Fall 2025 Sponsored by POTS Priority PARTNER HARMONY Managed AI Volume 25 Issue 5 | channelvisionmag.com QUANTUM SECURITY Guiding Customers Across the Gen AI Divide Scan to Register
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LetsTalkSolutions@entelegent.com | www.entelegent.com | #getentelegent Follow Us “M&A activity surged 27% in the first half of 2025, reaching $2.2 trillion — the highest in two decades.” — J.P. Morgan, July 2025 (as reported by Business Insider) Managed Services | SASE & Security | Network Services | VoIP/Voice/POTS Replacement | Connectivity | EnVision Life Cycle Management (TEM/WEM) | Mobility | White Labeling Regardless of industry, Mergers, Acquisitions, and Spinoffs, can overwhelm IT and Accounting, leaving users frustrated, and the future uncertain. EnTelegent provides clarity with AI-powered inventory, service consolidation, proactive monitoring, and expert support for unfamiliar technologies—ensuring seamless continuity, reduced costs, and a clear technology roadmap for the future. ENTELEGENT SIMPLIFIES THE M&A EXPERIENCE Transform M&A Technology Turn Complexity Into Clarity Read More CASE STUDY EnTelegent delivered business continuity and cost savings for a diagnostic imaging spin-off—proving M&A doesn’t have to mean disruption. One Partner, One Portal, One Invoice... Simplify Your Next Acquisition #GetEnTelegent Rapid AI-driven inventory of all tech assets Single contract & invoice to lighten back-office loads Centralized EnTeleSource Portal for contracts, commitments, services, and circuits Managed Network Services Monitoring from simple ping and alert up to full AIOps surveillance and resolution, backed by a US-based NOC. Expert IT support for acquired technologies
FALL 2025 AI & AUTOMATION 8 AI & Telecom 8 Ethical AI 10 Human Assist Still Essential to AI CX By Martin Vilaboy 12 The Four Pillars of Managed AI How MSPs move AI from hype to business value By Michael Gray 16 Across the Gen AI Divide Tips for guiding customers on their AI journey By Martin Vilaboy 24 Raising the Bar for UCaaS Intermedia delivers AI-powered communications for the next era of business collaboration By Gerald Baldino CYBER PATROL 26 Quantum Leap A tech advisor’s guide to quantum computing and cybersecurity By Gerald Baldino CORE COMMUNICATIONS 32 Voice & Messaging from a Carrier You Can Trust Commio works closely with MSPs to keep customers connected at competitive rates By Scott Navratil 34 Chasing the Copper Sunset Advisors need to prioritize POTS migration – and Telarus can help 38 Driving Innovation in Telecom Speedflow powers the future of VoIP and messaging with MediaCore and NexaMSG By Gerald Baldino CHANNEL MANAGEMENT 40 Beyond Transactions Building ‘partnership harmony’ in the AI age By Dennis Frank 42 Engagement Meets Execution How gen AI & agentic AI create a powerful duo to drive channel marketing By Daniel Nissan 45 Winning Together When MSPs, vendors and resellers align, everyone benefits By Gerald Baldino 48 Keep It Moving How competition fuels channel momentum By Glen Nelson 50 Charting the Next Chapter Granite’s 2026 Game Changers By Charlie Pagliazzo MOBILE & WIRELESS 52 Last Mile Level-Up Natural Wireless transforms last mile connectivity with Fixed Wireless Fiber By Gerald Baldino EDGE TO CLOUD 54 Xalyte Technologies: Pioneering the Next Era of Digital Efficiency By Gerald Baldino 6 Editor’s Letter 56 ICYMI 58 Ad index CONTENTS Volume 25 – Issue 5 4 CHANNELVISION | FALL 2025
Despite instability wrought by pandemics, major global conflicts and trade wars, tariff threats and the explosive growth of generative AI, top line IT spending has been surprisingly stable. Spending and staffing metrics such as IT spending as a percentage of revenue, IT budget increases, the percentage of CIOs reporting adequate budgets, the number of CIOs being asked to cut budgets, and IT staff hiring all look roughly the same as they have for much of the last decade, according to figures from Avasant Research. The firm’s 2025/26 benchmark study found that IT operational budgets are increasing by 3 percent, marking the second consecutive year and the fourth year in the last six that IT budgets have increased by 3 percent. During the last decade, IT spending has increased at a median of between 2.8 percent and 3.1 percent every year, except for the high inflation years of 2022 and 2023. “For about a decade, companies have increased their IT spending by about 3 percent every year, regardless of the state of the economy or the political atmosphere,” said Avasant analysts. “And for the most part, CIOs have reported in those same periods that they are ‘OK’ with that budget and that it was roughly adequate to suit their needs.” Even so, while there has been little movement in these key indicators, there are some “tectonic shifts” taking