Sherweb Launches Portal for MSPs; AI Pricing Evolves and Generative AI Risks Prompt Caution

[00:00:02] It's Friday, June 6th, 2025 and I'm Dave Solt. Four things to know today. Sherweb Launches a White Label Portal that doubles as a Revenue Lever for MSPs. AI Pricing Gets Real with Usage-Based Models from Globant and Salesforce. Generative AI Risks Surge while Regulation and Readiness Lag and the CISO role Faces a Fundamental Identity Crisis. This is the Business of Tech.

[00:00:31] Sherweb has launched a White Label Self Service Portal designed to empower managed service providers and their clients by streamlining everyday operational tasks. This new platform allows clients to manage their own technology licenses, subscriptions and payments, reducing the reliance on service providers for routine inquiries. According to Rick Stern, Senior Director of Platform at Sherweb, giving autonomy to clients not only expedites handling of simple requests but also allows

[00:00:59] managed service providers to focus on high-level strategic initiatives. The portal includes features like automated invoicing through PayPal, curated service catalogs and integrated chat support for faster issue resolution. Sherweb's solution is currently in use by over 450 managed service providers after a successful pilot program. Why do we care? Platforms like this reduce noise in day-to-day service delivery, improving customer satisfaction while creating operational scale. Notably,

[00:01:29] Sherweb is embedding monetizable features like curated service catalogs and automated billing. Not just a convenience, but a revenue capture and retention lever. If you're not capable of building your own e-commerce solution, this is a solution. Sherweb's move is part of the necessary tooling evolution for MSPs to scale without proportionally scaling headcount. But it's only valuable if MSPs

[00:01:53] actively reinvest that reclaimed time. Providers should treat portals like this as an ingredient in a broader transformation, freeing their teams to act as business advisors, not subscription administrators. This is also a reminder to MSPs not to let vendors own the customer experience. Even in white label form, the portal must feel like a branded extension of the MSP's value, not a convenience wrapper on

[00:02:19] Sureweb's back-end. The next competitive frontier isn't just automation, it's the narrative and outcomes you build on top of it. And from my vantage, e-commerce is a requirement for providers, not an option. Today's episode is supported by Huntress. Most cybersecurity solutions are built for massive enterprises with big budgets. Not Huntress.

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[00:03:32] Globant, a leading tech consulting firm, has transformed its traditional billing strategy to embrace artificial intelligence by introducing subscription-based AI pods. This new model allows clients to access AI-powered services, including software engineering and product design, through a monthly subscription that employs a token-based system for usage. The shift comes as generative AI technology drives changes across industries, compelling companies to rethink their pricing models.

[00:04:02] Globant's CEO, Martin Mayaga, emphasized that this approach represents a significant departure from the conventional effort-based billing models, which often leads to misalignment between service providers and clients. The firm aims to stabilize pricing and minimize surprises in the evolving landscape of AI-driven services, where outputs are increasingly determined by token usage rather than hours worked. Early adopters of this model include notable clients in the finance and entertainment sectors,

[00:04:31] indicating a potential trend toward subscription-based consulting services in the broader industry. Salesforce's senior executive, Bill Patterson, emphasized the need for flexibility in artificial intelligence pricing models, acknowledging that no vendor has definitively established the best approach. He stated that AI providers, including standalone model providers and cloud companies, are still trying to determine which pricing model is most effective for various use cases.

[00:04:58] Salesforce has experimented with different pricing structures, including a $2 charge per AI agent conversation, and a model based on AI actions, such as updating customer records or automating workflows. Recently, the company introduced a flexible pricing system called AgentForce, which combines conversation and action-based pricing alongside a credit-based system to enhance user flexibility. Patterson noted the potential for outcome-based pricing,

[00:05:25] but cautioned that it can be difficult to scale due to the subjective nature of determining contributions from AI and human input. Why do we care? Well, we care because these moves signal a critical inflection point in how AI services are being monetized, with deep implications for IT service providers. Traditional billing models built around time, effort, or vague project scopes are increasingly misaligned with AI-driven delivery, where output, not hours, defines the value.

