts — Council agenda, QBR slides, Maturity Score, AUP template — mean every MIP engagement ships the same way. Standardization is what separates a practice from heroics.
Continuous Scanner maintains M365 hygiene continuously, surfacing findings to the PSA queue. The Scanner is what keeps the MIP relationship strategic instead of reactive.
Career path for VCAIOs. Because the platform handles the repetitive prep, MSP delivery talent can specialize on the parts of the role that compound — sponsor relationships, in-room judgment, use case sequencing.

The thesis is direct. The MSP category is moving toward Managed Intelligence whether or not individual MSPs make the move deliberately. Lemhi exists to make the deliberate move repeatable.

Why We Wrote an Open Source AI Framework For the Industry

Wed, 25 Mar 2026 00:15:59 GMT

Write about something you know. If you don’t know much about a specific topic that will interest your readers, invite an expert to write about it.

One of the reasons AI feels so messy in the MSP world is simple. There isn’t a real framework.

Not a shared one.

Not a practical one.

Not something people can actually ground decisions in.

What exists instead is a mix of vendor narratives, half‑borrowed security models, and a lot of well‑intentioned guesswork. Everyone is trying to build structure at the same time they’re trying to figure out what AI even is in their business.

That’s a hard way to operate.

Most of what I see today isn’t really a framework. It’s paperwork layered on top of uncertainty. An attempt to look organized before there’s anything stable underneath it.

And that’s not a knock on effort. It’s just what happens when there’s nothing solid to anchor to.

This is actually why I ended up writing the Lemhi AI framework at all

Not because I wanted to introduce another abstraction, but because there wasn’t one to start from. There was no common language. No baseline for what “good” even looked like. No way to evaluate tools without starting from scratch every time.

Everyone was picking tools first and trying to justify them later. That’s backwards.

Without a framework, every AI decision feels heavyweight. Every new tool creates debate. Every customer conversation turns into a custom explanation. And every internal discussion becomes philosophical instead of practical.

A real framework does the opposite.

It gives you a place to stand.

It makes tradeoffs obvious.

It lets you evaluate tools against something instead of reacting to them emotionally or defensively.

Once we accepted that a framework was needed, the next decision was obvious. It had to be open .

If this lived behind a product, a paywall, or a consulting engagement, it would immediately lose credibility. It would feel like positioning instead of structure. Another opinionated take instead of a shared starting point.

That was the opposite of the goal.

The intent here is not to “win” the AI framework debate. It is to start it and open it to the community.

Open source forces discipline. Anyone can inspect it. Anyone can challenge it. Anyone can fork it. If something does not hold up in the real world, it gets exposed quickly. That is a feature, not a risk.

It also keeps the framework honest. The moment it turns into a sales asset, it stops being useful as a control system. MSPs already have enough vendor shaped narratives telling them how AI should work. They do not need another one.

So we gave it to the community and have decided to own changes. My take on it? You do not need to believe everything in it. You just need a place to stand.

If you have spent time in cybersecurity, the structure will feel familiar. That is intentional.

CIS works not because it is perfect, but because it respects how organizations actually adopt things. It recognizes that maturity is staged. That not every control matters on day one. That sequencing matters more than ambition. AI adoption follows the same pattern.

There is a massive difference between “we are experimenting” and “this is now part of how work gets done.” Treating those two states the same is how organizations either freeze or move too fast.

So instead of inventing something new, we copied the part that already worked.

What can you expect?

Implementation Groups.

IG1: Baseline – What must exist before AI is considered real

IG2: Scale – What prevents drift as adoption grows

IG3: Advanced – What only matters once AI is embedded into sensitive workflows

This is not about slowing teams down. It is about giving them permission to start honestly where they are.

Pillars

Pillars are not categories for organization. Each one maps to a failure mode we kept seeing in real environments.

Most AI problems are predictable. Missing ownership. Unclear data boundaries. No visibility. No rollback path. Pillars force teams to confront the parts they usually assume away.

What Each Pillar Represents

Each pillar answers a different “what breaks if we ignore this” question:

Strategy & Buy‑In – Who owns AI and why it exists

Policy & Governance – What is allowed, what is not, and how exceptions work

Technical Readiness – Whether the environment can actually support AI

Process Mapping – Where AI fits into real work, not demos

Data Security & Tagging – What data AI can see and what it never should

AI Observability – Whether usage, cost, risk, and quality are visible

Copilot Readiness – How Microsoft Copilot expands safely and deliberately

AI Tooling & Deployment – How pilots become production without chaos

Skipping a pillar usually shows up later as noise, risk, or rework.

What’s Inside Each Control

Every control is written to be executable, not theoretical.

Each one includes:

A clear objective

A concrete requirement

A defined cadence

A named owner

Evidence you can actually produce

Controls are not pass or fail judgments. They are orientation points. They tell you what matters now, what can wait, and what you should not skip.

The point of the framework is simple.

AI should feel boring once it is working. Owned. Governed. Measured. Improved over time.

If it does not, something upstream is missing.

Most MSP Problems Aren’t Technical, and AI is the Least of IT All (In your customer’s mind...)

Wed, 18 Mar 2026 00:16:00 GMT

There are so many good reasons to communicate with site visitors. Tell them about sales and new products or update them with tips and information.

The more conversations I have with MSPs about monetizing AI, the less convinced I am that their biggest problems are technical.