place below the surface, Avasant numbers showed. Ten years ago, for example, personnel made up about 41 percent of IT budgets, on average. Today, that number is about 30 percent. Security, meanwhile, made up 1.5 percent of the total IT budgets ten years ago – the same amount as printers. Today, security is 5.4 percent of budgets. AI made up such a small percentage of budgets in 2016 that Avasant didn’t even track it, but AI already accounts for more of IT budgets than security did a decade ago. After subtracting the number of companies that are reducing their budgets in a given area from the number of companies increasing their budget, Avasant found that a net of around 64 percent of companies is increasing their IT spend on AI. Further illustrating the importance of the transformation that is taking place, budgets for upgrading legacy systems are increasing by a net of 57 percent this year compared to only 15 percent last year. “CIOs have spent the last decade re-imagining the IT department, and that transformation continues at a remarkable pace,” said David Wagner, senior research director at Avasant. “As AI and automation continue to transform the business, CIOs need to be ready to move even faster.” This is all good news for technology advisors, whose primary value proposition has become helping CIOs and IT departments navigate the ongoing transformations of how we work and network, as well as keep up with the pace of change. These realities also are the precise reasons why we created CVxEXPO, which takes place this year on November 3-5, in Glendale, Ariz. From the expo hall showcasing the most cutting-edge technologies in the channel to the unrivaled educational content to the one-of-a-kind entertainment-based networking events, partners and providers come to CVxEXPO to see first-hand the solutions that are driving change across the channel, learn from like-minded peers and build their roadmaps for the coming year. But most importantly, they come to find ways to more quickly bring their customers to business outcomes, whether through networks, network services, business communications, cybersecurity or AI. If you can’t join us at CVxEXPO25, don’t worry. We’ll be back next November with the same intent of keeping our channel community and their customers moving forward in these transformational times. We hope to see you there. IT Budget Trends Disguise Transformation 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 | FALL 2025
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The usage of AI in the telecommunication sector is growing rapidly, according to a new report from Fortune Business Insights, driven by the demands for advanced network management, personalized customer experiences and greater operational efficiency. The global AI in telecommunication market size was valued at $3.34 billion in 2024, and is projected to grow from $4.73 billion in 2025 to $58.74 billion by 2032, showing a compound annual growth rate of 43.3 percent during the forecast period. “A key driver of this trend is the adoption of streamlined AI application development methodologies, enabling telecom firms to swiftly deploy tailored AI solutions internally,” stated the report. This approach involves leveraging pre-built AI models and frameworks, significantly reducing the time and resources required for AI application development.” Big data is projected to dominate the market share due to its ability to provide valuable insights into customer behavior, network performance and operational efficiency, leading to improved decisionmaking and customer satisfaction. What’s more, the telecom industry generates vast amounts of data daily, and harnessing this data through big data analytics extends new revenue streams and business opportunities, said Fortune Business analysts. Machine learning technology, meanwhile, holds the highest growth rate due to its versatility and ability to continuously learn from data, allowing it to adapt to diverse applications and industries. While organizations typically recognize the criticality of responsible AI, many don’t know where or how to get started. Enterprise Strategy Group surveyed 374 professionals involved in the decision-making, selection and management of AI initiatives and projects regarding their responsible AI strategies. Respondents cited multiple tools and tactics to ensure ethical AI practices and operational integrity. “Taking a multi-pronged approach will prepare organizations for success,” said ESG analysts. “Investing in technology, people and partnerships is key for growth and adaptability as technology innovation, associated talent and collaboration can help organizations enable the ethical and responsible use of AI.” Crexendo has added AI agent platform provider OneReach.ai to its Ecosystem Vendor Partner (EVP) program. The OneReach.ai Generative Studio X (GSX) platform comprises “a complete agent runtime environment that enables users to design, train, test, deploy, monitor, optimize and orchestrate AI agents at scale,” officials said. Agents can be trained and customized to enhance CX, boost employee productivity and streamline operations. “With seamless integration between OneReach.ai GSX and Crexendo platforms, this partnership empowers Crexendo customers to utilize intelligent, orchestrated AI agents for creating increased revenue opportunities and delivering enhanced customer experiences—all while maintaining the highest industry standards for enterprise data security and privacy,” said Robb Wilson, CEO at OneReach.ai. AI in Telecom Takes Off The Many Tools of Ethical AI Crexendo Partners With OneReach.ai AI & AUTOMATION Global AI in Telecommunication Market Share, By Technology, 2024 Source: Fortuneinsightbusiness.com Technology Adoption Life Cycle Source: B2U Agents enabled by generative AI could function as hyperefficient virtual coworkers Illustration of how an agent system might execute a workflow, from prompt to output Using natural language, the user prompts the generative AI agent system to complet a task. The agent system interprets the prompt and builds a work plan. A manager agent subdivides the project into tasks assigned to specialist agents; they gather and analyze data from multiple sources and collaborate The agent team shares the draft output with the user. The agent team receives user feedback, then iterates and refines output accordingly. External systems: Agents interact with databases and systems– both organizational and external data–to complete the task. Manager agent Specialist agents Start End Analyst agent Checker agent Planner agent 1 2 3 4 Machine Learning EARLY MARKET INNOVATORS 2.5% TECH ENTHUSIASTS EARLY ADOPTERS 13.5% EARLY MAJORITY 34% LATE MAJORITY 34% LAGGARDS 16% THE CHASM MAINSTREAM MARKET Natural Language Processing Big Data Others VISIONARIES PRAGMATISTS CONSERVATIVES SKEPTICS 26.1% CAGR The enterprise LLM market is experiencing accelerated growth, according to Global Market Insights, driven by an increasing demand for AI automation across corporate applications and data analysis. Valued at $6.7 billion in 2024, the market is expected to grow to $71.1 billion by 2034. Investments to Ensure Responsible Use of AI Modern technology platforms, solutions, services 51% Employee training 45% Technology partnerships and alliances 44% Hiring AI ethics experts/specialists 42% Internal risk assessments and/or audits 41% Regular policy reviews and updates 40% External risk assessments and/or audits 38% Diversity and inclusion initiatives 34% Employee accountability programs 32% Community/customer stakeholder engagement 29% Consulting partnerships 29% Cross-functional ethics committees 27% 8 CHANNELVISION | FALL 2025
Human Assist Still Essential to AI CX Even though the contact center is one of the places where AI has been successfully implemented early and often, “customer care is still very much a human-centered endeavor in the minds of consumers,” argue researchers at CX content company Execs In The Know. According to its September survey of U.S.-based adults, the primary concern for consumers when using an AI-powered or self-help solution to resolve a customer care issue is easy access to a live person. Even as AI capabilities evolve, the percentage who cite human backup as tops in importance has remained unchanged during the last three years. In fact, as companies increase their reliance on AI-powered solutions, the top three concerns among consumers (difficulty reaching a live person, AI solutions unable to address specific issues and getting trapped in an endless loop) all revolve around a pattern of frustration when an AI-powered solution is in some way broken and there is no one there to help. The percentage citing difficulty in reaching a live person actually increased from 2023 to this year. “This has been such a common experience that changing perceptions will not only take better AI-powered solutions but will also take time and lots of positive experiences to counter negative sentiment,” said Execs In The Know researchers. The data suggests that the growing use of AI in CX applications has not exactly made things easier for users. Respondents who listed “ease of use/ simplicity” as of primary importance when interacting with an AI powered CX solution increased by 6 percentage points between 2023 and 2025. “Although AI technology has come a long way, gaps in quality among customer-facing solutions persist, meaning that all too often customers need an easy off-ramp to a live agent,” the report continued. Overall, consumers tend to be net-positive about the impact of AI on CX, with 40 percent of consumers