Business of Tech: Daily MSP News & IT Services Analysis

A structural margin pressure is emerging as AI manufacturers bill MSPs for agent activity, while MSPs are paid by clients for resolved outcomes. Companies like ConnectWise and Super IT charge based on volumes of AI-driven attempts (agent activity, token usage), creating a financial disconnect—since MSP contracts tie revenue to tickets actually solved and kept closed, not the process steps taken.

ConnectWise introduced an RMM agent that automates diagnostics and ticket triage within its platform. Company-supplied data claims substantial efficiency gains: for one partner, 99% of over 7,000 tickets were evaluated in 30 days, but these metrics center on activity and hours "handed back," not the net number of tickets resolved. Shield Technology Partners’ analysis of its Forge platform found that higher pass rates from advanced models like GPT-6 increased the per-ticket cost, and that calculations often did not include the human review and correction required by agents’ errors.

Further developments reinforce this trend. Super IT’s North America plans charge for AI agent activity levels, and Cloudera’s industry survey found only 27% of enterprises can accurately measure AI spend, with just 23% reporting net-positive value. Vendor-reported metrics prioritize activity and recurring revenue, not actual outcome delivery—leaving MSPs to bear the impact of unresolved tickets on their margins.

The operational consequence is that MSPs must track the actual cost per resolved ticket—including failed agent attempts and human verification—to accurately set pricing models and protect profitability. Not accounting for this hidden cost can erode margins and obscure the real impact of automation initiatives. Accurate governance relies on updating pricing models and operational processes to reflect the full scope of AI-related costs and investments.

00:00 The Agent Is in the Ticket

03:45 Billed for the Try, Not the Fix

06:40 What a Resolved Ticket Costs

11:14 Why Do We Care?

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About Business of Tech: The daily MSP news and analysis podcast for managed service providers and IT services leaders. Host Dave Sobel covers AI for MSPs, MSP pricing and profitability, MSP M&A and private equity, vendor partner programs, MSP cybersecurity and liability, and MSP operations — and answers the question every story raises: Why do we care? Produced by MSP Radio.

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The episode centers on a structural shift in technology risk management: liability for AI decisions is increasingly transferring downstream from technology vendors to MSPs and their clients. Insurers are moving to exclude generative AI from standard liability policies, while California regulators have mandated new automated decision-making rules with specific risk assessment and audit requirements for certain businesses operating above defined thresholds. Product licensing from major AI manufacturers such as OpenAI and Anthropic continues to disclaim liability, relying instead on "as is" terms that put post-deployment legal exposure on service providers and end users.

The most consequential development discussed is the combination of insurance market adaptation and new regulatory mandates. Insurers are segmenting their approach: some explicitly exclude AI coverage via policy riders, others remain silent (neither excluding nor affirming coverage), and a minority price for AI-related risks. According to Bernhard Kirchner, silent coverage offers limited protection and will not shield MSPs from liability unless operators can demonstrate robust risk assessment and control activities. Simultaneously, under California regulations effective January 1, MSPs supporting clients above $26.25 million in California sales or with over 100,000 California clients are obligated to generate and retain evidence-based AI risk assessments, updating these every three years or upon substantive change.

Several supporting developments reinforce this liability transfer and governance gap. ISO has released optional generative AI exclusions for general liability, and specific carriers, such as Berkley, have implemented absolute exclusions across multiple product lines. Kirchner notes that the lack of clarity in many insurance contracts creates further exposure, as legal precedents—including the Air Canada chatbot case—suggest that courts treat AI as an agent of the business deploying it, not a standalone legal entity. Evidence and documentation requirements, including technical evaluations and governance records, are beginning to emerge as practical minimums for qualifying for any future AI coverage.

The implications for MSPs and IT service providers are a marked increase in contractual, regulatory, and operational responsibility related to managing AI risk. MSPs working with affected clients must closely audit their customer footprint, update risk documentation, and explicitly clarify risk allocation in contracts and master service agreements. Lacking proactive compliance or documented controls, both MSPs and clients risk significant uninsured exposure, with limited recourse to recover from AI failures. Operational safeguards—such as periodic self-assessment, updated evidence retention, and clear assignment of liability—are emerging as necessary components for both competitive differentiation and risk management.

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About Business of Tech: The daily MSP news and analysis podcast for managed service providers and IT services leaders. Host Dave Sobel covers AI for MSPs, MSP pricing and profitability, MSP M&A and private equity, vendor partner programs, MSP cybersecurity and liability, and MSP operations — and answers the question every story raises: Why do we care? Produced by MSP Radio.

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The episode identifies a structural shift: traditional patching is no longer sufficient protection as the act of releasing a patch now informs and accelerates attackers, particularly with the use of AI. When vendors publish fixes, attackers can promptly identify and exploit not-yet-patched systems at scale. Companies and data sources underlying this shift include OpenAI, Microsoft, CISA, and the Verizon 2026 Data Breach Investigations Report.

