Summer 2025 Sponsored by Private Cloud Reset ID PROOFING Volume 24 Issue 4 | channelvisionmag.com TSD Arms Race Helping Advisors Speak AI LANGUAGE LEARNING Scan to Register
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SUMMER 2025 AI & AUTOMATION 8 Agentic distrust 8 Bubble talk 8 AI onboarding 10 Latency Kills Five reasons interconnection is the missing link in AI infrastructure By Ivo Ivanov 12 Large Language Learning Helping partners speak AI with customers By Martin Vilaboy VIRTUAL REALITIES 18 Beyond A Single Pane of Glass The importance of contextualized network management powered by AI By Jamie Pugh CORE COMMUNICATIONS 22 Changing the Conversation It’s time for advisors to get introduced to vCons By Martin Vilaboy CHANNEL MANAGEMENT 28 Branch Out at CVxEXPO25 30 Let The Channel Manage It By Glen Nelson 32 TSD Arms Race TSDs are investing heavily to attract more advisors and help them close more deals By Gerald Baldino 36 ForgeOS: Leveling the Playing Field for Technology Advisors Gerald Baldino CYBER PATROL 36 Securing the Distributed Workforce Why robust identity proofing is the unsung hero of remote work security By Pavin Guduri 40 Zero Trust RMM: The New Security Standard for MSPs LogMeIn Resolve helps MSPs to ‘never trust, always verify’ for enhanced security MOBILE & WIRELESS 42 Thinking Inside the Box RYTHMz’s ruggedized ‘Internet in a Box’ solution redefines how MSPs deliver connectivity By Gerald Baldino EDGE TO CLOUD 44 Cloud Reset Private cloud takes a larger role in a ‘workload-first’ world By Martin Vilaboy 6 Editor’s Letter 48 ICYMI 50 Ad index CONTENTS Volume 24 – Issue 4 25 Glend Novem 25 Glendale, AZ November 3-5 Scan to Register 4 CHANNELVISION | SUMMER 2025
As organizations rush to embrace AI, virtualization, cybersecurity advances and industry-specific solutions such as smart manufacturing, telecommunications providers suddenly find themselves in a somewhat strategic and strong position, argue McKinsey analysts. “After a prolonged period of disruption spurred by new technologies, new competitors, rapidly evolving customer demands and dwindling opportunities in the B2C space, the telecom industry is now in a position to catalyze rapid B2B growth,” said the research firm in a recent report from its Technology, Media & Telecommunications practice. This is partly due to the key role connectivity plays in bringing the aforementioned digital and transformation plans to life. Aggregated global bandwidth demand tripled between 2019 and 2023, said McKinsey, and is expected to continue growing. As organizations venture further into AI adoption, McKinsey surveys show that respondents expect to spend 2.5 to 3.5 percent more this year than last year on legacy connectivity services and 6 percent more year-over-year on “next-gen” connectivity services such as 5G and SD-WAN. Also at issue is the even larger opportunity around “accessorizing access sales,” or what McKinsey calls the “information and communications technology” (ICT) play, whereby operators leverage their customer relationships by coupling connectivity with value-added products and services in areas such as cloud infrastructure, IT managed services and cybersecurity. According to McKinsey’s recent survey of IT decision makers, more than half of companies planned to increase telecommunications and technology spending this year, and 80 percent of these decision makers see telecom operators as viable partners for products and services beyond core connectivity. And while ICT already is driving growth for some telcos, the telecom industry overall accounts for just 12 percent of the ICT market. “As organizations demand faster connections, lower latency, more comprehensive security and a deeper understanding of the benefits that advanced connectivity can bring, telcos can lead B2B customers into the future while reducing their own reliance on legacy revenue streams,” said McKinsey. Not that things will be easy. Providers of telecom services will continue to wrestle with the decline of traditional value pools, such as voice and MPLS, while competition from hyperscalers and other non-telco players remains intense, as a quarter of decision makers said they would consider moving business away from telcos if viable alternatives materialized. Winners will be the contestants that position themselves as mission-critical partners, complete with knowledgeable sales teams that are highly attuned to customer needs, the ability to illustrate the value of solutions rather than the technical superiority of products, and marketing campaigns centered around specific customer needs and pain points. “To stay relevant, operators will need to prove they are not simply resellers but true integration partners providing end-to-end solutions – an important aspect of the value proposition, according to 55 percent of decision makers,” said the research firm. It’s the old “consultative versus transactional sales” approach. And while it may not be newest advice, McKinsey analysts still see it as some of the most salient advice for service providers. A Good Time to Know Telecom LETTER Martin Vilaboy Editor-in-Chief martin@bekabusinessmedia.com Gerald Baldino Contributing Editor gerald@bekabusinessmedia.com Tony Jones Contributing Editor tony@bekabusinessmedia.com Percy Zamora Art Director percy@bekabusinessmedia.com Jen Vilaboy Ad Production Director jen@bekabusinessmedia.com Berge Kaprelian Group Publisher berge@bekabusinessmedia.com (480) 503-0770 Beka Business Media Berge Kaprelian President and CEO Corporate Headquarters 10115 E Bell Road, Suite 107 - #517 Scottsdale, Arizona 85260 Voice: 480.503.0770 Email: berge@bekabusinessmedia.com © 2025 Beka Business Media, All rights reserved. Reproduction in whole or in any form or medium without express written permission of Beka Business Media is prohibited. ChannelVision and the ChannelVision logo are trademarks of Beka Business Media 6 CHANNELVISION | SUMMER 2025
