In 10 minutes daily, The Business of Tech delivers the latest IT services and MSP-focused news and commentary. Curated to stories that matter with commentary answering 'Why Do We Care?', channel veteran Dave Sobel brings you up to speed and provides resources to go deeper. With insights and analysis, this focused podcast focuses on the knowledge you need to be effective, profitable, and relevant.
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Insightful and Workflow Analytics: Measuring Activity vs. Actual Value for MSPs
Episode 2071
Friday, October 2, 2026 • Duration 13:42
The episode highlights a structural gap between projected AI-driven revenue in the channel and the concrete operational changes needed to capture measurable ROI. Industry surveys from Informa MSP 501, McKinsey, and PwC consistently show that while AI adoption rates among managed service providers (MSPs) are high, few organizations have restructured workflows or business models around AI. The focus remains on superficial activity tracking rather than genuine business transformation.
The MSP 501 survey found 57% of providers expect significant AI revenue growth, with 91% reporting some level of adoption. However, McKinsey’s analysis, as presented by IHL Group, shows only 11% of companies have comprehensively rebuilt processes around AI, with a minority of those realizing clear benefits. PwC’s CEO survey aligns, with over half of respondents seeing no substantive financial returns from AI unless it is extensively integrated beyond the user level.
Products like Insightful workforce analytics are now entering MSP portfolios, offering discounted access to AI usage tracking tools. Yet data from Visier and the OECD warn that monitoring software can promote superficial compliance—about half of workers admit to overstating AI usage, and most lack control over the data collected. The OECD also finds that this monitoring is often embedded in standard business software rather than dedicated AI platforms.
For MSPs, the risk is prioritizing activity measurement over outcome-based change. Vendors promote usage reports, but these often reward appearances rather than operational improvement. Meaningful results depend on redesigning workflows and measuring actual process improvements—not merely collecting tool usage statistics. Before offering AI usage metrics to clients, MSPs should validate the effectiveness of workflow changes internally to avoid incentivizing unproductive patterns.
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Ryan Morris on Why Vendor Growth Depends on Active, Profitable Partners—Not Just Big Numbers
Episode 2070
Thursday, October 1, 2026 • Duration 40:34
The primary structural shift examined is the move from vendor emphasis on quantity of partner recruitment toward a more nuanced focus on partner program health, accountability, and mutual business growth. This mechanism is highlighted by Dr. Backup’s acquisition by Hosvara, with the new owner, a former MSP operator, prioritizing the effectiveness and sustainability of the partner base rather than purely expanding headcount. The episode examines how explicit disclosure and management of active versus inactive partner numbers—rarely published in the sector—reflects a deeper push toward measurable outcomes and operational performance within indirect sales channels.
The standout evidence comes from Dr. Backup’s partner program, which has seen over 300 IT firms join since inception, but only 125 remain active. According to company statements, the new owner’s strategy is not product-centric but centers on leveraging the current partner base by integrating business coaching and operational support into the program. This approach is intended to drive growth through existing relationships, rather than relying on continuous recruitment or product expansion in what is described as an already saturated backup market.
Related developments reinforcing this shift include Microsoft retiring its most demanding MSP credential and ScanSource, a distributor, acquiring an MSP outright. Both actions signal that larger players are reorganizing their channel and partnership strategies, favoring authentic, measurable engagement over headline claims of partner volume. Discussion of the Pareto principle and active/inactive partner ratios further illustrates the risk of overreliance on recruitment metrics and the need for transparency and accountability regarding partner program health. The episode also critiques vendor behavior that distances itself from partner business performance, emphasizing the reputational and operational risks involved.
For operational leaders, this shift implies that evaluating vendor partnerships now requires greater attention to transparency regarding active engagement, business impact, and mutual investment in outcomes—not just product features or price. MSPs and IT service providers should probe vendors for clear data on partner program health, insist on evidence of sustained partner profitability, and treat orchestration skills and partner selection as risk mitigation strategies. The sustainability and business impact of a given vendor’s channel approach will increasingly affect operational costs, dependency risk, and go-to-market resilience.
Default Security Choices Leave MSPs Exposed as AI Bots Blend With Legitimate Users
Episode 2068
Wednesday, September 30, 2026 • Duration 13:20
Correction: In this episode, the name of Arkose Labs was mispronounced. Arkose Labs (arkoselabs.com) is the source of the commentary from founder and CEO Kevin Gosschalk.
A key structural shift identified is the growing governance gap created by AI agents that evade traditional detection and accountability measures. This challenge is exemplified by Meta’s Muse and OpenAI agents, which do not self-identify during interactions. As a result, determining agent activity and risk now depends on disclosures by the developer rather than the asset owner or operator, limiting the visibility and control of MSPs and IT service providers.
One consequential incident involved an OpenAI agent accessing both public and non-public files in the Australian government's health portal. The breach went unnoticed by government security for 54 days and was discovered during an internal OpenAI review, later reported voluntarily by the company. In retail, Amazon blocked Meta's Muse agent from its platform after it failed to identify itself and due to concerns about credential handling, according to statements cited by GeekWire. These events illustrate growing dependency on agent developers for incident discovery and disclosure.
Supporting developments include findings from Akros Labs that current rules are insufficient to distinguish customers, attackers, or bots, due to agents blending in as typical browsers. VentureBeat surveys show a decline in proactive agent isolation and a rise in uncontained incidents, indicating operational drift toward default-permissive security settings. While new standards from NIST for short-lived tokens and upcoming agent identification protocols are developing, enforcement and utility remain incomplete.
