Business of Tech: Daily MSP News & IT Services Analysis

The episode highlights a structural shift in the MSP sector characterized by consolidation at the top and fragmentation at the low end, driven by a rise in niche MSPs entering the market and ongoing acquisitions by larger providers. This shift is outlined in discussion of industry data sourced from the Kaseya State of the MSP report, as well as Greg Jones’ observations regarding both market entry trends and the increased prevalence of hyperscale consolidation activity.

According to the Kaseya report, there has been a reduction in the proportion of clients spending $25,000 a year or more with MSPs—from three quarters of the market to four in ten within a year—indicating a trend towards smaller client engagements. In parallel, 48% of surveyed MSPs identified AI as their clients' top need, but only 13% reported being able to charge for AI, pointing to a significant value capture gap. These figures were referenced during a discussion on monetization challenges and the changing expectations around AI service delivery.

Further supporting this structural change, the episode details Kaseya’s shift towards more transparent contract terms and new commercial offerings such as FLEXSpend and catastrophic loss prevention. These policy changes are described by company leadership as attempts to address longstanding provider grievances and increase flexibility, but the discussion also reinforces the persistence of operational risks related to contract renewals and alignment between vendor strategy and MSP needs.

For operators, the implications center on growing exposure to contract risk, complexity in billing and service models, and the challenge of capturing new sources of value such as AI services. The evolving mix of small and consolidated players in the MSP landscape puts further pressure on vendor selection, contract diligence, and process accountability, especially where new technologies are marketed ahead of proven monetization strategies.

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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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A disconnect between technical capability and end-user adoption is driving current decision-making in AI and infrastructure among managed service providers and IT leaders. While companies like Broadcom, Perplexity, Apple, and NVIDIA are introducing hardware and platforms for on-premises and local AI processing, the primary lever for value remains organizational adoption rather than available technology. Costs and infrastructure investments are being shaped by how—and whether—users engage with these tools.

The most significant evidence is from Voya Financial, where a five-hour AI training for 11,000 employees led to 98% adoption and frequent use, despite no change in underlying technology. Industry data cited shows that while generative AI now reaches 80% of occupations, only 2.8% of tasks have more than 50% usage, and none reach 70%. This indicates broad awareness but little real transfer of work to AI, making current spending primarily reflective of shallow adoption rather than deep operational change.

Additional developments include a clear industry shift toward privately owned AI infrastructure, as shown by recent product launches and by NVIDIA’s ongoing focus on cloud-rented compute. Gartner projects that consumption-based and inference-specific storage will expand rapidly by 2029, but these estimates are based on limited current adoption, underlining a mismatch between infrastructure readiness and immediate need.

For MSPs and IT providers, the risk is overcommitting to AI infrastructure before clients are ready. Real opportunity lies in structured adoption programs for existing AI tools—identifying staff with teaching skills, piloting training internally, and focusing on increasing client usage. Deferring on-premises AI infrastructure projects until client adoption and economic signals align is positioned as a safer, more pragmatic approach.

04:20 Nobody Turned It On

07:53 The Window Opens In 2029

10:46 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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A structural transfer of liability and risk is reshaping industry engagement models, with outcome-based contracting increasing across service agreements. This shift is being driven by buyer demands for accountability in technology solutions, notably in artificial intelligence deployments, and is illustrated by recent unpublished but credible reports that OpenAI is quietly allowing select large enterprise customers to pay only when AI tasks are successfully completed. Supporting research from Gartner and CIO Dive highlights a growing disparity: while 19% of service buyers seek outcome-based payment models, only 13% of agreements from sellers currently accommodate them.

The core development spotlighted is the disconnect between expectations for measurable AI-driven business outcomes and the lack of empirical evidence that such technologies are delivering on those promises at the organizational level. A large-scale survey by the National Bureau of Economic Research, encompassing nearly 6,000 senior executives across four countries, found that over 89% reported no observable improvement in employment or labor productivity from AI investments during the past three years, despite substantial organizational changes and budget reallocations. Additionally, research by Thomson Reuters found that 91% of 1,800 professionals reported their organizations were not realizing expected AI value, identifying a gap in demonstrable returns even while the technology is being deployed.

