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The structural mechanism highlighted in this episode is the gap between optimism for artificial intelligence (AI) in IT operations and the actual integration of AI as a core operational tool among MSPs. The conversation centers on Auvik’s market activity, including data from its 2026 IT Trends Report and the launch of its Aurora AI agent suite, which aims to operationalize AI in network management. This shift surfaces a reliance on vendor-developed automation tools to address efficiency constraints, while simultaneously raising questions about operational accountability, documentation quality, and risk management as automation expands.
Auvik’s report found that 67% of IT professionals are optimistic about AI, but only 5% report AI as core to daily operations. According to the company, true integration of AI requires that it consistently deliver repeatable value and autonomy in network troubleshooting, rather than partial assistance that still requires escalation to senior staff. The Aurora release focuses on embedding troubleshooting agents within alerts to enable lower-tier technicians to resolve incidents that previously required escalation. Auvik claims this approach can reduce troubleshooting time for network issues by around 50%—though actual results will vary by use case and organizational readiness.
Secondary developments discussed include the expansion of Auvik’s platform to server, endpoint, and SaaS management in response to customer demand for greater visibility and tool consolidation. Shadow IT, particularly unauthorized AI and SaaS usage, emerged as an ongoing governance and security challenge, with Auvik detecting over 100,000 shadow AI applications across client networks in 2025, and 60% of IT teams discovering unauthorized SaaS monthly. The discussion also examined the need for up-to-date documentation, and the ongoing tension between adding more monitoring tools versus the operational burden and alert fatigue those tools can introduce.
For MSPs and IT service leaders, these trends increase dependence on vendors to supply both the automation and the context necessary for safe and efficient operations. Effective AI integration requires accurate network documentation, clear governance, and jointly developed client policies for shadow IT management. As tool sprawl grows, the sector cannot rely solely on more visibility; instead, the actionable quality of alerts, tool interoperability, and operational discipline will be key to managing ticket loads and avoiding inefficiency or compliance risks. Vendor pricing shifts and platform lock-in further reinforce the need for informed procurement, benchmarking, and contingency planning.
Supported by:
TimeZest
ScalePad
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 ongoing adoption of AI in managed services is exerting downward pressure on service margins and changing how value is delivered and retained. According to discussion with Dr. Gleb Tsipursky and analysis of case studies such as ImageQuest, even MSPs serving small organizations (as few as 8-50 staff) must address this shift. AI-based automation and process optimization reduce the operational cost of service delivery but also risk eroding the provider's pricing power, forcing firms to reevaluate their growth and retention strategies.
The episode details that AI projects frequently fail not due to technology gaps but because of organizational resistance and inadequate alignment with end-user workflows. Dr. Tsipursky cites research indicating that 95% of AI pilots fail to scale, and only a minority deliver measurable ROI . A referenced Stanford study found that companies successfully adopting AI increase headcount 6% faster and revenue 9% faster than their peers, though market share and profitability gains are realized by those able to overcome fear, identity threat, and social stigma among staff.
Further examples highlight the risk of margin compression, such as law firms and other service organizations passing AI-generated cost savings directly to clients in the form of fee reductions (8-30%) . For MSPs, especially those on fixed-fee contracts, this competitive dynamic may lead to price-driven client churn unless operational efficiencies can be recaptured as profit or used to accelerate market share gains. The operational challenge is compounded by the need to retrain staff on natural language programming and prevent issues like "AI workslop," where poor-quality outputs from AI waste significant employee time.
For MSPs and IT service leaders, the immediate implications are increased pressure to adopt AI for internal gains while managing associated risks to employee engagement, quality, and client retention. Providers must quantify and control the costs and benefits of AI usage, track operational metrics beyond simple time savings (such as deflection percentage and client satisfaction scores), and develop policies to address employee resistance, accountability for errors, and margin dilution. Failing to do so risks loss of market position to more adaptive competitors and exposes firms to both direct and indirect costs associated with ineffective AI integration.
Supported by:
HaloPSA
USecure
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 channel programs are structurally organized around metrics and incentives that prioritize initial partner acquisition over long-term partner viability or growth. Analysis of policies and practices at firms like Dr. Backup, Microsoft, and Arctic Wolf highlights that most vendors track and publish partner sign-ups (“joined”) but seldom disclose the ongoing, active partner count or revenue growth among partners. This structural approach creates a visibility gap and misaligns vendor and partner incentives, presenting ongoing governance risk for MSPs and IT providers.