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For most of the small businesses recently surveyed for RingCentral, the top challenge they experienced around AI integration involved data security. Things change, however, when it comes to very small businesses (VSBs). While nearly four in 10 firms (37.27%) with 400 or more employees said data security was their top concern, and a third of firms with 21-99 employees said the same, that figure dropped to 15.7 percent among small businesses with 0-20 employees. For those very small businesses, a complex “onboarding process with unclear expectations” was the number one concern, named by about 30 percent. It’s apparently a substantial challenge. Of course, it may not be the only resource constraint limiting AI adoption, but VSBs are falling behind their larger counterparts. Whereas the large majority of larger small businesses have integrated AI into customer conversations (such as through chatbots), just a third of VSBs surveyed by RingCentral have some the same. Data recently published by Genesy indicates there are some disconnects between the readiness and willingness of businesses to deploy agentic AI and the trust and acceptance of consumers who’ll ultimately interact with it. While 81 percent of CX leaders trust agentic AI with sensitive customer data, just 36 percent of consumers feel the same. Similarly, 74 percent of businesses indicated they’re comfortable using agentic AI for billing, financial transactions and account security, while consumers were more hesitant. Of those surveyed, only 35 percent of consumers were comfortable with the technology handling money transfers, 49 percent with resolving billing issues and 50 percent with updating personal information. When it comes to overall customer service resolution, though, more than half of consumers (58 percent) said they don’t care whether the issue their experiencing is resolved by a human or AI as long as it’s “handled quickly and completely.” This indicates that the efficiency and effectiveness of the technology can overcome consumer skepticism, analysts said. OpenAI CEO Sam Altman raised fears of an AI bubble with comments in a recent interview with The Verge. “Are we in a phase where investors as a whole are overexcited about AI? My opinion is yes,” said Altman. “Is AI the most important thing to happen in a very long time? My opinion is also yes.” Perhaps Altman knows that one of ever two venture capital dollars are going to AI startups, according to findings by CB Insights, and AI funding in the first half of 2025 already surpassed 2024’s record full-year total. “Startups are raising at sky-high valuations,” said CB Insight analysts, “with multiples crossing 100x.” AI is transforming how work gets done, and the pressure on networks is mounting, said researchers at Cisco. According to its survey of more than 8,000 senior IT and business leaders responsible for networking strategy and infrastructure, a major architectural shift is underway across enterprise networks, as AI assistants, agents and data-driven workloads create faster, more dynamic, more latency-sensitive and more complex network traffic. • 94% of respondents say AI, IoT and cloud computing will have the greatest impact on their network in the next two years; 62% say it’ll be down to AI alone • 97% say a modernized network is critical to rolling out AI, IoT and cloud • 91% plan to increase the share of IT budget allocated to networking • 71% say their data centers can’t scale AI • 88% plan to expand capacity to support AI workloads across on-prem, cloud or both • Only 11% of data centers are fully optimized for AI workloads with advanced capabilities • 98% say AI-native, autonomous networks are essential to future growth “The network is no longer just infrastructure. It’s strategy. It’s value. And it’s the priority for leaders,” said Cisco researchers. VSBs Need Help Onboarding AI Genesys Survey Reveals Enthusiasm, Distrust for Agentic AI AI Bubble Alert AI Transforming Infrastructure AI & AUTOMATION Have Integrated AI in Customer Conversations 400+ employees 81.55% 100 to 388 employees 74.55% 21 to 99 employees 55.36% 0 to 20 employees 33.33% Source: RingCentral 8 CHANNELVISION | SUMMER 2025
Latency Kills AI that has the potential to revolutionize entire industries has long passed its demo stage; it now runs through the veins of businesses and will soon become the lifeblood of our economy. From fraud detection and humanoid robotics to autonomous vehicles and real-time language processing, AI is now expected to operate instantly, intuitively and everywhere. But with this shift comes a new bottleneck: latency. No matter how powerful the model or how abundant the compute power, if the network can’t deliver data with single-digit millisecond precision, AI won’t deliver the intended results. The reality is simple – without ultralow-latency connectivity, there is no viable future for AI at scale. That’s why I was pleased to join Tonya Witherspoon of Wichita State University and Hunter Newby of Connected Nation Internet Exchange Points (CNIXP) for a webinar on one of the most overlooked constraints in digital infrastructure: round-trip delay (RTD). While our conversation covered AI, network design and public-private collaboration, the central message was clear: we cannot solve tomorrow’s challenges with yesterday’s networks. Latency isn’t just a technical metric; it’s an economic limiter, a competitive differentiator, and now a make-or-break component of AI. Below are five key discussion points from our webinar, titled “Latency Kills: Solving the bottleneck of RTD to unlock the future of AI,” on why solving the latency challenge – both locally and nationally – is the next critical step on the road to AI mastery. 