The most consequential development is Verizon’s finding that vulnerability exploitation now leads breach causes at 31%, overtaking credential theft and phishing. The percentage of fully remediated exploited flaws has declined to 26%, and the average time to fix has increased. Notably, 263,000 servers in the U.S. still run unsupported PHP, and coordinated AI-driven attacks have reduced attacker dwell time to hours in real-world incidents like the ConnectWise breach.

Vendor responses illustrate the risk landscape. N-able has launched AI-assisted patching to speed remediation and scale endpoint management, but its patching platform was itself compromised. Industry observers such as Krugman and Thompson foresee a move away from open internet-facing systems toward closed or gated platforms, accepting slower workflows as a tradeoff for reduced exposure.

For MSPs and IT service providers, the implication is clear: the key operational metric is not patching speed or compliance, but minimizing clients’ internet-exposed assets. Providers should classify all client-facing resources, move critical services into platforms with pre-release patching or behind access controls, and focus quarterly reporting on reducing the attackable surface. Patching remains a backstop, but accountability shifts to exposure reduction as both attackers and defenders automate.

00:00 We Can’t Patch All the Bugs

03:39 Every Fix Starts the Race

05:53 Run Faster or Leave the Race

09:33 Why Do We Care?

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About Business of Tech: The daily MSP news and analysis podcast for managed service providers and IT services leaders. Host Dave Sobel covers AI for MSPs, MSP pricing and profitability, MSP M&A and private equity, vendor partner programs, MSP cybersecurity and liability, and MSP operations — and answers the question every story raises: Why do we care? Produced by MSP Radio.

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The episode highlights the increasing shift of operational risk and accountability to MSPs as AI-powered agents become integrated into client environments through white-label and usage-based models. This shift is illustrated by Pax8’s introduction of 8Bot, an AI coworker that MSPs can deploy and manage for their clients, alongside offerings from vendors such as NeoAgent, IgnitHQ, and Roost. These platforms enable rapid deployment and automation but also increase the burden on service providers to oversee AI behavior, manage variable billing, and maintain service consistency.

Key evidence comes from Pax8’s strategy for 8Bot, which is designed for partner-led deployment and management. As of the latest announcement, 8Bot is in limited early access, with general availability planned for early 2027. Its pricing model has not yet been fully announced. Other vendors illustrate the range of emerging approaches: NeoAgent offers automated service desk plans based on ticket volume, while IgnitHQ reports that approximately 25% of its partners allow its AI engine to close tickets autonomously. These examples demonstrate the shift toward consumption-based pricing and delegated autonomy, with potential margin and operational risks for MSPs.

Secondary developments reinforce this trend. Flamingo advertises pricing starting at $1 per device per month, including an allocation of AI tokens, with additional token consumption potentially affecting costs. Meanwhile, Roost, Rewst, and TD SYNNEX have highlighted the rapid adoption of direct chatbot and MCP (Model Context Protocol) integrations, allowing MSPs to initiate automation through AI assistants rather than relying exclusively on traditional software interfaces. Rewst, for example, reported that approximately 80% of automations created by its early-access group were initiated through AI models such as Claude or ChatGPT. This shift raises questions about how vendors maintain control and accountability when AI systems increasingly initiate workflows.

For MSPs and IT leaders, these developments introduce new contractual, billing, and liability risks. Providers must monitor AI usage and understand variable costs to avoid unexpected charges that could erode margins if they are not properly accounted for in client agreements. As vendors move toward AI-driven automation and more direct integrations, service providers must establish appropriate governance, auditing, and accountability measures while maintaining processes that can be reviewed and verified. Effective adoption will require investment in process management, clear responsibility boundaries, and billing transparency rather than reliance on vendor defaults or unattended automation.

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About Business of Tech: The daily MSP news and analysis podcast for managed service providers and IT services leaders. Host Dave Sobel covers AI for MSPs, MSP pricing and profitability, MSP M&A and private equity, vendor partner programs, MSP cybersecurity and liability, and MSP operations — and answers the question every story raises: Why do we care? Produced by MSP Radio.

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The episode documents a structural shift in the managed IT services sector: automation and AI are displacing both entry-level and advanced technical work, eroding the traditional skills ladder within MSP operations. This transformation is illustrated by platforms such as IgnitHQ and Rewst, which deploy AI-driven automation that closes support tickets and builds workflows, and by vendors like Pax8 and Automated Edge, which commercialize senior-level AI expertise as a service. Industry frameworks, such as Forrester’s analysis and CompTIA’s response, confirm broad disruption of established staffing and skill development models.

IgnitHQ reports that one-quarter of its partners now allow AI to close support tickets without technician input, while ServiceNow’s platform, Thrive, claims a reduction of engineer connection time from 6 minutes to under 1, with a significant volume of engineer hours transferred to higher-level work. Rewst found that 80% of automations from its early adopters are being generated inside chatbots rather than directly by technicians. According to CEO Aharon Chernin and other quoted executives, this means that "the MSP isn't using the product—Claude is." KPMG’s CEO survey indicates that large enterprises are actively redesigning roles and career paths to integrate AI, whereas in smaller providers, change is occurring by default, not design.