The purchase of Dr. Backup by Hosvara, led by Nancy Henriquez, highlights this disconnect. Public figures claim over 300 partners in the Dr. Backup program, but only 125 are active according to Henriquez—meaning more than half of all partners have left. This attrition is not unique. Industry survey data from Techaisle shows that 72% of vendor incentive spending happens at deal closure, while 41% of MSP revenue derives from renewals. Vendor programs often fail to account for long-term partner success, incentivizing sign-ups instead of sustainability.
Additional developments reinforce this structural pattern. Microsoft’s retirement of its Azure Expert MSP tier by January 2027, alongside Arctic Wolf’s introduction of a new partner program tier for smaller MSPs, both reflect vendor-driven changes to partner structure that serve corporate strategy rather than address partner outcomes. Scorecard models and published partner metrics remain opaque to most participants, making it difficult to evaluate the stability or effectiveness of a given partner program.
For MSPs and IT leaders, these structural dynamics amplify contract risk and operational complexity. Reliance on vendors that refuse to disclose active or growing partner numbers leaves service providers exposed to sudden program changes, with migration and customer disruption costs that are rarely priced in advance. Practical safeguards include demanding transparent partner retention data during contract negotiations, aligning agreement terms with visible vendor commitments, and budgeting explicitly for client migrations tied to vendor program volatility.
00:00 Why She Bought The Partners
05:26 Vendors Pay For The Sale
07:34 Moved When The Map Changes
10:39 Why Do We Care?
Supported by:
USecure
Pax8
Mailprotector
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 shift is materializing in the IT services market as software vendors move away from traditional fixed software pricing and towards models based on AI token consumption, effectively redefining the sources of margin and exposing providers to variable cost structures. This transition is embodied by companies such as Flamingo, which has constructed an MSP software suite and prices access at $1 per device per month, including a token allotment, but whose actual profits—and partner costs—are derived from overage on AI token consumption. The fundamental mechanism is that margin is increasingly being earned from metered AI usage rather than the flat software license model that previously dominated the sector.
The most consequential development highlighted involves Flamingo’s approach: software fees serve primarily as an entry point, while ongoing, uncapped AI token consumption drives real costs for providers. According to Flamingo, most partners use 10 to 20 times their monthly token allocation, with the overage billed at rates that track costs from AI lab providers such as Anthropic and OpenAI. These rates are variable, not transparently published, and fluctuate as AI model costs change. Flamingo claims incremental margin via internal consumption efficiency, particularly as it plans to host its own models and increase token allotments without reducing partner pricing, consolidating margin from lowered costs.
Additional evidence is provided by Integris, a private equity-backed MSP that launched a bundled AI service, Core, currently priced per seat or device despite the product’s purpose being automation and labor reduction. Integris representatives indicated that no stable, customer-aligned outcome-based pricing model has emerged, a challenge echoed by other large MSPs and exemplified by Salesforce’s challenges in usage and outcome-based AI billing. At every level—from the vendor building software on top of AI labs to the MSP implementing services for clients—pricing mechanisms remain unsettled, with risk of misalignment and unpredictability being pushed downstream.
For MSPs and technology leaders, these developments introduce new operational risks, chiefly around cost forecasting, pricing transparency, and margin management. The shift to consumption-based and hybrid models increases the burden of monitoring both cost and value delivered from AI-powered services, while lack of clear, measurable outcome units limits the feasibility of outcome-based billing. Providers may find themselves forced to absorb cost variability while delivering fixed-fee services, or to renegotiate contracts and client expectations as variable pricing becomes standard. As AI-related spending rises and billing models remain opaque, effective governance, cost auditing, and risk management become central to sustainable operations.
Supported by:
GoTo(LogMeIn)
Proofpoint
USercure
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 core structural shift discussed is the transition from fixed, seat-based licensing models to metered, consumption-based pricing for artificial intelligence tools. Driving forces behind this change are vendor margin pressures and increasing alignment between costs and actual resource utilization, which is resulting in a measurable difference on invoices rather than announced policy changes. Gartner’s projections indicate that by 2028, over 35% of new corporate legal technology spending will operate on usage-based models, and this trend is evident in technology procurement and vendor billing practices.