1. Low latency is no longer optional AI applications are no longer abstract, back-end computations; they are real-time, front-line systems that increasingly underpin daily life. Whether it’s a fintech company performing fraud detection at a keystroke, a vehicle processing sensory data on the move or a manufacturing plant using robotics for precision tasks, latency has become the hard ceiling of performance. As I’ve said many times before, latency is not just a metric – it’s currency. For 4K streaming, the boundary is around 15 milliseconds. For high-frequency trading and autonomous driving, it’s under 5 milliseconds. And when we enter the realm of humanoid robotics and AI agents that interact like humans, we’re talking about single-digit millisecond responsiveness, and that translates to a physical radius of 50 to 150 miles. Beyond that range, the round-trip delay is too high, and the application breaks down. As Newby puts it, “Fraud detection from the major banks is something that they want to do at the keystroke, on the phone, as it’s occurring. That’s a 3 or sub-3 millisecond requirement. Without the right physical infrastructure in place – land, buildings, fiber and an internet exchange – it simply can’t happen. We’re talking about needing thousands of facilities like that across the U.S., and they don’t exist.” This is the kind of performance that enterprises must now design for, and it’s impossible to achieve without rethinking where infrastructure lives and how data moves. Adding more compute capacity won’t solve the problem if By Ivo Ivanov AI & AUTOMATION Five reasons interconnection is the missing link in AI infrastructure 10 CHANNELVISION | SUMMER 2025
the data can’t get there and back in time. The only way forward is through proximity; by building the right interconnection points in the right places, we can shrink that round-trip delay and make real-time AI a reality. 2. Geography matters For decades, network infrastructure has mirrored population density and economic gravity, clustering around coastal metros and skipping over large swathes of the country. The result is what Newby described during the webinar as “flyover cities” – places where fiber may pass through but never breaks out. These cities aren’t disconnected; they just have poor accessibility. And that has real consequences for latency. Every extra mile a packet travels adds delay, which adds cost, which in turn limits the viability of emerging AI services. This is why geography matters. If we want real-time digital experiences, whether for a bank customer in Kansas or an autonomous vehicle in rural Texas, then we need physical infrastructure built where the people, machines and data actually are. Newby offered an analogy that resonated with many: “Think of the internet like air travel. Nobody wants to take three connecting flights to get somewhere. Everyone wants a direct flight. But right now, for many parts of the U.S., we’ve built the equivalent of runways with no airports.” And the stakes are rising. Applications such as autonomous vehicles and robotics don’t just benefit from direct interconnection – they require it. Which is why projects including the new IXP at Wichita State University are so important. By bringing neutral, highperformance interconnection into the heartland, we’re not just solving the latency problem, we’re rewiring the connectivity map. As Witherspoon put it, “It’s not just about bringing fast connectivity to the middle of the country in areas that don’t have it, but also for the redundancy and resilience that the entire country needs so that we’re not solely dependent on the few nodes that have already been built.” 3. Round-trip delay will define AI connectivity Round-trip delay (RTD) is becoming the defining metric for AI viability. It measures the time it takes for data to travel from the user to the compute instance and back again. In the context of AI inference, that journey needs to be completed in just a few milliseconds. And yet, RTD remains poorly understood, often confused with “fast fiber” or dismissed as a non-issue by those who focus solely on power and compute capacity. Newby made the point with some clarity: “Gone are the days where you could say, ‘I’ve got powered land and fiber is a mile away, so latency isn’t a problem.’ None of that makes sense anymore.” He’s right. The location of compute power, the route of fiber and the presence of a neutral interconnection point all now define whether an AI application can function as intended. This is especially true for applications that require deterministic routing, where the AI doesn’t just need access to data but needs it from a specific source, at a specific time, through a specific path. That’s what RTD is really about: not just speed but certainty. And to achieve that, we need to bring routing, interconnection and AI workloads into much closer physical alignment. 4. Enterprises must control their data journey or be disrupted by it In a post-AI world, the enterprises that succeed will be those that treat digital infrastructure as a core business asset instead of just a back-end IT function. Enterprises must think about their networks the same way they think about physical supply chains; something to control, optimize and secure. That means building presence in regional interconnection hubs, engaging directly with internet exchanges and designing architectures that support real-time performance at scale. If you want to monetize a digital asset effectively – whether it’s a vehicle generating real-time telemetry or a financial service delivering AI-driven insights – you need to control how and where that data flows. And this isn’t just theoretical. As I said in our discussion, if automakers don’t control the data flows into and out of the car, someone else will. The same logic applies across every industry. Witherspoon summarized this well when she said, “It’s not nearly enough to just think about your own building, service or product, because with inference – with all the data that’s moving – you don’t know where you’re going to connect or who you’re going to connect to.” Enterprises must extend their digital presence beyond their walls and