Secondary developments confirm this displacement from both ends of the skills spectrum: CompTIA is poised to launch an AgentOps+ certification for AI agent governance, but acknowledges current gaps in defining responsible practices. Simultaneously, the practice of renting out high-level AI operations from vendors such as Automated Edge is becoming common, with knowledge and skill-building bypassed in favor of operational outsourcing. The episode also recounts Thrive reallocating 32 engineers out of Level 1 roles after AI assumed intake responsibilities, demonstrating deliberate rewriting of job structures in response to automation.

For MSPs and IT leaders, the operational implication is a growing dependence on vendor-driven automation paired with a significant governance gap in staff development. Without deliberate intervention—such as reserving certain ticket types for junior staff, incorporating knowledge transfer provisions in vendor contracts, and explicitly writing new “middle” roles—technical acumen and organizational memory risk being externalized to vendors or lost entirely. The episode emphasizes that this is a risk not just to service quality or efficiency, but to the long-term viability and ownership of technical expertise within services organizations.

00:00 AI Closes the Tickets Techs Learned On

03:53 Renting Skips the Apprenticeship

06:31 The Missing Middle Job

10:23 Why Do We Care?

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About Business of Tech: The daily MSP news and analysis podcast for managed service providers and IT services leaders. Host Dave Sobel covers AI for MSPs, MSP pricing and profitability, MSP M&A and private equity, vendor partner programs, MSP cybersecurity and liability, and MSP operations — and answers the question every story raises: Why do we care? Produced by MSP Radio.

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The episode exposes a governance gap in risk management and asset responsibility for internet-facing systems—especially websites—within managed service agreements. This gap arises from unclear contractual boundaries between MSPs, clients, web developers, and hosting providers, leaving vulnerabilities unaddressed when major infrastructure flaws appear. The PHP vulnerability incident underscores how lack of clearly assigned accountability can delay or prevent timely remediation.

Two severe flaws in PHP, present since version 5 and with a severity of 9.2/10, affected around 70% of websites using a server-side language, as reported by The Verge and Cloudflare. Exploits began before official patches or public disclosure, and fixes were not available via automatic updates but required manual intervention by the relevant party—MSP, host, or support vendor. Within hours of disclosure, millions of attack attempts were detected, resulting in confirmed breaches on unpatched systems.

Further risk is shown by the high prevalence of out-of-support PHP deployments: Perforce estimates 40% of users run unsupported versions, for which patches are only available if prior arrangements exist with Linux distributions or support vendors like Zend. Regulatory guidance and industry analysis highlight insufficient coordination and communication, with many organizations lacking designated patch liaisons or clarity on who is responsible for updates.

For MSPs and IT leaders, the implication is clear: inventory all client-facing systems, explicitly document who is responsible for maintenance and patches, and ensure clients are aware of the risks for systems not under direct management. Without such steps, coverage gaps persist, exposing both providers and clients to avoidable incidents and delayed responses due to unclear accountability.

00:00 The Day Websites Went Dark

03:24 The Patch Was Never the Scarce Part

05:37 The Website Outside the Contract

08:17 Why Do We Care?

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About Business of Tech: The daily MSP news and analysis podcast for managed service providers and IT services leaders. Host Dave Sobel covers AI for MSPs, MSP pricing and profitability, MSP M&A and private equity, vendor partner programs, MSP cybersecurity and liability, and MSP operations — and answers the question every story raises: Why do we care? Produced by MSP Radio.

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The integration of generative AI into small business environments is driving a shift toward localized infrastructure management. As staff members frequently input sensitive business data into third-party, free-tier chatbot services, a governance gap is emerging. To address this, providers are moving away from direct, open connections to public AI models and toward the implementation of orchestration servers and database systems that reside within the client's network to maintain control and security.

Evidence suggests that shadow AI usage is pervasive across client environments. Assessments performed using desktop agents indicate that employees are already utilizing AI tools for daily tasks, often without clear permission or security oversight. Data collection during these assessments is typically isolated to a single, temporary database per engagement, which is then destroyed to minimize liability. This process is intended to provide visibility into usage patterns and potential risks before determining if a more permanent, integrated AI service is required.

Secondary developments include the emerging role of the Managed AI Services Provider (MAISP). These providers focus on putting security and permission-based guardrails around AI interactions. By implementing an orchestration layer, these services can filter requests, determine which data is safe to share with Large Language Models (LLMs), and verify user permissions based on existing structures like Active Directory. This model mimics traditional IT service delivery, treating AI as another infrastructure component that requires assessment, configuration, and maintenance.

The shift toward managed AI carries significant operational implications for MSPs regarding liability and contract structure. When providing AI services, MSPs must assume responsibility for the security of the data collected and the systems deployed, mirroring existing backup and disaster recovery obligations. This requires shifting from a model of connecting clients directly to public AI tools to a model of providing a secure, internal, and orchestrated environment. Failure to establish these governance frameworks risks exposing client business data and assumes potential liability for unauthorized data usage.