Key evidence is provided by data from Accenture and Gartner. Accenture’s survey of 750 executives found that only one in five dollars spent on AI token usage can be traced to clear financial outcomes, while Gartner estimates global AI spending will reach $2.7 trillion in 2026, mainly on infrastructure. CIOs are frequently unaware of embedded AI costs, with untracked usage and spending increasing accordingly. Deloitte reported that 31% of UK workers use generative AI at work without employer knowledge, and 17% cover these tools out of pocket, totaling £958 million.
Further supporting this shift, BambooHR data shows that 42% of AI tool usage time involves troubleshooting or prompt iteration, equating to roughly 20 workdays per user per year—an activity that becomes billable under consumption models. Vendors like Addigy are rolling out monitoring suites to track shadow AI usage and enforce compliance, while routing platforms such as OpenRouter guarantee data residency and track counts at a granular level. Across the technology stack, billing and consumption visibility are concentrated with vendors, leaving service providers and clients without independent reconciliations.
Operational implications for MSPs and IT leaders center around contract exposure, accountability, and the need for defensible consumption tracking. Service providers face the choice between reselling metered AI services (and absorbing variability inside fixed-price contracts) or focusing on policy, instrumentation, and independent usage measurement. Practical safeguards include establishing clear roles in AI procurement, conducting usage amnesties to inventory real adoption, and maintaining independent records to validate vendor invoices and mitigate dispute risks. The absence of such mechanisms increases exposure to unexpected billings and client dissatisfaction.
00:00 The Bill Nobody Can Check
03:40 You Pay For Every Retry
06:26 The Meter Isn't Yours
09:56 Why Do We Care?
Supported by:
ScalePad
Proofpoint
Mailprotector
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 SponsorsMSP Radio is supported by our partners:
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The episode details a structural shift in the allocation of risk and liability for artificial intelligence within commercial insurance policies, driven by changes in standard policy language published by Verisk’s ISO division. Insurance carriers are now able to explicitly exclude claims related to generative AI from general liability coverage using endorsements such as CG 40 47, CG 40 48, and CG 35 08, which attach at policy renewal without formal notification. This trend is not confined to general liability but extends across trade, directors and officers, and cyber policies, with companies like W.R. Berkley filing for broader absolute AI exclusions.
Supporting evidence centers on the rising financial exposure associated with AI-related incidents. Chubb’s Cyber Claims Report found the average large company claim increased to $4.4 million, doubling year over year, while mid-market claims rose 22%, even as the total number of claims declined. Sophos and OneTrust research indicates that MSPs are increasingly being relied upon as de facto CISOs and that nearly half of organizations have experienced unapproved AI actions within the past year, highlighting the escalating operational and financial risks for service providers.
Additional developments reinforce the shift of accountability downstream to service providers. The MSP Alliance has updated its Unified Certification Standard (version 4.0) to explicitly govern AI-enabled services with requirements such as approval, monitoring, and evidence collection. Meanwhile, legislative efforts remain stalled, as the Durbin-Hawley bill to classify AI as a product sits in committee without movement. Simultaneously, cases such as Google's Gemini model interacting with live systems demonstrate gaps in external disclosure and enforceability, leaving service providers with limited recourse.
For MSPs and IT leaders, the operational implications are immediate. AI exclusions now often attach silently at policy renewal, making it critical to review insurance documents in detail rather than relying on summaries. Providers must leverage written client acceptance for AI activities, clarify contract scopes, and scrutinize vendor support agreements for clear boundaries of responsibility. Failure to address these areas exposes service providers to uninsured risks, with consequential liability likely to be litigated as a service failure rather than a product defect.
00:00 Insurers Are Backing Out
03:38 Why Nobody Upstream Pays
06:38 The Bill With No Address
09:40 Why Do We Care?
Supported by:
Guardz
HaloPSA
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 channel consolidation continues to shape decision-making for MSPs, as the industry evaluates tradeoffs between integrated security stacks and maintaining best-of-breed toolsets. The episode’s discussion centers on the role of platform consolidation, referencing Guardz as an example of a provider leveraging third-party engines like SentinelOne for EDR and Check Point/Avanan for email, shifting focus from proprietary tool development to deep integration and operational unification. This shift reflects broader industry movement away from fragmented tooling toward unified security operations designed specifically for MSP and SMB environments.