Supporting data from Techaisle reveals partner capability thins dramatically as customers progress into advanced AI adoption stages. Most channel providers retain capacity only for basic "estate" work, with capability dropping to near zero for the most advanced client needs. Forrester has also identified persistent barriers to reliable measurement, such as fragmented and inconsistent data baselines, as well as dependencies on customer-side decisions. These factors magnify contract risk and reinforce the liability shift toward providers, who become responsible for defining, measuring, and underwriting outcomes without always possessing necessary levers or data.

The operational implication for MSPs and IT service providers is heightened exposure to contractual and financial risk when agreeing to outcome-based terms, especially without mechanisms to price or control every relevant input. Providers are often unable to flow contract risk upstream to vendors or technology manufacturers, as their own agreements typically exclude outcomes. The episode concludes that early engagement and proactive definition of acceptable, controllable outcomes is essential, as outcome-based demands are likely to appear pre-baked in future client agreements, shifting bargaining power away from providers unprepared to quantify their exposure. Using data from their own worst-performing months, documenting client dependencies as contract conditions, and piloting outcome-based lines in otherwise standard agreements are outlined as practical tactics to mitigate downside risk before broader market adoption.

00:00 Four Numbers, One Cause 

03:45 What You Ask For When You Can't Tell

06:45 Nobody Underneath You

10:20 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 reveals a structural shift away from indiscriminate adoption of AI tools toward targeted deployment based on business needs within the MSP sector. According to Uptime Global, a channel-only outsourcing provider, premature or hype-driven implementation of AI frequently results in incomplete projects and missed operational gains. The review of AI execution within MSP ticketing environments shows that sustainable value emerges when organizations align AI initiatives with clearly identified business issues, rather than searching for use cases to justify a technology already acquired.

Concrete evidence from Uptime Global’s experience indicates that approximately 25% of their MSP partners have deployed notable AI solutions beyond ticket triage functions in the past 18 months, according to Jason Kemsley, Chief Revenue Officer. Outcomes vary: roughly half saw ticket volume reductions of 10–25%, while the remainder experienced limited value or customer dissatisfaction tied to inadequate human involvement in support processes. Uptime’s operational model uses a confidence threshold for AI-driven actions, where only outputs above 98% confidence automatically execute—mirroring human engineer accuracy rates—and anything below triggers human review.

Supporting developments further highlight the shift’s operational impact. Uptime Global has eliminated dedicated triage and dispatch roles due to increased AI efficiency, resulting in a role blend where “first responders” now handle both traditional triage and basic technical support tasks within set time limits . The company’s pricing structure is affected by AI efficiency, creating margin pressure as contract models based on per-ticket or per-device pricing are exposed to declining ticket volume and shifting customer expectations around service value and outcomes.

For MSPs and IT service providers, the operational implications center on increased pricing pressure, the erosion of certain labor-based roles, and the requirement to redesign governance and accountability models to accommodate both automated and human ticket outcomes. Vendor dependency grows as MSPs rely on AI-driven platforms to triage, allocate, and resolve tickets, increasing exposure to platform-specific risks and necessitating clear thresholds for AI confidence and human intervention. Contract structures that do not account for dynamic efficiency gains may create pricing misalignment and customer dissatisfaction over time, emphasizing the need for providers to actively manage and transparently communicate both the tradeoffs and limits of AI-enabled support.

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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A growing gap between artificial intelligence (AI) adoption and effective data governance is reshaping the operational landscape for managed service providers (MSPs) and IT service providers. Industry data cited by GTIA’s Data and AI Interest Group indicates that while 97% of providers have adopted AI, only 20% have implemented governance frameworks sufficient to extract value or mitigate risk. This structural mismatch is driving increased scrutiny from regulatory authorities and forcing MSPs to revisit internal decision-making, especially regarding data integrity and platform selection.

Supporting this trend, a Federation of Small Businesses report found 86% of organizations in the UK are struggling with AI and data governance adoption, often uncertain about where to begin. Hollie Whittles, co-founder of Purple Frog Data, described practical barriers: many businesses believe their data is clean and structured but are routinely confronted with evidence of significant inconsistencies and errors during implementation. The cost of remediation and governance, including board-level buy-in, can climb to tens of thousands of pounds, highlighting the substantial upfront investment required before AI can deliver reliable business value.