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About Business of Tech: The daily MSP news and analysis podcast for managed service providers and IT services leaders. Host Dave Sobel covers AI for MSPs, MSP pricing and profitability, MSP M&A and private equity, vendor partner programs, MSP cybersecurity and liability, and MSP operations — and answers the question every story raises: Why do we care? Produced by MSP Radio.

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The primary structural trend is the shift from reactive auditing of AI agent actions to proactive, policy-driven control over what AI agents are allowed to do within IT environments. This is illustrated by companies like Charles Schwab and JPMorgan Chase, which have established specific limits on agent actions rather than broad bans, and by technology providers such as Anthropic and Microsoft introducing tools to enforce granular permissions before AI agents are deployed.

Evidence underscores this shift: Acronis found AI agent tools on 9.4% of monitored Windows machines, with higher adoption among MSP-managed clients. Financial institutions are restricting critical actions—Charles Schwab only permits limited AI agent access through controlled servers and explicitly blocks agents from moving money, while JPMorgan Chase prohibits external AI agents altogether. OpenAI’s disclosures of agents circumventing safeguards led to increased automated oversight. Microsoft now contains agent permissions dynamically, citing the inherent risk in delegating broad privileges to AI.

Supporting developments include reports that less than 3% of legitimate agent activity can be reliably identified, highlighting the difficulty of external oversight. Tools like ThreatLocker’s Zero Trust controls and 1Password’s credential policies show vendors are responding with more rigorous permission and approval frameworks, but these solutions still require explicit configuration and maintenance by service providers.

For MSPs and IT service providers, this trend increases both governance obligations and operational workload. Action policies must be created, updated, and monitored for every client and internally for provider systems. The work of defining and maintaining these policies affects margins unless priced explicitly. Approval fatigue is another risk, as excessive prompts can reduce the effectiveness of human oversight. Providers should anticipate that clients will expect AI usage policies to be a formal part of agreements, requiring new approaches to contractual and operational management.

00:00 Banks and Sites Push Back on AI Agents

03:50 Why Watching Agents Afterward Fails

06:43 The Permission Layer Needs a Policy

09:41 Why Do We Care?

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About Business of Tech: The daily MSP news and analysis podcast for managed service providers and IT services leaders. Host Dave Sobel covers AI for MSPs, MSP pricing and profitability, MSP M&A and private equity, vendor partner programs, MSP cybersecurity and liability, and MSP operations — and answers the question every story raises: Why do we care? Produced by MSP Radio.

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This episode outlines a structural shift whereby accountability for AI results is increasingly being transferred from customer leadership to Managed Service Providers (MSPs) and IT service providers. This transfer is being accelerated by internal pressure on client organizations to report AI success while reliable performance metrics remain elusive. Evidence from Futurum Group’s “The New AI Workforce” report and actions by vendors such as Dell and Accenture demonstrate how responsibility and reporting for AI are consolidating under technical teams, often with insufficient checks and unclear governance boundaries.

Concrete findings show a notable gap between reported and actual AI outcomes. Among 775 surveyed technology leaders, 30% experience pressure to overstate AI success, yet only 27% observe substantial ROI. Rather than increased IT budgets, spending is being reallocated internally toward AI projects, raising the stakes for providers to demonstrate credible impact. Technical leadership—notably the CIO, CTO, or CISO—now often sets AI strategy and evaluates results, centralizing both decisions and oversight.

Supportive details include a growing operational reliance on, rather than replacement of, technology staff. While 58% of respondents expect headcount reductions over three years due to AI, only 14% report layoffs so far, with most changes oriented around re-skilling and role shifts. Only 4% rate their teams as fully prepared for AI-first environments. Verification demands are rising, often falling on MSPs, while governance remains underdeveloped, presenting both an opportunity and a liability risk as clients may try to offload oversight.

MSPs and IT service providers face elevated risks surrounding contract terms, outcome measurement, and governance. They must carefully define and document project milestones, clarify roles in outcome-based agreements, and ensure governance functions do not outpace their actual control. Investment in upskilling is essential, particularly for smaller providers lacking the resources of larger competitors. Providers should assess where they add demonstrable value in the AI supply chain and align accountability structure accordingly.

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About Business of Tech: The daily MSP news and analysis podcast for managed service providers and IT services leaders. Host Dave Sobel covers AI for MSPs, MSP pricing and profitability, MSP M&A and private equity, vendor partner programs, MSP cybersecurity and liability, and MSP operations — and answers the question every story raises: Why do we care? Produced by MSP Radio.

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The episode highlights a structural shift in vulnerability management, where accelerated discovery of software flaws—driven by AI—has outpaced the ability to remediate. Companies including Microsoft and research groups like Epoch AI are reporting record vulnerabilities, with Microsoft fixing 1,411 flaws in a single Patch Tuesday and July seeing over 2,500 high or critical flaws disclosed sector-wide, according to Epoch AI. This growth is prompting a move away from traditional policies based solely on severity scoring.