For MSPs and IT leaders, the immediate implication is the need to revisit client-specific controls. Default reliance on legacy bot rules increases undetected risk, while inaction effectively shifts governance to external agent vendors. Providers must decide whether to block all unauthenticated agent traffic—accepting potential business impact—or allow agents and rely on token expiration and allow-listing signed agents as standards evolve. Continuous monitoring and regular adjustment of controls are necessary to minimize harm when agent anonymity and developer-only surveillance persist.
Measuring False Guidance: The Untracked Risk in AI-Driven Ticket Resolution
Episode 2067
Tuesday, September 29, 2026 • Duration 14:04
The episode highlights a structural shift toward operational reliance on AI agents as service staff rather than support tools, raising new questions about skill degradation, checking mechanisms, and accountability in managed service environments. Shield Technology Partners and Microsoft exemplify this trend by deploying AI operating systems—Shield's Forge and Microsoft's Autopilot—that independently handle substantial parts of the IT support process, in some cases from intake to ticket closure, often without direct human approval or intervention.
According to Shield, its Forge platform is currently resolving approximately half of all actionable help desk tickets across its network of MSPs, with 92% of these resolutions occurring without technician time or explicit human oversight. On comparable tasks, Shield claims Forge resolves tickets 25 times faster than human technicians, yielding a median resolution time of 16 minutes. Microsoft’s latest Copilot update introduces Autopilot, which enables persistent, role-specific AI agents with individual user identities, email accounts, and organizational chart positions, further blurring the line between staff augmentation and staff replacement.
These technical developments are accompanied by evidence of skill decay among technicians and knowledge workers, as cited in IBM’s survey of over 10,000 HR leaders and employees. The ability to supervise, validate, and override AI output is named by 71% of HR executives as an essential future skill, yet only 38% of employees agree, while 60% acknowledge that AI is eroding critical thinking. The discussion also touches on the operational risks documented within OpenAI, where human checkers are expressly forbidden to use AI-based tools to audit AI output, underscoring the persistence of human-in-the-loop requirements even as automation rates increase.
Operational implications for MSPs include the need for new governance measures and skill tracking metrics. The podcast proposes a practical protocol for MSPs: periodic resilience drills that benchmark technicians' ability to catch false or misleading AI-generated guidance. These exercises would generate a proprietary “false guidance acceptance rate,” providing a critical datapoint missing from current vendor dashboards. The analysis suggests that without active measurement of this kind, throughput statistics alone may mask growing dependency risks, skill atrophy, and unrecognized exposures as AI handles a rising share of support work.
AI-Driven vCISO Platforms Create Pricing and Liability Shifts for MSPs—David Primor of Cynomi
Episode 2066
Monday, September 28, 2026 • Duration 26:49
The primary structural mechanism examined concerns the shift in cybersecurity operations for MSPs driven by increased automation and AI-powered platforms, specifically as evidenced by Cynomi’s virtual CISO (vCISO) solutions. This transition signals a migration of both operational workload and expertise from traditional, manual processes toward digitally augmented roles, with significant implications for how accountability and liability are managed within managed security services.
According to the facts presented by Cynomi’s CEO, the company’s recent integration-focused releases enable MSPs to accelerate assessment and compliance processes, claiming reductions from multi-day efforts to under 60 minutes in some cases. The platform aggregates data from existing MSP ecosystems (including PSA, EDR, and vulnerability management tools such as Tenable and Microsoft integrations) to assemble remediation plans and operational roadmaps, often with minimal human mediation. Cynomi states that a single vCISO operator, using these tools, can service up to 10-12 clients compared to 5 previously, with some projections reaching as high as 50. The company positions its technology as supplementing, not fully replacing, human expertise—though it acknowledges the potential for junior personnel, aided by AI, to bridge previous capability gaps.
Supporting this dynamic, the episode explored how efficiency gains introduce questions around pricing, market access, and liability. Dave Sobel questioned the sustainability of pricing models anchored on scarcity of human CISOs when automation multiplies operator capacity and competitors adopt similar tools. The conversation highlighted that as AI reduces human labor in delivering assessments and compliance, market prices could decline, but the accessible market might expand, especially among previously underserved SMBs. Liability, however, remains with the MSP; automated recommendations must still be reviewed and approved by a designated human. Concerns were raised regarding insurance exclusions when AI-generated security policies are implicated in claims, prompting focus on the importance of maintaining human oversight and evidentiary processes.
Operationally, MSPs face heightened pressure to clarify vendor and personnel accountability, reinforce internal QA on AI-driven deliverables, and rethink service economics in light of scalable automation. Vendors providing AI-enabled security assessments shift both risk and workload, but do not absolve providers of responsibility in the event of customer breaches or compliance failures. Insurers are beginning to scrutinize and sometimes exclude AI-generated outputs from coverage, underscoring the importance of documenting processes and retaining subject-matter oversight. These developments compel IT service providers to reassess vendor relationships, liability boundaries, and training needs for staff operating in an increasingly automated environment.
Why Aurora AI Agents Only Reduce Ticket Load When Built on Accurate Network Data — Steve Petryschuk
Episode 2064
Sunday, September 27, 2026 • Duration 16:16
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.
AI Margins and MSP Growth: Dr. Gleb Tsipursky on Passing Savings vs. Competing Away Profit
Episode 2065
Saturday, September 26, 2026 • Duration 28:23
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.
Active vs. Joined Partners: The Overlooked Risk in Vendor Programs
Episode 2063
Friday, September 25, 2026 • Duration 13:37
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.
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Flamingo’s AI Token Pricing Model Shifts Margin Risk for MSPs – With Rich Freeman
Episode 2062
Thursday, September 24, 2026 • Duration 42:38
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.
Vendors Build the Meter: MSP Risk Rises as AI Usage Moves to Consumption Pricing
Episode 2061
Wednesday, September 23, 2026 • Duration 13:11
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.