saying they either “strongly agree” or “agree” that AI-powered tools and solutions will greatly benefit their customer care interactions in the future. On the flip side, just less than a quarter of respondents said they “strongly disagree” or “disagree.” “This is good news given how quickly the technology is evolving and growing in capability and accuracy,” the researchers concluded. o AI & AUTOMATION What’s your biggest concern with companies increasingly relying on AI solutions for customer care? 2023 2024 2025 Difficulty reaching a live person 29% 27% 33% AI solution unable to address specific issues 24% 25% 22% Getting trapped in an endless loop 20% 25% 21% Information not safe or secure 12% 9% 12% Feeling of being disconnected from the brand 9% 8% 8% Getting a solution will become more difficult 6% 6% 4% Source: Execs In The Know; Transcom When resolving a customer care issue using a solution powered by artificial intelligence (AI), which is most important to you? 2023 2024 2025 Easy access to a live person 33% 32% 32% Accurate, consistent information 28% 25% 25% Ease of use/simplicity 15% 20% 21% Security of information 14% 13% 12% Lifelike communications 8% 9% 9% Multilingual support 0% 1% 1% Source: Execs In The Know; Transcom By Martin Vilaboy 10 CHANNELVISION | FALL 2025
AI & AUTOMATION How MSPs move AI from hype to business value By Michael Gray The Four Pillars of Managed AI To date, AI projects have been a big, expensive experiment. Innovators and early adopters jumped on the hype and have shelled out billions collectively to test and stretch AI to see what’s possible. All things considered, success has been limited. Nearly 90 percent of AI projects never make it into production, according to Garnet estimates, and 92 percent of enterprises reported at least one failed proof-of-concept in 2024. Despite how bleak that sounds, these experiments haven’t been entirely fruitless. In fact, they’ve given us incredible insights into this technology in record time. But we’re moving further into the adoption lifecycle, where midmarket pragmatists and the greater mainstream market are starting to pick up the mandate. This group is notoriously more hesitant than the early adopters that came before them. We’re already seeing it play out with midmarket companies that are now far less whimsical in their AI approach: we’re seeing less emphasis on experimentation at any cost, a bit more return consciousness and decision-makers more influenced by data than rhetoric. These early experiments made it clear that the problem isn’t the technology; it’s the lack of a plan. And that hasn’t completely been a bad thing. For a lot of these experiments, failure was the point. Fast failure guides 12 CHANNELVISION | FALL 2025
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faster iterations and, ultimately, highervalue outputs. But the mainstream market wants results the first time, and that’s something MSPs can help them get in a way they wouldn’t be able to guarantee on their own. As you advise your clients on AI, seeing provable business results depends on careful consideration across four core pillars. Data and Use Cases If there’s one lesson every AI initiative has reinforced so far, it’s the need for clarity. Artificial intelligence tools are educated curators and creative regurgitators, meaning their usefulness is limited by the data that feeds them. MSPs have been beating that drum for what feels like years: high-quality, wellstructured data is non-negotiable. But clarity doesn’t end with the inputs. Equally critical is defining the output. What’s the use case? What business outcome do they want AI to help achieve? Too often, clients start with “we need AI” rather than “we need AI to do this.” MSPs can help bridge that gap, guiding clients to articulate both where they’re starting (the data) and where they’re headed (the outcome). That clear line of sight from inputs to results is what separates experiments from projects built for success. Strategy Midmarket organizations considering any project must think strategically and act tactically, even when starting with pilots. They don’t have the budget to do otherwise. Once the inputs and outcomes are defined, the real work begins: defining the path between them. Every AI rollout involves dozens of choices – from selecting the right models and tools to establishing governance guardrails to planning for scale. Each decision shapes the trajectory and increases the risk of veering off course, and midmarket organizations rarely have the time or budget to reinvent the wheel. MSPs give them a scalability advantage: enterpriselevel lessons without enterprise-level trial-and-error. With managed AI services and a portfolio of projects to draw from, MSPs bring the benefit of hindsight: spotting patterns, avoiding