[00:05:54] The billing transformation we're witnessing isn't about payment mechanics. It's about how the value is defined and delivered in the AI era. For providers, this means rethinking offerings to align with usage-based economics and outcome-driven expectations. The global model suggests an early move toward productizing services, bundling AI capabilities with advisory layers in a way that supports scale and predictability. However, caution is warranted.

[00:06:23] Most MSPs are unprepared to adopt token-based or usage-tiered pricing without foundational changes to how they scope, deliver, and support services. The smart play now is experimentation in controlled segments, paired with strong analytics and transparency tools. Those who master pricing innovation early will define how trust and margin coexist in an AI-powered services economy.

[00:06:50] A recent report from the data management firm Compromise reveals that nearly 80% of IT leaders have observed negative consequences resulting from the unauthorized use of generative artificial intelligence tools within their organizations. The survey, which pulled 200 IT directors and executives from U.S.-based enterprises with over 1,000 employees, highlights significant concerns regarding privacy and security, with 90% of respondents expressing worry about the risks associated with shadow AI.

[00:07:19] Among the adverse outcomes reported, 46% of IT leaders cited false or inaccurate AI-generated outputs, while 44% noted leaks of sensitive data into AI models. In response to the challenges, 75% of IT leaders plan to adopt data management platforms, and 74% are investing in AI discovery and monitoring tools to better oversee the use of generative AI in their networks.

[00:07:44] A Stanford professor has developed an artificial intelligence fund manager that's outperformed 93% of human stock pickers, raising concerns about the future of junior analysts in the finance industry. This AI tool, likened to a Terminator for the finance sector, demonstrates the ground capabilities of machine learning and making investment decisions. The performance of this AI-driven fund manager highlights a significant trend in finance,

[00:08:11] where technology is increasingly taking over roles traditionally held by humans. But a recent study reveals that artificial intelligence is not yet capable of making clinical diagnoses from radiological scans, despite its potential to transform the field. Researchers from institutions including Johns Hopkins University and the University of Bologna, developed a benchmark test called Deep Tumor VQA, which utilized over 9,000 computed tomography volumes

[00:08:40] and involved expert-level questions focused on abdominal tumors. The study evaluated five visual models designed for healthcare and found that while these models performed better than random guessing and measurement tasks, their recognition capabilities ranged from 65% to 86% accuracy. However, the model struggled significantly with medical reasoning tasks that required integration of unseen training data.

[00:09:06] The authors conclude that while current visual language models show promise in basic tasks, their applications in real-world diagnostics remains limited, emphasizing the importance of human judgment in clinical settings. But in the legal sector, regulatory approval of Garfield AI in the UK marked the emergence of an artificial intelligence-based legal firm capable of managing small claims litigation with minimal human involvement.

[00:09:34] The Solicitor's Regulation Authority, which granted this approval, describes it as a landmark moment for legal services in the United Kingdom. Garfield AI allows claimants or their lawyers to upload relevant case information, enabling the platform to generate necessary legal documents, handle settlements, and prepare for trial. According to Daniel Long, co-founder of Garfield AI, the service aims to provide small businesses with access to legal processes

[00:10:00] that are often seen as economically unfeasible when hiring a lawyer. Long expressed that the approval signifies a shift towards integrating technology into legal services, with the potential to enhance accessibility and affordability for consumers. The Solicitor's Regulation Authority is advocating for further development of AI-driven legal services, citing the potential benefits for individuals and small businesses facing challenges in accessing legal support. Why do we care?

[00:10:28] Comproviso's report reveals a harsh truth. AI is already in the enterprise, even where it's not sanctioned. The emergence of Garfield AI in legal services and the Stanford-backed fund manager in finance show how AI is rapidly encroaching on high-value knowledge work, automating tasks traditionally held by junior professionals. In both cases, AI delivers substantial functional value, low-cost litigation processing or superior portfolio performance, at a fraction of the traditional human cost.

[00:10:58] But the deep tumor VQA study is a vital reminder. AI still fails at reasoning in critical high-context scenarios. Healthcare AI models that can't generalize to new inputs are not ready for autonomous clinical decision-making. Despite all the headlines, AI often lacks domain fluency, memory, and nuance needed for high-stakes judgment. Costs all these stories is a caution. Adoption is outpacing readiness.