build infrastructure strategies that align with how AI actually operates. Those who don’t will soon find themselves disrupted by those who do. 5. We need a new Internet for the AI era – and it starts locally We’ve entered a new phase of digital infrastructure – one that requires us to rethink not just how the internet works, but where it works. AI can’t be built solely on hyperscale data centers or global cloud backbones. It needs a new layer: local, neutral, high-performance interconnection that sits closer to users, devices and machines. We’re not talking about replacing the global Internet – just filling in the gaps. The future will depend on a distributed mesh of IXs, regional edge hubs and localized routing platforms that can support deterministic, low-latency data flows at scale. That’s why we’re building smaller, more accessible internet exchange models – what I like to call the “pizza box” IX – that can be deployed anywhere from shopping malls and universities to roadside fiber huts. Infrastructure isn’t just an enabler of AI – it’s the foundation. And unless we build it where people actually live and work, AI will remain an uneven promise: powerful in some places, inaccessible in others. The good news is that we now have a blueprint – combining neutral facilities, regional IXs and public-private cooperation – to ensure that the AI revolution reaches every corner of the map. o Ivo Ivanov is CEO, of DE-CIX. This article originally appeared on the DE-CIX blog. 11 SUMMER 2025 | CHANNELVISION
AI & AUTOMATION Helping partners speak AI with customers By Martin Vilaboy Large Language Learning For the second year in a row in the annual Telarus technology trends report, released at the technology solution distributor’s 2025 partner summit in Anaheim in August, artificial intelligence (AI) once again ranked as the leading driver of IT investment. And this time it’s with increasing momentum, as 58 percent of IT buyers surveyed identified AI as their top priority for 2025, compared to 53 percent in 2024 and a mere 13 percent in 2023. At the same time, a full 71 percent of technology advisors surveyed by Telarus believe AI will be a source of revenue growth in the next 12 to 24 months, up from the 65 percent who said the same in 2024. Despite the clear importance for both buyers and sellers, many technology advisors simply are not ready to advise on AI. A mere 13 percent said they feel very prepared to sell AI solutions. Nearly half (48 percent) admitted to either struggling to communicate AI’s value to clients or avoiding the selling AI solutions altogether. Nearly one-third (32 percent) said they are not discussing AI with customers or offering AI solutions at this time at all. Advisors we spoke to at the recent Telarus partner summit spoke to a lack of confidence, even trepidation, when it came to talking to customers about AI solutions and value propositions. So perhaps it’s no surprise that much of the conversations at the partner summit centered around what advisors can do to gain confidence and competencies when it comes to leading customers on the AI journey and selling AI solutions. And while, as Telarus chief revenue officer Dan Foster said, “there is no playbook for AI deployment,” findings in the technology distributor’s latest tech trends report provide partners with a strategy of attack, as well as a window into what buyers want to hear and the outcomes they want to see. 12 CHANNELVISION | SUMMER 2025
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Indeed, the importance of AI competency can’t be overstated. “Buyers aren’t just looking for access to technology; they’re seeking strategic advisors who can guide them through AI adoption, implementation and impact,” said the Telarus report. For example, 96 percent of mid-market firms are open to support from a new advisor, while three quarters said they were most interested in meeting a new advisor for help with AI. “Buyers are ready,” said Foster. “The question is, are you ready?” Not that anyone can blame advisors for being a bit intimidated. Few technology areas in the history of the networks services channel, including VoIP and broadband access, have been as disruptive or moved as rapidly as AI and automation. The good news is, advisors don’t need to get a Ph.D. in machine learning to play in the AI space. A common piece of advice is that it’s okay to start small. That can mean putting parameters around on what an agency needs to be an expert on, focusing on a subset of AI or the AI being used only within one core category of services that a firm already represents. “We’ve seen advisors win big (with AI deals) just by being the first one there to have that conversation,” said Josh Lupresto, senior vice president of sales engineering at Telarus, “because customer need help and they don’t know where to lean.” Advisors also can start with systems and parts of a business in which “it’s okay to be a little wrong,” many experts suggest. While that may be within verticals such as healthcare or financial services, there are usually places within most businesses that are not mission critical where AI-enabled solutions can have a positive impact. “Organizations don’t need massive rollouts to generate meaningful insights,” said analysts at McKinsey, in its report on how to accelerate AI adoption. “Some of the most valuable organizational experiments involve five to ten people over two to four weeks.” Dog Eat Dog Food Perhaps even before starting any conversations, many of the presenters at the Telarus partner summit suggested advisors heed the old axiom regarding “eating your own dog food.” In this instance, however, they don’t necessarily mean a provider using the AI solutions that it provides customers. Rather, they suggested agencies and advisors use any AI technologies within their internal operations and process. That could include tools such Nearly Half of Advisors Don’t Feel Prepared to Sell AI Not at all prepared – I avoid