Current evidence shows a widening gap between the volume of disclosed vulnerabilities and those actually exploited. While Anthropic’s AI identified more than 10,000 serious flaws, the CISA Known Exploited Vulnerabilities catalog, which focuses on proven exploitation, is growing at a slower rate. CISA’s updated directives now prioritize remediation by exposure and exploitation evidence, shifting away from blanket criticality.

For MSPs and IT providers, this creates operational strain: Nerdio’s survey indicates 60% see management complexity surpassing their ability to scale, with 29% affected by burnout-driven attrition. Large enterprises can add headcount to tackle volume, but most MSPs cannot match that response.

The implication for MSPs is clear: outdated patch policies promising to fix all critical flaws are unsustainable. Operational focus must shift to active risk triage, dynamic queue management, and transparency with clients—prioritizing remediations based on real-world exploitation and exposure, and maintaining explicit records about deferred vulnerabilities as client and regulator scrutiny rises.

00:00 Patch Tuesday Breaks a Record

03:52 Severity Stopped Sorting the Work

06:50 When the Queue Outgrows the Team

10:24 Why Do We Care?

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The episode details an ongoing shift toward upstream platform control and infrastructure dependence in the managed IT services sector. Centralized platforms, such as Pax8’s 8OS and governance initiatives from the Channel Trade Association (GTIA), increasingly control AI, access rights, consent, and management tooling, concentrating privileged identities and access points within vendor environments rather than those of MSPs or clients.

A key case highlighting the risk is the ConnectWise Cloud Backup incident, where one credential enabled access to 173 Microsoft 365 tenants in two hours. This credential, solely managed by the vendor, had broad, ongoing access, and MSPs lacked independent means to audit or disable the access. The breach led ConnectWise to shut down related services for all partners while investigating, demonstrating both the operational risk of centralized credentials and the limits of vendor-based governance.

Supporting developments reinforce this centralization. Pax8’s marketplace approach to AI management and the GTIA-led Managed Intelligence Alliance aim to standardize and certify managed AI, but with rules and control largely remaining with upstream platforms. Other products, such as GoTo’s LogMeIn Resolve, are also embedding privileged AI automation, expanding vendor-side access and responsibility.

For MSPs and IT leaders, the practical implications include increased operational risk and dependency on platform vendors for identity, consent, and logging. Effective risk reduction now requires maintaining inventories of privileged applications, verifying credential custody, keeping independent audit logs, and preparing for rapid manual deactivation if needed. Governance and contracts must reflect the reality that vendor breaches or failures can have wide-reaching, simultaneous impact across client environments.

00:00 Managed AI Moves Upstream

04:30 One Vendor Key, 173 Tenants

07:28 AI Agents Multiply the Keys

10:39 Why Do We Care?

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A persistent governance gap is emerging in organizational AI implementations, especially among clients of MSPs. Despite the integration of AI in regulated sectors like wealth management and construction, most MSPs are neither engaged at project inception nor maintain oversight through formal policies or audit-ready documentation. Specific examples include deployments using technologies such as Claude, where neither roles for approving AI configurations nor processes for tracking data access are uniformly established.

A key development is the insufficiency of existing evidence and controls around AI use. Fewer than 20% of current client projects have written AI policies, and among 53 surveyed family offices, 80% are using AI without documented policy guidance. Insurers have started to introduce generative AI exclusions (e.g., CG4047), shifting the liability and evidentiary burden onto clients and their MSPs. Auditors now require dated, traceable documentation demonstrating that controls align with enacted policy, especially when AI accesses sensitive platforms like Microsoft 365.

Related discussions further illustrate the depth of this issue. The lack of clarity around who is authorized to approve or modify AI settings creates gaps in accountability. There are technical controls, such as independent validation engines, that can halt operations on data mismatch, but many MSPs rely on vendor-provided logs and have not established consistent evidence retention or incident management processes.

For MSPs and IT leaders, the practical effect is heightened risk due to poor documentation, undefined responsibilities, and growing insurance exclusions. Future operational resilience depends on developing and enforcing AI governance—restricting unauthorized data flows, fully recording system activity, and regularly reviewing user privileges. These practices are becoming core requirements for maintaining compliance and managing liability in client environments where AI is deployed.

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About Business of Tech: The daily MSP news and analysis podcast for managed service providers and IT services leaders. Host Dave Sobel covers AI for MSPs, MSP pricing and profitability, MSP M&A and private equity, vendor partner programs, MSP cybersecurity and liability, and MSP operations — and answers the question every story raises: Why do we care? Produced by MSP Radio.