The primary evidence highlighted is the operational friction and compromises created by legacy all-in-one approaches, which often involved aggregating standalone tools without meaningful integration, leading to inefficiencies and substandard outcomes. Doni Brass detailed Guardz’s initial strategy of building proprietary AV and EDR products, ultimately conceding the inability to match specialist vendors’ effectiveness. The current Guardz model combines licensing and unified management for technologies like SentinelOne, managed through a single point of support and tied together with an identity-centric architecture. The operational benefit, according to Doni Brass, is streamlined onboarding, reduced tool sprawl, and simpler day-to-day management.
Supporting developments reinforcing the structural channel consolidation theme include user poll data indicating a split among MSPs: some using under three security vendors, others supporting four to eight, and a minority historically managing as many as 15. The discussion also addresses the risks associated with consolidation—namely increased dependency on single-vendor platforms, reduced flexibility to swap components, and potential compliance shortcomings for high-regulation sectors such as CMMC-restricted defense contractors. Doni Brass acknowledged Gardz’s lack of CMMC certification and identified large, mature MSPs with internal SOCs as less likely to benefit from consolidated stacks unless targeting downmarket segments.
For operators, the main implications concern assessment of operational risk, contract liability, and long-term agility. Single-platform solutions can simplify onboarding and management but may introduce lock-in, especially if multi-year contracts are involved. Doni Brass recommended favoring short-term agreements and avoiding exposing specific vendor brands in client-facing deliverables to maintain stack flexibility. Growing reliance on unified platforms demands thorough trial evaluation and continued scrutiny of channel strategy and compliance postures, as vendor pivots and regulatory expectations can change with little notice. Careful governance remains necessary to mitigate both strategic and operational downside.
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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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Margin pressure, driven by a widening profitability gap among MSPs, is the primary structural shift highlighted in this discussion. According to Dave Cava, industry data shows that over half of MSPs operate at less than 5% profitability, while around 27% are running at a loss. Larger and better-funded MSPs are leveraging resources to accelerate adoption of new technologies such as AI, increasing competitive risk for smaller providers that lack operational maturity and financial resilience.
The discussion identified concrete data on workforce dynamics and hiring models as significant, with PeopleSharp internal figures revealing a 30-day gap between presenting a hiring candidate and accepted offers, attributed to process inefficiency on the MSP side. Additionally, Dave Cava referenced market research (Saya) showing a jump from 9% to 16% of MSPs struggling to find qualified technicians, despite a labor market some perceive as soft. This further intensifies pressure on mid-sized and smaller MSPs with limited recruiting power, especially as staffing expectations and willingness to work on-site have been drastically altered post-COVID.
Secondary developments include the evolving structure of technical teams and career ladders. Traditional L1-to-L3 progression is under scrutiny as automation and AI begin to erode the volume of entry-level roles. While the shift is gradual, Dave Cava noted that "talent factories," or MSPs able to internally develop staff, are increasingly necessary. Process and operational maturity, not early AI adoption alone, are indicated as pre-requisites for sustainable growth, as merely adding new technology does not solve the underlying margin or process challenges.
For MSPs and IT leaders, these dynamics translate to concrete operational risks: underestimating cost structure, slow hiring processes, and reliance on commoditized pricing expose businesses to margin erosion and slow response to market shifts. Building robust, value-based pricing strategies, investing in internal talent development, and streamlining hiring and onboarding are positioned as necessary—but not sufficient—conditions for survival. Rapid AI adoption without foundational process discipline creates more risk than opportunity in the current market landscape.
Supported by:
Proofpoint
HaloPSA
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 SponsorsMSP Radio is supported by our partners:
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The episode outlines a structural shift in managed services and IT operations: the automation and unbundling of junior technical work due to the integration of automated tools and AI-driven solutions. This change is absorbing the traditional entry-level, apprenticeship-oriented roles within MSPs and IT organizations, fundamentally altering career development pathways. The trend is illustrated by specific product launches and research findings from entities such as TeamViewer, RDE Technologies, the Center for an Urban Future, the Bureau of Labor Statistics, and SignalFire.