selling AI solutions 14% Not very prepared – I struggle to communicate AI’s value to clients 22% Somewhat prepared – I understand the basics but need more support 41% Very prepared – I have deep knowledge and expertise 13% Source: Telarus One in Three Advisors Aren’t Discussing AI with Clients (In what ways is AI integrated into your sales process? Select all that apply) Offering AI solutions for specific use cases and applications 33% I am not discussing AI with customers or offering AI solutions at this time 32% Using AI discussions to attract prospects and initiate conversations 29% Selling AI-enabled solutions (positioning AI as a feature or enabling technology) 25% Providing consultations on AI readiness (including data readiness and governess) 20% Other 5% Source: Telarus Biggest Challenges for Partners in 2025 Source: Futurum Research, channel decision maker surveys 0% 10% 20% 30% 40% 50% Percentage of Attack Source: ConnectWise Best Way Vendors Can Support Your Business, % Share of Repondents Source: Futurum Research, channel decision maker surveys 0% 10% 20% 30% 40% 50% Internal AI Use Strongly Correlates with Advisor Confidence in Selling AI How prepared do you feel to sell AI-powered technology solutions? How is your technology advisory business currently using AI? Source: Telarus Very prepared - I have deep knowledge and expertise Somewhat prepared - I understand the basics but need more support Not very prepared - I struggle to communicate AI’s value to clients Not at all prepared - I avoid selling AI solutions Rank 1 Rank 2 Rank 3 A challenging economy Highly competitive ecosystem Rising costs impacting our profits IT budgets under pressure Inability to find highly skilled talent Complex regulatory environment Supply chain challenges Limited funding/financing to support growth Lack of support from technology (vendor) partners Lack of competitiveness within our portfolio 17% 14% 17% 17% 15% 14% 8% 7% 7% 5% 8% 5% 5% 8% 13% 4% 7% 10% 9% 7% 9% 10% 5% 6% 12% 13% 15% 9% 14% 10% Rank 1 Rank 2 Rank 3 Developer tools Best-in-class technical support Training programs Early access and roadmap visibility Lead generation Co-sell support Dedicated account management Modern portals and tools Marketing resources Attractive commercial terms 14% 7% 9% 13% 13% 14% 12% 8% 9% 10% 8% 8% 10% 17% 15% 9% 7% 10% 9% 8% 7% 9% 12% 12% 8% 8% 9% 7% 10% 9% Using AI for Internal operations Using AI for marketing and sales Offering AI-embedded solutions to customers Exploring AI solutions but have not yet implemented them Running AI Pilot programs 21% 20% 29% 10% 24% 57% 58% 58% 51% 49% 20% 20% 13% 34% 22% 3% 2% 0% 5% 5% 0 10 20 30 40 50 60 40% 2019 2020 51% 14 CHANNELVISION | SUMMER 2025
as marketing automation, quoting tools, basic office productivity and organization, social media moderation and even text generation and summarization. The logic here is that using even simple AI tools will provide advisors with a better understanding of the outcomes that can be delivered, providing them more confidence and credibility when leading conversations with clients. Findings from Telarus seem to support this notion, as advisors who actively use AI within their own operations report significantly greater confidence in selling AI solutions. “Whether deploying AI for internal workflows or marketing, or running solution pilots, these advisors are more likely to understand real-world challenges and demonstrate what’s possible,” said the research report. Compared to the 13 percent of all advisors who feel very prepared to discuss AI with customers, 21 percent of those who use AI for internal operations feel the same way. Nearly eight in 10 of the advisors that use AI in internal operations are very or somewhat prepared. Very similar percentages were seen among technology advisors who are using AI for marketing and sales, as well as running AI pilots. Advisors using AI internally are also more optimistic. These partners are more likely to believe AI will drive more business and revenue in the next 12 to 24 months, showed the Telarus survey. “This suggests that hands-on experience not only builds credibility but also translates into stronger client conversations and more consultative selling,” said the report. Better Outcomes Once AI conversations are initiated, advisors must be careful not to get tied down in technical capabilities and general infrastructure modernization. Similar to other areas of technology being sold nowadays, advisors are recommended to anchor their AI messaging in specific business outcomes. And across both mid-sized firms (50 to 500 employees) and large enterprises (500+), decisionmakers for the most part are looking to AI to boost productivity and operational efficiency and improve customer experience. Telarus findings, however, did point to some clear distinctions between the desired outcomes of mid-market firms and large enterprises. Mid-market organizations, for example, are far more likely to emphasize innovation, modernization and improved employee experience, while large enterprise buyers a particularly focused on cost reduction and the headcount control provided by tools such as automation. Indeed, large enterprises are greater than 4x more likely to have investment decisions influenced by costcutting than mid-market organizations, showed the survey. Mid-market, meanwhile, tends to prioritize AI investments that deliver immediate operational improvements and measurable revenue impact. In general, the mid-market is outpacing large enterprise peers when it comes to AI adoption. These organizations tend to be more agile, more open to experimentation and more likely to embed AI across departments, often coupling AI investments with cloud modernization, cybersecurity upgrades and CX platform overhaul, said Telarus researchers. “While enterprises are tightening their belts, mid-market is not holding back with investment and experimentation as AI provides them a route to catch up in scale