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AI service pricing for managed IT services is increasingly unstable due to manufacturers’ ability to change subscription deliverables and costs unilaterally. OpenAI’s reduction of its $200 plan benefits and the shifting terms in leading AI offerings, noted by SemiAnalysis and Omdia’s analysts, exemplify how MSPs risk absorbing margin losses when vendor pricing changes are passed downstream with little notice or warning.

SemiAnalysis found that leading AI subscription services are often subsidized, using more infrastructure than they generate in direct revenue, with Gartner projecting a $5 trillion gap between required AI infrastructure investment and realistic revenue through 2030. The sustainability of current AI pricing is questionable, and over 80% of smaller AI cloud providers are expected to lose independence in this period. These developments elevate margin and continuity risks throughout the services ecosystem.

Market data from Omdia indicates only 20% of North American partners have the maturity to productively monetize AI services, while most AI work remains experimental or internally focused. Some MSPs have bundled AI into flat fees without contract triggers for cost changes, exacerbating the risk. Techaisle’s data shows buyers’ top frustration is “bill shock” as token-based AI pricing remains volatile and unpredictable.

For MSPs and IT leaders, the implication is growing vendor dependency and contract risk. Flat-fee AI bundles expose service providers to sudden margin erosion if manufacturers alter price or service conditions. Practical risk mitigation includes writing re-opener clauses and escalation triggers into contracts, charging separately for data readiness, and avoiding open-ended pilots. The benchmark should focus on contractual flexibility and risk allocation, not initial price alone.

00:00 AI Services Grow, Prices Don’t Settle

03:38 Why Today’s AI Price Is Borrowed

06:43 Price It Now, Write It to Reopen

10:00 Why Do We Care?

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The episode highlights a structural split within the managed services market, defined by how different segments are responding to rising economic uncertainty, AI experimentation, and shifting revenue expectations. Omdia’s recent partner sentiment polls reveal a divergence: approximately 44% of channel partners expect their revenue to fall, while 41% anticipate growth—signaling a bifurcation rather than a broad recession. The episode identifies this as a period of market stress with implications similar to the post-2008 transition from break-fix to recurring revenue, but now driven by new pressures and emerging technologies such as AI.

Key evidence centers on AI-related services, where Omdia reported around 60% growth, concentrated among a minority of advanced MSPs, compared to roughly 10% for managed services overall. This growth remains experimental, with most providers currently using AI to drive internal efficiencies rather than as client-facing offerings. The ongoing uncertainty around how AI capabilities will be priced and delivered through the MSP channel mirrors previous transitions—such as the shift to cloud licensing—where price-setting power and business models were contested between vendors and service providers.

Supporting developments discussed include new funding models entering the acquisition market, such as E4N’s family office–backed approach, as well as ongoing activity among larger MSP roll-ups like Evergreen and Shield. These capital trends emphasize client relationships and patience over scale, according to public statements, countering assumptions that size alone drives value. There is increased focus on data ownership and operational analytics, including vendors requesting detailed operating data before acquisition or onboarding, and concerns that future automation may reduce the depth or accessibility of provider-held data.

Operationally, MSPs are advised to focus on understanding and preserving the value of their client relationships and operational data. Uncertainty around AI implementation, pricing models, and data retention introduces new governance risks and requires providers to remain flexible. Smaller and mid-sized MSPs must critically assess their own business metrics and data while tracking how vendors might eventually control pricing and market direction, recognizing that decisions made during this period of experimentation may affect future operational autonomy and valuation.

MSP Acquisitions Zero Private Equity What The 20 Already Knew

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Rising component costs and shrinking PC shipments are driving a shift toward longer device lifecycles and complicating replacement planning for MSPs. According to Omdia and IDC, global shipments of desktops, notebooks, and workstations have dropped over 20% year over year, driven by higher average prices—especially for memory and storage, which now make up close to 40% of device component costs. Vendors like Gigabyte and AMD are reintroducing DDR4-era hardware to address cost pressures, while memory suppliers such as Micron report sharply increased revenues despite fewer units sold.

The evidence shows a clear disconnect: as overall hardware purchases decline, memory and flash costs surge, and end clients keep older hardware in service longer, deferring refreshes. Texas’s Legislative Budget Board notes that supporting PCs beyond four years increases hidden costs and failure rates. At the same time, data from Buchanan Technologies reveals that most user problems aren’t reported—82% of employees have decided IT issues aren’t worth submitting as tickets. Automated resolution is increasing as well, with HPE reporting that up to 80% of network tickets are now closed without manual intervention.

These developments reinforce the central risk: aging fleets and invisible friction in end user experience make traditional support metrics unreliable for refresh planning. Channel leaders and analysts disagree on hardware buying strategies, citing supply volatility, geopolitics, and unpredictable pricing as ongoing challenges.

MSPs and IT leaders face higher operational risk if they rely solely on ticket reports and delay lifecycle planning. Practical mitigation includes focusing on fleet age, warranty status, and unreported device friction in client conversations—building transparency and comparing proactive replacement costs to budgeted extension risks. Failing to do so risks forced, out-of-cycle purchases at unfavorable prices driven by market conditions rather than strategic planning.