common pitfalls and tailoring proven approaches to fit each client’s goals. That perspective helps clients move with confidence, knowing their investment is backed by a structured plan for return. Execution Even the best strategy falls flat without effective execution. An investment in AI only delivers value if teams adopt it – and not just adopt it but use it well. That means more than standing up a model: it takes training, clear implementation procedures, seamless integration into existing tools and workflows, and incremental rollouts that build confidence without disrupting business. Above all, execution must leave employees with a faster, more effective way of working than what came before, or the project will be effectively dead before it’s off the ground. For many organizations, this is where the real hurdles appear. Securing adoption, avoiding “rip and replace” panic, and ensuring early wins demand a level of expertise these midmarket organizations don’t yet have. MSPs can step in here as a guide and safety net, offering proven playbooks, surfacing challenges companies might not anticipate and making sure execution stays aligned to business goals. This is also where MSP can create continuity – keeping the rollout on track day-today and ensuring adoption doesn’t stall after the initial push. Measurement & Recalibration No AI initiative is ever “set it and forget it.” Models drift, business goals evolve and inputs lose clarity over time. Even the best rollout will require ongoing adjustment. The key is to treat AI as a living system that needs to be measured, reviewed and refined over time – not as a one-time deployment. Built-in reporting and regular reviews ensure AI delivers real ROI and stays aligned with business objectives. But the challenge for most organizations is knowing which numbers matter. Chasing vanity metrics can give the illusion of progress while masking real issues. MSPs can bring discipline to this process. With both technical insight and business context, they help clients measure what matters, identify when recalibration is needed and course-correct before small problems become costly setbacks. In doing so, they turn AI from a fragile experiment into a sustainable, continually improving capability. Early adopters may have been willing to experiment, but midmarket organizations can’t afford that luxury. By guiding clients through data, strategy, execution and recalibration, MSPs can turn AI from a costly experiment into a practical engine for measurable business value. o Michael Gray is CTO of Thrive Technology Adoption Life Cycle Source: B2U Agents enabled by generative AI could function as hyperefficient virtual coworkers Source: McKinsey & Co. Illustration of how an agent system might execute a workflow, from prompt to output Using natural language, the user prompts the generative AI agent system to complet a task. The agent system interprets the prompt and builds a work plan. A manager agent subdivides the project into tasks assigned to specialist agents; they gather and analyze data from multiple sources and collaborate with one another to execute their individual missions. The agent team shares the draft output with the user. The agent team receives user feedback, then iterates and refines output accordingly. External systems: Agents interact with databases and systems– both organizational and external data–to complete the task. Manager agent Specialist agents Start End Analyst agent Checker agent Planner agent 1 2 3 4 The steep drop from pilots to production for task-specific GenAI tools reveals the GenAI divide Source: MIT Project NANDA Why GenAI pilots fall: top barriers to scaling AI in the enterprise Users were asked to rate each issue on a scale of 1-10 Challenging change management Lack of executive sponsorship EARLY MARKET INNOVATORS 2.5% TECH ENTHUSIASTS EARLY ADOPTERS 13.5% EARLY MAJORITY 34% LATE MAJORITY 34% LAGGARDS 16% THE CHASM MAINSTREAM MARKET VISIONARIES PRAGMATISTS CONSERVATIVES SKEPTICS General-Purpose LLMs 80% 60% 50% 20% 40% 5% Investigated Piloted Successfully Implemented Embedded or Task-Specific GenAI 14 CHANNELVISION | FALL 2025
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By Martin Vilaboy Across the Gen AI Divide Tips for guiding customers on their AI journey A first step to being a credible and trusted advisor to customers seeking guidance on future AI investments is not telling them to distrust what they’ve seen with their own eyes. While it may be true that artificial intelligence and AI agents are set to revolutionize knowledge work the way industrialization revolutionized physical work in past centuries, it’s quite likely that your customers’ early experimentations with AI have not foreshadowed that type of transformation. According to research out of the Massachusetts Institute of Technology’s Project NANDA (Networked Agents and Decentralized Architecture), the vast majority of organizations that have piloted gen AI have seen no measurable impact on P&L statements. Only a small fraction of organizations has moved beyond experimentation to achieve meaningful business transformation. Despite all the hype and the $30 to $40 billion in enterprise investment AI & AUTOMATION 16 CHANNELVISION | FALL 2025