with their enterprise counterparts,” said the report. Mid-market firms also are 3x more likely to engage external advisors. “Identification of project motive early in a sales cycle will enable advisors to properly guide selling conversations based on desired outcomes,” advised the report. McKinsey & Co. analysts Bob Sternfels and Yuval Atsmon, for their part, recommend that participants focus on clear and specific goals and outcomes instead of vague hypothesis. Rather than “improve productivity with AI,” for example, begin with specific, testable predictions such as, “Using AI to automate your monthly reporting process will reduce the time spent by 50 percent while maintaining accuracy above 95 percent,” they continued. This becomes even more important as businesses shift their attention away from “horizontal uses cases,” such as enterprise-wide copilots and chatbots, and toward more challenging “vertical uses cases,” or those embedded into specific business functions and processes, suggest McKinsey findings. Data Sets Apart One way for an advisor to stand out is a willingness to dive into the tough but important questions around data readiness. In other words, is data available, high enough quality, properly structured and aligned with AI use cases. According to a recent report from MIT and Snowflake, more than three quarters of businesses lack a very ready data foundation to support generative AI. In a recent Salesforce study, 52 percent of CIOs cited untrustworthy data (poor accuracy, recency) among their top AI fears. And while one third of buyers ranked data readiness as a top IT buying driver, only a small share of channel partners has How is your technology advisory business currently using AI? Using AI for internal operations 20.38% Using AI for marketing and sales 17.83% Offering AI-embedded solutions to customers 12.10% Running AI pilot programs 12.10% Exploring AI solutions but have not yet implemented them 16.56% Not currently using AI but interested in learning more 17.83% Not interested in AI 3.18% Source: Telarus 15 SUMMER 2025 | CHANNELVISION
stepped into the role of data readiness advisor, showed the Telarus findings. More than four in 10 (44 percent) said they rarely or never discuss data readiness with clients. An additional one quarter of respondents admitted data readiness comes up only occasionally during AI discussions. Telarus executives recommend advisors walk their customers and prospects through these “building blocks of data readiness.” • Data hygiene – Is your data clean, current and error-free? • Data accessibility – Can the right users and systems securely access it? • Data quality – Is it consistent, accurate and complete? • Context – Is it labeled and structured so AI models can interpret it? From here, a conversation can move to some key general topics surrounding existing infrastructure, skills gaps and eventual implementations goals, suggested Koby Phillips, vice president of cloud strategy at Telarus. For infrastructure and architecture, begin by mapping out the customer’s existing compute, storage and networking capabilities, as well as their cloud and edge deployments, to help assess whether current systems support AI readiness, said Phillips. Are AI workloads running on dedicated AI infrastructure? What types of storage solutions is the company using, and what connections are in place? The IT skills gap, meanwhile, is expected to impact as many as 90 percent of global organization by 2026, according to IDC, so for customers looking to implement AI, “it’s worth asking if they are comfortable with their current teams, or if they may need advanced guidance and manpower,” advised Phillips. Question here can center around how taxing AI implementations will be on IT teams and whether they will cause delays in other projects. With the emergence of agentic AI and the constant flow of new productivity tools, most companies will be looking to build upon AI pilots and initial deployments in order to take advantage of emerging capabilities. In turn, technology advisors would be wise to discuss near-term and longerterm AI investment priorities and how data readiness can help customers achieve those goals. Questions here can start with the more immediate concerns around how many streams of data are coming in, where they are landing and how they are connected, and turn to bigger picture discussions around the core business challenges hopefully being solved with AI and how the company sees AI being used a year to two down the road. Make no mistake, AI is no longer confined to the domains of pilots, innovation labs and exploratory meetings. It’s being rapidly deployed across most departments and is having a major influence on the spending in cybersecurity, cloud and CX, among other areas, the Telarus data showed. Solution distributors such as Telarus, vendors, publishers and research firms all are rushing to market with whitepapers, e-guides and training tools to help technology advisors build AI fluency. But until those materials can be absorbed and processed, it’s possible that channel partners will be compelled to start AI conversations with their customers and simply trust that their vendor and distributor partners will help close the deal and deliver the goods. It may not be an ideal scenario, but it’s much better than your customers having AI conversations with a competitor. o How often do you discuss data readiness with your customer when selling AI solutions? Never – It has not been a focus in my sales conversation 18% Rarely – Most customers don’t ask about it 26% Occasionally – It comes up in some AI discussions 26% Regularly – It’s a key part of every AI-related conversation 29% Source: Telarus Percentage of Attack Detections That Were Malware-free Source: ConnectWise Productivity Improved operational efficiency Improved customer experience Automation Innovation Modernization Cost reduction Improved employee experience Scalability Head count reduction If you are planning to adopt AI, what are the desired outcomes you are seeking? (Select all that apply) Source: Telarus Mid-market Enterprise 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 65%64% 60% 67% 52%52% 51% 78% 51% 28% 47% 30% 43% 64% 41% 25% 37% 25% 50% 16% 40% 2019 2020 2021 2022 2023 2024 51% 62% 71% 75% 79% 16 CHANNELVISION | JULY - AUGUST 2025