Operational vulnerability is further magnified by ease-of-use pitfalls and vendor relationships. MSPs lacking in-house data engineering or readiness tools frequently misjudge the scale and complexity of cleaning and securing client data estates. Whittles warned of scenarios where consultants, through careless or uninformed data modelling (such as over-exposing HR data in reporting tools), inadvertently increase both cyber and confidentiality risks. Some firms approach the problem by retaining all data and tooling within clients’ own cloud tenants, shifting data residency and associated risks directly to clients, but raising new questions of contractual clarity and due diligence.

For MSPs and IT leaders, these developments reinforce the need for explicit governance structures, clear contractual provisions regarding data handling, and internal upskilling rather than relying solely on third-party expertise. Short-term revenue can be lost to competitors willing to bypass governance, but there’s an increased long-term risk of error, replacement, or regulatory sanction. Effective governance not only requires financial investment, but also consistent participation from leadership to avoid operational blind spots and to ensure client obligations—and liabilities—are clearly defined and managed.

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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 driven by the falling cost of AI-assisted insight extraction and its impact on how buyers assess technology providers. Referencing companies such as OpenAI and Google, as well as research from the AI Revenue Institute and Gartner, Dave Sobel highlights how lowered model prices enable automated systems to rapidly analyze vendor documentation and shape procurement decisions, fundamentally changing the basis of competition from persuasion to transparent, retrievable data.

A recent AI Revenue Institute study, as cited by Dave Sobel, found that over half of surveyed decision-makers had removed a vendor from consideration after an AI assistant highlighted a documented shortcoming. Simultaneously, OpenAI and Google have reduced their top-tier AI model pricing, with OpenAI dropping costs by more than 20% and Google offering a temporary 50% cut before reverting. Analysis from TD Cowen and Business Insider shows that such price cuts have driven up both usage and revenue, with OpenAI’s low-cost models experiencing a 14-fold usage increase post-reduction.

These developments are reinforced by Gartner’s identification of the “inference paradox,” where greater AI capabilities and lower per-query costs actually raise overall spend due to increased volume and complexity of tasks. Supporting data includes Google’s reported 50x annual increase in tokens processed and a Deloitte case of a healthcare provider with unplanned AI costs rising as much as 3x in a year. Alongside this, Pew Research identifies that a third of new web content on commercial sites is machine-generated, leading platforms like LinkedIn to introduce AI-detection and downranking measures.

For MSPs and IT leaders, the implications are direct. Automated buyer research now prioritizes concrete, extractable data over marketing language; any absence or non-disclosure—especially around pricing—can result in removal from consideration without notice. Publishing specific, measurable facts (service boundaries, pricing logic, response times with dates) increasingly determines whether a provider is surfaced or omitted by AI agents assembling comparative analyses. Failure to clearly define offerings and exclusions results in unfavorable inferences or comparisons, increasing operational risk and transfer of accountability away from the provider.

00:00 The Buyers Brought a Machine 

03:45 Cheaper Made It Bigger

06:49 Your Website Is a Deposition

10:06 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 central structural shift discussed is the repricing and erosion of the MSP business model as artificial intelligence (AI) and automation impact service delivery, pricing models, and margin structures. Analysis referenced recent polling and reporting, including the Omnia poll of 22,000 MSP partners and Service Leadership’s financial benchmarking. The integration of AI is reducing direct labor requirements and shifting traditional cost structures—posing both short-term increases in service margins for top-tier MSPs and complex, longer-term risks to the per-user pricing paradigm. Vendors, such as ConnectWise and RapidScale, are central to these developments, as their platforms, pricing models, and reporting mechanisms increasingly determine downstream MSP economics.

Supporting evidence from Service Leadership’s recent profitability report shows top quartile MSPs achieved a service multiple of wages (SMW) of 3.01—a level reached previously only during periods of wage collapse. The report also highlights that this margin growth is isolated: while the best-in-class are realizing sharply higher service margins, the median and bottom quartile remain flat. Reporting and analysis attributed this phenomenon to the earliest and most effective adopters of automation, particularly service desk automation aligned with AI, according to theories discussed with Service Leadership and ConnectWise representatives. However, there is a notable lack of definitive causation, as Service Leadership states the link between AI adoption and observed margin increases remains a working theory pending further data.