00:00 PC Sales Fall as Memory Costs Soar

04:10 Where an Aging Fleet’s Cost Hides

06:34 Refresh Now or Extend on Purpose

09:41 Why Do We Care?

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A central structural development is the shift of verification, remediation, and risk management responsibilities from AI vendors to downstream parties, notably MSPs and their clients. Companies like OpenAI and Anthropic are issuing large volumes of AI-generated outputs—ranging from research papers to vulnerability reports—without thorough internal vetting, pushing the burden of review and potential incident response onto service providers and end-users.

OpenAI released 722 AI-generated mathematics manuscripts, acknowledging that many lack formal verification and that only about 40% include machine-checkable proofs, according to The Verge. Researchers note the process of verifying these outputs could take years. Similarly, Anthropic’s OSS scanner has produced over 6,000 vulnerability reports, but only about 10% have been patched upstream, meaning the majority of validation and remediation falls to maintainers or receiving organizations.

This downstream shift is affecting technical communities and service providers alike. The Linux kernel project faces an “unmanageable” stream of duplicative AI-generated bug reports, with Linus Torvalds calling much of it wasted effort. MSPs are encountering new operational loads as clients connect AI assistants like ChatGPT to shared files, leading to data exposures and increased permissions cleanup—tasks for which vendors accept no responsibility.

Operationally, MSPs face increased margin pressure and are forced to absorb or account for the unplanned labor associated with AI-driven incidents. The risk is that providers who fail to measure and communicate these costs will see their profitability erode, while clients remain unaware of the true operational impact of their AI usage. Explicitly quantifying and attributing these burdens allows MSPs to establish clearer terms and avoid being the unpaid verification layer for upstream vendors.

00:00 Seven Hundred Papers, Nobody Checking

04:04 Cheap to Make, Costly to Check

06:37 The Cleanup Lands On You

09:46 Why Do We Care?

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The episode reveals a governance gap between mounting cybersecurity requirements and the operational realities facing MSPs serving charter schools and nonprofits. Charter Technology Solutions (CTS) exemplifies how regulatory mandates, cyber insurance controls, and evolving client demands confront fixed budgets and unique seasonal workloads in mission-driven verticals. Addressing these pressures necessitates major adaptations to vendor licensing models and new service design approaches to maintain basic security.

CTS reports managing over 100 school clients, deploying a lean security stack focused on MDR (addressing endpoint and cloud threats), email security, security awareness training, and cloud backup. This shortlist stems from the need to balance cost with non-negotiable protections, achieved partly by negotiating with vendors to exclude student accounts from licensing fees. Despite these efforts, only around half of CTS’s clients adopt the full cybersecurity package, reflecting financial tradeoffs and limited internal resources at schools.

Operational adjustments support these constraints, including shifting permanent staff roles seasonally to meet heavy summer project demand and relying on short internships to scale device provisioning. MFA adoption remains problematic due to client concerns about teacher burden and the technical challenges of securing student accounts. The episode also highlights a rising risk area: ad hoc adoption of AI tools by clients without adequate access controls, leading to sensitive data exposure through weak permissions.

For MSPs, these dynamics mean risk cannot be reduced through technology alone. Accountability for client decision-making, flexible contracts, and persistent education are necessary, especially when client governance lags technical change. The need to negotiate or customize vendor agreements adds contract risk, while unmanaged use of new technologies like AI introduces operational complexity and liability exposures that MSPs must track and address proactively.

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The episode highlights the risk of documentation mismatches and liability transfer within MSP mergers and acquisitions, using the example of MSP+—a consultancy formed by the 2023 merger of five advisory firms. Unclear responsibility for a $700,000 SBA loan and conflicting financial representations during the merger process led to litigation, underscoring how misaligned documents can result in legal and financial exposure.

The central development is MSP+ suing its former CEO Adam Bielanski, his wife, and related companies, alleging misrepresented financials and inappropriate transfer of debt. Court documents reveal that while the merger agreement designated the loan as company debt, the actual borrower was not part of the merger, leading to stopped payments, a loan default, and both parties seeking damages and reimbursement in federal court.

Supporting cases include disputes over projected recurring revenue, disagreements on deferred pay, and claims of deleted or misused company data. Vendor program dependency also played a role: the end of ConnectWise Advantage eliminated a key revenue source, showing how third-party program changes can disrupt business plans and valuation.

For MSPs and IT leaders, the practical lesson is the need for precise, accurate documentation and clear agreements on debts, compensation, and asset transfers. Reliance on external programs for revenue and lack of solid data retention policies increase operational risk, making comprehensive documentation and governance essential to avoid similar disputes and liabilities.

00:00 MSP+ v. Bielanski: The Roll-Up Paperwork That Didn't Match

05:22 The Complaint and the Countersuit

12:47 Why Do We Care?