[00:11:24] Whether it's unmonitored generative AI in the enterprise, legal automation for SMBs, or overconfident diagnostic AI, the tools often exceed the governance infrastructure around them. IT providers who chase flashy AI offerings without a foundation in data management, identity protection, or compliance will likely contribute to the chaos rather than lead the solution. Additionally, there's a growing inequity in AI benefit distribution.

[00:11:50] Big firms with governance capacity and legal teams can afford to experiment safely, while smaller orgs risk being early casualties of misused or misunderstood tech. AI's next growth curve will be governed, specialized, and embedded. And that's where IT service providers can lead. Generic AI enthusiasm is waning. What's rising is demand for secure, by-design, AI deployments that prevent shadow use.

[00:12:15] Vertical-aligned tools like Garfield AI, tailored for specific business needs. And value framing that helps clients understand what AI can and cannot do. IT services professionals must now act as AI interpreters and integrators, helping clients navigate risk, translate capabilities, and build systems where humans and AI coexist with accountability.

[00:12:38] The winners will be those who stop selling AI as magic and start delivering it as managed, measurable, and meaningful. Time for some big ideas. In TechNative, cybersecurity expert J.C. Galliard argues that the current role of the Chief Information Security Officer, or CISO, is failing in many organizations. He highlights that although the CISO position has existed for nearly 30 years, it has become increasingly complex and burdensome,

[00:13:07] leading to high stress levels and short tenures for those in the role. He points out that the CISO was never truly established as a C-level position, often filled by technologists rather than individuals with strategic business acumen. This misalignment has resulted in a focus on compliance and risk management rather than proactive cybersecurity measures. The article cites a need for organizations to reevaluate the role, suggesting a split between the CISO and a new chief security officer

[00:13:36] to better address the complexity of modern cybersecurity threats. According to Galliard, this approach could foster better engagement with senior executives and improve the overall effectiveness of cybersecurity strategies. A recent article by Nicholas Carr highlights concerns about the rise of generative artificial intelligence in education, suggesting that while it may enhance productivity, it fundamentally undermines the learning process. Surveys indicate that nearly 90% of college students and over 50% of high school students

[00:14:06] are using chatbots for their assignments, leading educators to fear that this trend discourages critical thinking and genuine understanding. Carr emphasizes that generative AI is an automation technology that replaces human labor with significant implications for skill development. According to a study from the University of Pennsylvania, while access to AI tools like ChatGPT may improve student performances in the short term, it can result in worse outcomes when those tools are removed,

[00:14:34] suggesting a detrimental impact on long-term learning. Clay Shirky, vice provost for AI and technology in education at New York University, notes that the relationship between student outputs and their learning experiences is increasingly decoupled, raising serious questions about the future of education. And according to the Wall Street Journal, Morgan Stanley has developed an in-house artificial intelligence tool called DevGen.ai,

[00:15:02] which has significantly improved the process of updating legacy code. This innovative tool has reviewed 9 million lines of code this year, resulting in a remarkable savings of 280,000 hours for developers. The tool, built on OpenAI's GPT models, translates outdated programming languages like COBOL into plain English specifications, enabling developers to rewrite the code more efficiently. As enterprises increasingly face challenges in modernizing software,

[00:15:31] Morgan Stanley's approach addresses one of the industry's persistent pain points major technology companies have struggled to resolve. Why do we care? Let's ask some questions to ponder. Is your CISO or vCISO offering truly empowered to drive security strategy, or are they stuck in a reactive compliance-first position? If your clients rely heavily on AI to shortcut effort, are you helping them build sustainable practices?

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[00:16:56] they prepare. It's National Donut Day. I mean, with that one, I'm done. You don't need more than that. Go get your donut. Join me for a webinar sponsored by Nerdio, modern endpoint management with Intone, what works and what doesn't. Visit bit.ly slash nerdiowebinar to sign up and register now. Link in the show notes. The Business of Tech is written and produced by me, Dave Sobel,

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