Additional developments reinforce the risk environment. Nearly 43% of surveyed MSPs are actively considering alternatives to per-user pricing, with another 17% acknowledging a need to change their pricing but lacking a defined direction. Industry analysis notes that consumption-based models—such as token-based or outcome-based pricing—present challenges, including unpredictable vendor cost structures and difficulties in measuring actual results achieved. Meanwhile, the risk of vendor-driven reenactment of break-fix economics and cost volatility increases, with some vendors already raising prices significantly to offset their own AI-related costs, pointing toward future margin compression downstream.

For MSPs and IT service providers, the operational implications are immediate and material. Providers face growing exposure to pricing and margin risk, especially as clients begin to recognize and challenge efficiency gains achieved by automation. Structurally, there is rising accountability for justifying service costs and delivering new forms of value outside commoditized support. Continued reliance on legacy pricing models without adaptation to AI-driven changes increases the risk of eroded margins or adverse contract negotiations. The most resilient operators will need to stabilize internal cost structures, reconsider client contracts, monitor vendor behavior closely, and prepare for increased customer scrutiny on both cost and deliverables.

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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.

MSP Radio is supported by our partners:

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A persistent governance gap is evident in current IT operations, as credential management and authorization checks fail to keep pace with increased automation and AI integration. This is visible in incidents involving major vendors such as N-able (through Passportal), Anthropic’s Claude, AI-based retail management at Andon Labs, and legacy industrial controllers monitored by agencies like the NSA, CISA, and FBI. The episode highlights how systems are increasingly reliant on automated actors and credentialed assistants, while foundational questions of access rights and accountability remain unresolved.

The most consequential case centers on a vulnerability in N-able's Passportal browser extension, disclosed by security researcher James Arnott. The flaw allowed any website—or embedded ad—to request and obtain session tokens, enabling decryption of entire password vaults. This affected approximately 2,500 MSPs and 165,000 SMBs, with each stolen token remaining valid for 100 days. N-able patched the issue quickly, but Dave Sobel emphasizes that the responsibility for checking permitted actions within such systems is often misattributed or left unaddressed.

Supporting developments reinforce this governance gap. An AI assistant exploited poor authorization in an Australian gym reservation system, canceling another user’s booking without hacking or unauthorized login. Similar risks persist in industrial environments, where controllers for energy, water, and agriculture often lack basic authentication—exposing them to AI-generated exploitation scripts, according to joint agency warnings. Additionally, retail automation at Andon Labs revealed AI-driven policy lapses, where systems cannot reliably document or enforce their own rules, highlighting operational weaknesses.

Operationally, MSPs face increased risk from both their own service infrastructure and client environments. The practical recommendation is to issue discrete, revocable credentials tailored to each system agent, limiting their scope and ensuring traceable accountability. Providers are advised to formally define and document their responsibility boundaries regarding access and permissions in third-party applications. These steps shift the focus from attempting to control every client-side variable to clear documentation and compartmentalization, reducing dispute risk and speeding incident investigations.

03:39 The Check Was Always a Person 

06:37 Your Tools Ask the Wrong Question

10:27 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.

MSP Radio is supported by our partners:

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

Get exclusive access to investigative reports, vendor analysis, leadership briefings, and more.

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The core structural shift highlighted is the disconnect between service reliability gains from AI automation and readiness for strategic change among IT service providers and their clients. Reports from SolarWinds, Corsica Technologies, and Deloitte reveal that AI is delivering measurable productivity benefits, but those time savings are consumed by ongoing reliability work rather than being directed toward governance, process redesign, or workforce adaptation. This leaves most organizations with improved operations but unprepared to leverage AI for broader business transformation, creating a gap between what clients say they want and what providers are set up to deliver.

SolarWinds’ 2026 State of ITSM report found that 84% of IT teams report AI meeting or exceeding their return on investment expectations, with teams recovering roughly three hours per week in several core areas, such as issue detection and ticket triage. However, almost the same amount of capacity is then redirected to keeping those new AI systems running—83% of teams spend three or more hours weekly maintaining AI reliability. Simultaneously, Corsica Technologies’ Censuswide research among 600 IT and security leaders at U.S. mid-sized businesses found that 96% claim to trust their MSP, yet two-thirds are considering switching within 12 months, citing limited AI or automation support as one of the top reasons.