Primary evidence centers on quantitative labor data. According to the Center for an Urban Future, entry-level tech job postings in New York City declined 49% since 2022, while Bureau of Labor Statistics projections show a 3% decline in employment for computer support specialists by 2035, amounting to a reduction of 24,300 roles. At the same time, new graduate hiring at large technology firms and startups has dropped by 65% and 76%, respectively, according to SignalFire. In contrast, higher-skilled technology roles—including data scientists and security analysts—are forecasted to add over 310,000 positions over the same timeframe.
Secondary developments reinforce the trend. TeamViewer released a support agent that automates fixes with senior technician approval, while RDE Technologies adopted a tool (Vight) automating ticket notes, time entries, and coaching data from support calls. These tools reduce learning opportunities for junior staff. On the hiring side, research highlights that junior hires are arriving at pay rates equal to or above existing staff, creating training, motivation, and retention challenges. At the senior end, a noticeable increase in exits from AI-exposed professions among older workers represents a further supply squeeze.
The operational implication for MSPs and IT leaders is a direct challenge to traditional hiring and talent development strategies. Automation is reducing the volume of teachable, ticket-based tasks necessary for hands-on training, while elevating compensation for new and senior staff. MSPs face a choice: deliberately reserve real client work for skill development—accepting lower margins to “manufacture” future engineers—or compete for costly senior talent amid a shrinking candidate pool. Budgeting for training must become an explicit, defended line item, and pricing should reflect the operational burden of nurturing internal talent versus buying it on the open market.
00:00 The Job That Stopped Existing
03:48 Built Out Of Easy Tickets
06:54 The Engineer Isn't For Sale
11:03 Why Do We Care?
Supported by:
WebPros(CometBackup)
USecure
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 dominant structural shift outlined is an accelerating concentration of market power and operational control within a handful of large technology companies and platforms, exacerbated by aggressive vendor channel consolidation and a move toward marketplace-based service delivery. This concentration is evidenced by recent actions such as Broadcom’s decision to cut roughly 90% of VMware’s partners and take top-tier accounts direct, as well as the increasing tendency of hyperscalers and major vendors—including Microsoft, AWS, and Google—to funnel services and resources directly through their own marketplaces and forward-deployed engineering teams. Reports discussed, such as the Omdia Global Partner 1000, reinforce the extent to which the industry has pivoted toward highly scaled players at the expense of smaller channel partners and MSPs.
Evidence from the Omdia Global Partner 1000 report demonstrates that the top 30 service partners now generate the same amount of revenue as the bottom 970 combined, with the remaining 970 firms outperforming over a million additional smaller providers. The managed services market was noted at $608 billion—1.5 times the size of the global SaaS industry and all hyperscalers—yet smaller MSPs report decreased growth expectations and declining vendor satisfaction; for example, satisfaction in the UK and Ireland dropped from 37% to 19%. Further, partner programs for generative AI remain underdeveloped, with over 90% at only “maturity 3 of 10,” while 82% of MSPs acknowledge they are not prepared to scale as rapidly as customer demand for AI-driven outcomes will require.
Additional developments deepening this concentration include widespread launches of vendor-controlled marketplaces and the growth of token-based consumption models. Companies such as SuperOps, ManageEngine, and Pax8 are positioning their platforms as marketplaces for MSP-delivered AI and SaaS, while Microsoft and AWS continue to expand both their direct-to-customer strategy and investments in pre-sale technical resources. Analysts project that the shift toward token-based billing and variable consumption will disrupt traditional per-user pricing, limiting future margin opportunities and accelerating direct transactional relationships between vendors and end-customers.
For MSPs and service providers, these shifts increase dependency on large vendor platforms, raise the risk of abrupt contract changes, and intensify pricing and margin pressure. Traditional models relying on single-source vendor relationships and predictable per-user or per-device billing are likely to be replaced by variable, consumption-based contracts governed by token usage and direct marketplace transactions. Providers must prepare for heightened governance requirements, increased operational complexity in managing multi-vendor and multi-marketplace integrations, and potential threats to their role as strategic intermediaries in client accounts.
Supported by:
Proofpoint
Guardz
USecure
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 SponsorsMSP 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.
🚀 Join Business of Tech Plus
Get exclusive access to investigative reports, vendor analysis, leadership briefings, and more.
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