They feel technical. They show up as tool debates, platform decisions, AI comparisons, and architecture questions. What bothers me is everyone treats this AI problem like an engineering problem that just need better tools.

But that’s not actually where things break down.

What I hear most often isn’t “this tool doesn’t work.”

It’s “we’re not sure what to use.”

Or “we’re still testing a few things.”

Or “we don’t really know how to talk about this with customers yet.”

That last part matters more than people want to admit.

A lot of this came up in recent coffee chats. Someone will say they’re looking at Copilot, but also using ChatGPT, and then another AI product their vendor just showed them. They’re trying to decide which one to standardize on, whether they should offer multiple options, or whether they should even be selling AI at all yet.

None of that is a technical limitation. It’s a clarity problem.

MSPs don’t lack tools. They lack conviction.

Every option sounds plausible, and in a lot of cases they ARE plausible. Every vendor has a story. Every demo works in isolation. And because everything might be important, nothing gets fully committed to.

So teams keep evaluating. They keep experimenting. They keep waiting for the moment when it all becomes obvious. And in the meantime, selling AI feels hard.

But that is absolutely not because customers don’t want it. But because MSPs don’t know how to explain it without talking tools. That’s the part I think a lot of people miss. Selling AI isn’t hard because the technology is complex... iIt’s hard because the narrative is unsettled.

If you’re not clear on what AI is in your stack, what problem it actually solves, and where its limits are, then every sales conversation turns into a ramble. You hedge. You over‑qualify. You list tools instead of outcomes.

Customers feel that.

And when the story isn’t clear, trust doesn’t form. Deals stall. AI gets positioned as “interesting” instead of “necessary,” or they go on their own way and solve their own problems. AI isn’t a particularly difficult one to self-service, so that’s the path of least resistance.

That’s not a sales failure. That’s a prioritization failure.

Most MSPs don’t need better pitch decks or smarter demos... They need stronger filters.

They need to decide what they believe. They need a default answer. They need to say no to a lot of things so the yes actually means something.

That’s where ecosystems start to matter.

An ecosystem reduces choice. It forces consistency. It gives your team shared language instead of a dozen different explanations depending on which tool someone last tried.

When the internal story stabilizes, selling gets easier. Not slicker just clearer. Most MSP problems aren’t technical. And most MSP AI sales problems aren’t either. They’re narrative problems. They’re clarity problems. They’re commitment problems.

AI didn’t create that. It just exposed it. And until that’s addressed, no new tool is going to make AI easier to sell. It’ll just add another option to an already crowded list.

This is where my skepticism keeps leading me.

The real question isn’t how do we sell AI? It’s what are we actually willing to stand behind?

Once that’s clear, the rest starts to quiet down.

In an AI World Full of Noise, I’m Being Skeptical on Purpose

Wed, 11 Mar 2026 00:16:00 GMT

The new season is a great reason to make and keep resolutions. Whether it’s eating right or cleaning out the garage, here are some tips for making and keeping resolutions.

I’ve been labeled skeptical a bit recently around AI. I’m fine with that.

But it’s worth saying what that skepticism actually is (and what it isn’t).

I’m not anti‑innovation. I build things for a living. I like new ideas. I like progress. I like when technology actually moves the ball forward. What I’m against is noise. And right now, MSPs are drowning in it.

Every cycle brings a new framework, a new model, a new set of tools, a new abstraction layer that promises to “change everything.” The language is confident. The diagrams are clean. The demos are impressive. And yet, when you step back, a lot of it doesn’t survive contact with reality.

That’s where my skepticism comes from. I’ve been in this space for 15+ years. I know what the beginning of a cycle looks like. It often follows the same patterns and the same experimentation. And for a decade and a half, it’s mostly landed in the same ending position, whether cloud, cyber, or AI: repeatable and monetizable at scale.

I don’t start by believing vendors. I don’t start by assuming the abstraction is necessary. I don’t start by trusting that because something is popular, it’s useful. I start by asking a much more boring question: Does this actually hold up when you try to run it, scale it, support it, and charge money for it?

Most things don’t fail because they’re bad ideas. They fail because they’re fragile, expensive, hard to explain, or impossible to operationalize. Or they only work under perfect conditions that never exist outside a demo. And in today’s era, with the break neck speed that things get done, none of that is acceptable.

I think a lot of people miss this. Especially in AI.

There’s a temptation to treat intelligence as magic instead of infrastructure. To stack more layers, more orchestration, more cleverness on top, and assume value will appear. But intelligence that can’t be repeated, governed, or monetized isn’t progress. It’s a science project, and its irresponsible if you’re doing it in your customers environments.

Healthy skepticism is how you protect yourself from that.

It forces you to slow down and separate what’s interesting from what’s durable. What sounds smart from what actually compounds. What helps one team ship a demo from what helps an organization operate at scale. And it’s the exercise you need to stop feeling so overwhelmed with the noise.

This is where my head goes with it: if something can’t be explained simply, deployed repeatdely, and improved incrementally, it’s probably not ready.

So yes, I’m skeptical. On purpose. Because skepticism is how you calm the noise. And once the noise is gone, the real work can start.

Look forward to sharing more of my learnings soon! (They’ll be a little less skeptical, I promise)

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Implementation Groups.

Pillars

What Each Pillar Represents

What’s Inside Each Control

Most MSP Problems Aren’t Technical, and AI is the Least of IT All (In your customer’s mind...)

In an AI World Full of Noise, I’m Being Skeptical on Purpose

A Guide for MSPs