Additional research contextualizes the readiness gap. According to a PwC survey, only 5% of organizations report their business processes as highly prepared for AI agents, and a Cloudera study found that 95% of large companies delayed or canceled at least one AI project in the past year due to governance, compliance, or regulatory concerns. The episode also notes a public sentiment shift, citing a Pew Research poll in which over half of American adults express more concern than excitement about AI—a trend particularly strong among people under 30. Vendor product launches from companies like Kaseya and Syncro are described as offering only superficial differentiation in this environment.

For MSPs and IT leaders, this dynamic presents operational risks. The default allocation of AI-driven productivity gains toward reliability tasks undermines investment in strategic readiness, reinforcing dependence on vendor offerings without improving meaningful differentiation. Most clients lack a specific benchmark for “AI readiness,” creating an open but temporary competitive opportunity for providers willing to define and document it for them. However, unless time and resources are explicitly earmarked for readiness activities—in governance, process adaptation, and client education—MSPs risk being evaluated on ill-defined criteria or commoditized platforms, increasing contract risk and exposing gaps in internal accountability.

00:00 The Two Numbers Don't Fit 

04:52 Only One Half Can Take the Hours 

08:02 Everyone Buys the Same Platform

11:20 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.

MSP Radio is supported by our partners:

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The core structural shift addressed in this episode centers on the unbundling and modularization of vendor platforms in the MSP technology market. This shift is exemplified by ThreatCaptain’s launch of its Gen 4 product, which transitions from an all-encompassing platform to discrete modules aligned to specific MSP business challenges—lead generation, sales enablement, and ROI/risk analytics. The move is designed to align product structure and pricing more closely to the diverse operational maturity levels of MSPs, as described by Brad Powell, co-founder of ThreatCaptain.

ThreatCaptain’s Gen 4 is available in three modules priced at $199, $399, and $599, most notably a move away from the earlier $1,499 per month pricing reported in March. According to Brad Powell, this change was driven by limited adoption among smaller MSPs, with the prior model better suited to larger firms already equipped with mature sales teams. He cites customer Novus Insights as an example, attributing $80,000 in professional services revenue over three months and more than $1 million in expected ARR, but acknowledges this reflected a highly mature CISO-led operation. The vendor currently reports approximately 65 active paying MSP partners, intending to scale significantly.

Supporting developments include the influence of insurance risk modeling and industry threat intelligence frameworks on new MSP toolsets. ThreatCaptain originally built its risk engine leveraging data from the IBM Cost of a Data Breach Report and the Verizon DBIR, adapting these for SMB scenarios. The episode also highlights the role of information sharing organizations (ISAOs), with Brad Powell noting the challenges of translating technical threat data into actionable intelligence for SMB-focused MSPs and illustrating ongoing coordination and separation of threat feeds between vendor sales processes and industry sharing mechanisms.

Operational implications for MSPs include increased need for prudent selection among modular product offerings, clarity around the scope and accountability of vendor-delivered analysis, and awareness of potential misalignments between vendor risk models and actual business outcomes. The trend underscores cost versus capability tradeoffs, especially for smaller providers balancing limited resources against the operational benefits of specialized tools. For MSPs participating in threat intelligence programs, there is also an ongoing requirement to maintain clear boundaries around shared data to prevent unintentional exposure or misapplication in commercial contexts.

Supported By: 
ScalePad 
Pax8

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.

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.

Get exclusive access to investigative reports, vendor analysis, leadership briefings, and more.

👉 https://businessof.tech/plus

Want the show on your favorite podcast app or prefer the written versions of each story?

📲 https://www.businessof.tech/subscribe

Looking for the links from today’s stories?

Every episode script — with full source links — is posted at:

🌐 https://www.businessof.tech

Pitch your story or appear on Business of Tech: Daily MSP News & IT Services Analysis

💬 https://www.podmatch.com/hostdetailpreview/businessoftech

LinkedIn: https://www.linkedin.com/company/28908079

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