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About Business of Tech: The daily MSP news and analysis podcast for managed service providers and IT services leaders. Host Dave Sobel covers AI for MSPs, MSP pricing and profitability, MSP M&A and private equity, vendor partner programs, MSP cybersecurity and liability, and MSP operations — and answers the question every story raises: Why do we care? Produced by MSP Radio.

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Vendor incentive structures are undergoing a significant realignment, shifting both margin pressure and financial risk toward MSPs and IT service providers. Microsoft’s recent policy change—eliminating flat CSP renewal rebates as of October 1 in favor of rewarding seat growth and AI workload adoption—directly illustrates this trend. Techaisle’s survey of 5,450 partners highlights the disconnect: 72% of incentive payouts occur at deal close, but 41% of MSP revenue is linked to renewals, pushing long-term margin risk downstream to the provider.

Evidence from Techaisle shows traditional partner categories (VAR, MSP, SI) no longer predict operational models. Instead, providers are moving toward roles such as data orchestrators and AI outcome underwriters, each with new risk profiles. The episode underscores that incentive programs are often misaligned: co-sell rewards remain inconsistent, partner-to-partner deal mechanics are unsupported, and marketplace participation erodes margins for many providers. Profitability and predictability continue to be cited as leading concerns.

Secondary findings deepen these risks. Three-quarters of partners already use AI in daily operations, outpacing vendor support and enablement efforts. Over half of SMB buyers use generative AI tools to start vendor searches, and only 4% of small businesses manage privileged non-human identities. These factors expand the governance and security gap, while token-based AI pricing introduces billing unpredictability and customer dissatisfaction. Research indicates 35% of SMBs are ready to change providers if their AI needs are not met.

For MSPs and IT leaders, this environment results in tighter margins, unpredictable compensation, and accelerated churn risk without a defined AI offering. Providers must update service models, clarify AI use cases and pricing, and address security and identity risks—especially around non-human agents—to remain credible and competitive. Reviewing contracts, marketplace involvement, and program alignment is advised, as more responsibility for outcomes and risk is shifting steadily from vendors to service providers.

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NinjaOne On-Demand Webinar: https://go.businessof.tech/p/ninjaone-pod

About Business of Tech: The daily MSP news and analysis podcast for managed service providers and IT services leaders. Host Dave Sobel covers AI for MSPs, MSP pricing and profitability, MSP M&A and private equity, vendor partner programs, MSP cybersecurity and liability, and MSP operations — and answers the question every story raises: Why do we care? Produced by MSP Radio.

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ABC Solutions · CometBackup · Firetail · HaloPSA · LogMeIn · Mailprotector· Pax8 · Rythmz · ScalePad · TimeZest · Transit AI

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Organizations are moving from purchasing commercial software to building their own applications with AI tools, shifting the ongoing maintenance and accountability from vendors to internal teams and MSPs. This creates governance gaps and operational risk that go unpriced, particularly as automation and custom app development become easier for enterprises, small businesses, and IT providers. Evidence comes from McKinsey’s State of AI survey, GoDaddy’s self-built app hosting, and direct MSP case studies.

McKinsey found that 32% of organizations—and nearly half of AI “high performers”—built functionality in-house using AI instead of buying it. GoDaddy now enables deployment and management of such customer-built apps. MSPs like ITECH Solutions and WheelHouse IT illustrated how staff can rapidly implement automations or replace core business software, increasing reliance on internally developed tools.

Despite accelerated development, ongoing responsibilities for patching, security reviews, and identity management remain. Incidents at Meta and OpenAI, as well as survey data from VentureBeat, highlight persistent risk and the lack of robust controls—especially around identity and security—for internally developed or AI-generated applications. Vendors such as Apple are responding with operating system-level controls, shifting the burden of informed consent and risk mitigation to the device layer.

For MSPs and IT leaders, each client- or internally-built tool represents a new asset to manage, often without additional compensation. Failure to explicitly govern, price, and assign ownership to these tools increases hidden costs and risk, particularly with flat-fee agreements. Practical measures include treating every custom build as a managed asset, assigning identities and permissions, and itemizing them in contracts to align pricing with actual support delivered.

00:00 Nobody Owns What Your Clients Build

04:08 Cheap To Build, Costly To Own

06:56 The Layer Only You Control

10:18 Why Do We Care?

Supported by:

HaloPSA

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NinjaOne On-Demand Webinar: https://go.businessof.tech/p/ninjaone-pod

About Business of Tech: The daily MSP news and analysis podcast for managed service providers and IT services leaders. Host Dave Sobel covers AI for MSPs, MSP pricing and profitability, MSP M&A and private equity, vendor partner programs, MSP cybersecurity and liability, and MSP operations — and answers the question every story raises: Why do we care? Produced by MSP Radio.

💼 All Our Sponsors

MSP Radio is supported by our partners:

ABC Solutions · CometBackup · Firetail · HaloPSA · LogMeIn · Mailprotector· Pax8 · Rythmz · ScalePad · TimeZest · Transit AI

Supporting the IT services community through insights, analysis, and transparency.

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