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As networking has become increasingly strategic to enterprise IT, the importance of an intelligent network management platform has grown. This requirement influences how channel players, including VARs, systems integrators and other types of resellers, select their managed service provider (MSP) partners. Enterprise IT leaders are seeking to reduce network complexity, optimize performance, improve efficiency and automate remedial actions, while relying on network infrastructure to run business-critical applications reliably and securely. Artificial Intelligence (AI)-powered end-to-end observability, delivered through a single pane of glass, is a cuttingedge innovation that is rapidly becoming a critical capability, driving more automated and intelligent features. These features function to detect dynamic changes in the network environment and respond in real-time to ensure uninterrupted network performance. Leveraging AI, machine learning (ML) and other advanced software technologies as a next-generation network management platform, this dynamic functionality improves network performance, security and efficiency while lowering mean-time-to-repair (MTTR) metrics. This drives improvements in business continuity and the user experience with an overall positive impact on enterprise competitiveness. Contextualized Observability Through a Single Pane of Glass Six key components comprise observability through a single pane of glass: data aggregation, visualization, data normalization, analytics and insights, and notification and alerts. These By Jamie Pugh The importance of contextualized network management powered by AI Beyond A Single Pane of Glass VIRTUAL REALITIES 18 CHANNELVISION | SUMMER 2025
components function in a dynamic yet uniform manner through a single graphical interface with a customizable dashboard view, automating workflows, integrating third-party applications and providing dynamic and intelligent monitoring across an enterprise’s network infrastructure with robust reporting capabilities. Data-driven observability takes this networking management and monitoring functionality to a new level. Contextualization utilizes AI to generate enriched and correlated (versus isolated) data that provides a richer, real-time view of the network environment, spanning components, access points and devices. With full-stack observability in place, organizations benefit from more meaningful data, actionable insights and reduced time to resolution through faster triaging and escalation. Additionally, AI technology enables enhanced automation of these functions, leveraging predictive analytics and event correlation techniques. The improved automated workflows enable more accurate and efficient data management, minimize alert fatigue and reduce the need for manual intervention. Much like a ballet where the harmony of performers depends on synchronized movement, a network operates smoothly when all components interact fluidly. A single disruption can cause cascading effects, alerting multiple systems and triggering redundant tickets. Contextualization helps stitch these signals together, presenting a holistic view that quickly identifies the root issue. Key Advantages Contextualized network management platforms delivered through a single pane of glass offer many advantages that include reduced ticket volumes and MTTR, as well as enhanced control and visibility that is provided through a centralized dashboard. First and foremost, it simplifies network management in an increasingly complex and distributed enterprise network environment. Secondly, it reduces network vulnerabilities to security breaches through enhanced threat detection and remediation capabilities. Additionally, it enables more efficient and optimized performance, helping reduce costs, conserve scarce IT resources and improve productivity. The consolidated view of applications also helps ensure enhanced control and reduced opportunity for error. This relates to the expanding array of use cases across cloud, security and third-party middle-mile monitoring. The improved automation capabilities also enable realtime response times and faster incident resolution. These improvements are further strengthened by more advanced event correlation techniques, which enable pre-emptive and proactive problem remediation. In addition, access to data, along with its integrity and meaningfulness, is also improved, driving more efficient and effective decision-making. Finally, holistic observability delivered through a single pane of glass helps ensure operational stability and continuous business, improving both the user and customer experience. Use Cases Data-driven observability through a single pane of glass has numerous applications across network management, cybersecurity, business intelligence, supply chain management and overall IT operations management. The operation Number of Network-related Tools in Use Source: Uptime Institute; 2022 Source: Auvik 10% 2% 26% 62% 5% 2% 44% 49% 4% 8% 20% 68% 4% 29% 67% 12% 36% 52% 12% 2% 35% 51% 6% 1% 31% 62% 7% 1% 44% 48% 10% 3% 26% 61% Corporate IT IT MSP 1-100 employees 101-500 employees 501-1000 employees 1000+ employees 0-4 years 5-9 years 10+ years Less than 10 CORPORATE VS SERVICE PROVIDER ORGANIZATION SIZE ORGANIZATION OPERATIONAL YEARS Between 10-20 More than 20+ Don’t know 19 SUMMER 2025 | CHANNELVISION
