Channel Bypassed
Large language models are transforming online search, replacing traditional engines as users rely more on AI answers. Recent studies show that when AI summaries appear in results, clicks on ranked websites drop by 47%, with some publishers seeing an 89% decrease in click-through rates.
The same kind of shift is showing up in route to market. The Global Technology Distribution Council’s report shows IT distributors are crucial in selling cloud and AI services. It states 80% of vendor leaders see partner ecosystem growth as key to market success, while 86% are using or testing digital platforms. The report also notes 60% of vendors have direct ties with hyperscalers, and 40% use a hybrid model with distributors.
From The Information, Anthropic is currently in discussions with private equity firms, including Blackstone and Hellman & Friedman, to establish a joint venture focused on artificial intelligence consulting. It’s not the only example — OpenAI has already partnered with Thrive Holdings and Shield Technology Partners to drive AI deployment across their entire portfolios.
Delivery Commoditized
This isn’t three separate developments. It’s the same economic logic showing up in different layers of the market.
Distribution isn’t fulfillment; it’s policy, packaging, financing, and marketplace placement. Digital partner platforms turn selling into routing: who gets surfaced, who gets attached, who gets paid.
PE doesn’t buy tools. PE buys repeatable operating models—and they prefer standardization across portfolios, which turns service providers into interchangeable delivery.
That matters because the more labor-constrained the market becomes, the more valuable standardized delivery and controlled buying paths become.
A British Chambers of Commerce survey finds 72% of UK firms see labour costs as the main expense, worsened by an aging population and a projected 30% decline in the working-age population by 2060. Consequently, automation becomes essential, with 38% citing labour shortages as a growth barrier, leading firms to pursue cost-effective automation to stay competitive.
When labor is the constraint, buyers don’t want tools; they want fewer humans required per outcome.
AI is becoming operational infrastructure, not just a helper. Hewlett Packard Enterprise is exploring AI to improve IT management by creating models that detect silent failures before outages. An outage can cost at least $4,000 per minute, with partial failures causing greater losses. Their whitepaper, “Beyond the Noise: Toward a Self-Healing Autonomous IT,” highlights benefits of IT-optimized models for a self-healing environment. Automation changes the production function—but trust and governance determine how fast it gets adopted.
MSPs Left Holding
Artificial intelligence is already moving into live SMB operations, with 53% of small and midsize businesses using AI and another 29% planning to within the next year, according to SMB Group. That means MSPs will be dealing with AI in client environments now, not later.
A recent survey by Pegasystems reveals that nearly two-thirds of consumers lack confidence in how companies utilize generative artificial intelligence for customer interactions. This sentiment is echoed by the fact that 46% of respondents believe AI-driven customer service rarely results in successful outcomes. According to the same survey, over 75% of consumers report that dealing with a human representative often leads to better outcomes, while only 2% favor interacting exclusively with AI chatbots. When trust is weak, someone has to own the fallback: escalation paths, human handoff, and the cleanup when automation fails.
A recent report by Atlassian found that while workers using generative artificial intelligence are experiencing a 33% boost in productivity, only 3% of organizations report seeing tangible business transformation. The work may get faster, but the business is not being redesigned at the same pace. Nearly 40% of respondents to ECI Software Solutions survey said they have not seen measurable results from AI initiatives.
And the operational burden lands on already strained teams. Glassdoor reports tech employee confidence fell 7.1% over the past year, while Talker Research says 41% of employees are experiencing burnout.
That leaves service providers absorbing the support load, governance burden, and expectation management even when they do not control the platform or capture the premium margin.
Job replacement can take decades. Margin replacement doesn’t. The channel reorganizes first, and delivery gets repriced after.
AI adoption is arriving faster than trust, process redesign, or measurable business value, so MSPs risk carrying the operational mess while margin shifts to whoever owns the interface, marketplace, or playbook.
Why do we care?
The 3% transformation figure — only 3% of organizations seeing tangible business transformation despite a 33% productivity boost — that’s not a rounding error. That’s the entire market failure of the current AI deployment wave in one number. Tools are being adopted. Processes are not being redesigned. And the organizations who are going to close that gap are not MSPs with vendor certifications — they’re PE-backed consulting vehicles explicitly modeled on Palantir, moving into portfolio companies through the Anthropic joint venture structure. They are industrializing the transformation layer.
Meanwhile, the distribution platform data tells you that route-to-market is already shifting to whoever controls the digital platform — 86% of vendors are on or testing these systems. That means your visibility, your attach rates, your margin — increasingly determined by platform logic you don’t own. The click collapse in search is the same mechanism: the interface captures the value, the producer becomes an input. If you’re a reseller dependent on distributor surfacing, you are the producer in that equation.
The consumer trust gap — 75% still preferring human representatives — is not a reason to slow down. It’s a reason to own the governance wrapper before someone else standardizes it. The MSP who builds repeatable AI governance frameworks owns the adoption throttle. The MSP who waits for PE firms to package it into a portfolio playbook will buy it back as a product.
AI is not a product category. AI is reorganizing who controls the interface between capability and outcome — and that’s the position worth fighting for.
What to Consider
- Reposition the 3% gap as your sales entry point. When engaging SMB clients already using AI tools, lead with the documented reality that productivity gains rarely produce business transformation without process redesign. That redesign engagement is billable, defensible, and not yet commoditized by PE playbooks at the SMB scale.
- Do not lead with AI-driven customer service automation for client-facing workflows. Consumer trust data is unambiguous — 75% prefer human representatives, 46% report AI customer service rarely succeeds. Deploying this prematurely creates client-side liability and damages your credibility as an outcomes-focused advisor.
- Develop a governance and “safe wrapper” practice before PE firms standardize it. The trust gap in AI adoption is real and documented. MSPs who build repeatable governance frameworks — data handling, AI output validation, human escalation protocols — own the adoption throttle. That is a margin-bearing position. Wait too long and it becomes a line item in a PE firm’s portfolio playbook. Call it An AI Governance Control Pack: data boundaries, output validation, logging, escalation design, and a quarterly model-risk review.
If this trend continues, the dominant MSP model becomes subcontracted delivery under distributor-, hyperscaler-, or PE-owned AI operating frameworks, and independent MSPs that don’t own a governance/IP wedge will see their gross margin reset to implementation labor rates.