of the network can be more effectively optimized through real-time policy changes, ensuring better-performing cloud applications align with the latency requirements. Additionally, contextualization facilitates simplified network management and monitoring. For example, when a site experiences degraded performance, multiple alerts might be triggered across routers, firewalls and SD-WAN devices. Without contextualization, these generate separate tickets, which increases the time to resolution. Intelligent correlation reduces this noise by linking alerts to a single event. Comprehensive observability also enables the assessment of network health and efficiency, supported by detailed analytics and real-time reporting. Anomaly detection and identifying issues are critical functions supported by end-to-end observability to ensure network availability and uptime. Security-related incidents are a facet of this functionality at both the individual user and device levels, as is the case with secure access service edge (SASE) type solutions, which demand immediate remediation to protect an enterprise’s confidential data resources and brand reputation. Additionally, the integrated services view, offered through a single pane of glass with system-wide observability, drives streamlined network management across wireline and wireless applications. This extends to the more efficient management of customer and marketing data, which impacts the quality and use of an enterprise’s business and marketing intelligence, while enhanced data visibility resulting from telemetry-driven observability can also drive improved supply chain management. Not All Network Management Platforms are the Same When assessing the various network management and monitoring platforms offered by network solution providers in the marketplace today, it’s essential to consider the functional robustness of the single-pane-of-glass approach and the level of full-stack observability it provides. It starts with a consolidated dashboard view at an integrated services level across wireline and wireless device connections, which can be a differentiator compared to the various SaaS-enabled network management platform offerings. This also applies to the breadth of network services that can be viewed across a single interface. The adaptability of the provider’s API is also a critical capability, enabling the integration of local access providers as well as third-party network technologies and solutions. The breadth and flexibility of dashboard customization is another key consideration, especially when evaluating from a reseller perspective. The security functionality is a further differentiator, leveraging a platform’s comprehensive observability to address security incidents in real-time. Sitelevel visibility is another differentiator, ensuring better performance on an end-to-end basis. AI has also moved the needle with automation and analytics, leading to more outcome-based SLAs as a differentiator. Finally, the degree of contextualization engineered into the platform should be carefully assessed, as this will drive better network performance, narrow troubleshooting timeframes and enhance network reliability. This is an area of network management functionality where there is a significant variance of capability across the MSP spectrum. Building versus buying this network management and portal functionality is another key strategic aspect that MSPs, resellers and end-users must consider carefully. The versatility of SaaS-enabled platforms has significantly improved the efficiency of acquiring much of the network management functionality, rather than implementing a DIY approach. Beyond cost, the buildversus-buy decision also hinges on staffing. Internally managing a platform like this requires skilled personnel who understand implementation, tuning and ongoing optimization, often necessitating more than one dedicated full-time equivalent (FTE) technician. To address this resource requirement, MSPs can offer out-of-the-box functionality, including preconfigured contextualization, reducing internal overhead. The Security Imperative The value of a single pane of glass with end-to-end observability in helping to protect an enterprise’s network and IT infrastructure from malicious attacks and breaches cannot be overstated. SSE (secure service edge) and ZTNA (zero trust network access) technologies and solutions have served to fortify cloud security and network access since their initial rollout more than five years ago. Integrating these service capabilities into the dashboard view through a single pane of glass can significantly improve the effectiveness of security incident monitoring and remediation. The enhanced ability of data-driven observability to detect anomalies and remediate them through AI-driven automation is a more effective defense against security threats, especially as these malicious attacks become increasingly sophisticated. A single pane of glass with systemwide observability functions serves as a data aggregation and analytics tool, used to measure network performance, bandwidth utilization and application performance, which are often specified in service level agreements (SLAs). This includes latency performance, network uptime and availability and bandwidth usage. Additionally, a single pane of glass with system-wide observability has a significant impact on metrics relating to ticket management and problem resolution, including ticket volumes, escalations and MTTR. These metrics are critical to assuring the operational integrity of an enterprise network and that business applications run uninterrupted. In today’s managed network services market, a single pane of glass with holistic observability has become a critical factor when evaluating a provider’s capabilities. In many ways, it has become the “secret sauce” to providing an exceptional user and customer experience through its enhanced visibility, dynamic monitoring, improved automation, actionable insights and faster remediation. Jamie Pugh is CTO of Globalgig. 20 CHANNELVISION | SUMMER 2025
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