into gen AI so far, only 5 percent of initiatives delivered measurable business returns at six months postpilot, exposing what MIT researchers dubbed a “widening Gen AI Divide.” McKinsey & Co. researchers, for their part, refer similarly to a “gen AI paradox,” whereas nearly eight in 10 companies have deployed gen AI in some form, but roughly the same percentage report no material impact on earnings. What’s more, only 1 percent of enterprises recently surveyed by McKinsey view their gen AI strategies as mature. “For all the energy, investment and potential surrounding the technology, at-scale impact has yet to materialize for most organizations,” said McKinsey. This divide, proclaimed MIT researchers, does not seem to be driven by business model, regulatory implications, industry vertical or company size. Rather, “the divide stems from implementation approach,” they continued. In other words, partners can use the lessons learned from the 300 public implementations studied by MIT to guide customers across or out of the divide as they experiment with gen AI or transition into the next wave of AI technologies. A Wide Divide Certainly, gen AI tools such as ChatGPT and Copilot have been widely adopted, with more than 80 percent of organizations surveyed having explored or piloted them, and nearly 40 percent reporting deployment. But these tools primarily enhance individual productivity, showed the MIT study, not P&L performance. “Despite high-profile investment, industry-level transformation remains limited,” said MIT researchers. “GenAI has been embedded in support, content creation and analytics use cases, but few industries show the deep structural shifts associated with past general-purpose technologies such as new market leaders, disrupted business models or measurable changes in customer behavior.” All the while, the more customized, enterprise-grade systems “are being quietly rejected,” said MIT researcher. While 60 percent of organizations evaluated such tools, only 20 percent reached pilot stage, and just 5 percent reached production. Most fail due to “brittle workflows, lack of contextual learning and misalignment with day-today operations.” McKinsey shared similar findings. It cited an imbalance at the heart of its paradox between widely deployed “horizontal” copilots and chatbots, which have scaled quickly but deliver diffuse, hard-to-measure gains, and more transformative “vertical” or “function-specific” use cases, of which about 90 percent remain stuck in pilot mode. According to the MIT data, resource intensity had little bearing on success. Large enterprises, defined as firms with more than $100 million in annual revenue, lead in pilot count and allocated more staff to AI-related initiatives. Yet these organizations report the lowest rates of pilot-to-scale conversion. Interestingly enough, smaller and mid-market firms, which tend to move faster and more decisively, tended to have more success with gen AI. These top performers reported average timelines of 90 days from pilot to full implementation. Large enterprises, by comparison, took nine months or longer, showed MIT’s findings. Similar findings emerged among most vertical studied, as well. Of the nine industry segments studied, only tech and media (which often prioritize marketing, content and developer productivity) showed clear signs of structural change. 18 CHANNELVISION | FALL 2025 Source: McKinsey & Co. with one another to execute their individual missions. The steep drop from pilots to production for task-specific GenAI tools reveals the GenAI divide Source: MIT Project NANDA Why GenAI pilots fall: top barriers to scaling AI in the enterprise Users were asked to rate each issue on a scale of 1-10 0 1 2 3 4 5 6 7 8 9 10 Source: MIT Project NANDA Challenging change management Lack of executive sponsorship Poor user experience Model output quality concerns Unwillingness to adopt new tools How executives select GenAI vendors Derived from interviews and coded by category “Would you assign this task to AI or a junior colleague?” 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Source: MIT Project NANDA Flexibility when things change The ability to improve over time Clear data boundaries Minimal disruption to current toots Deep understanding of our workflow A vendor we trust General-Purpose LLMs 80% 60% 50% 20% 40% 5% Investigated Piloted Successfully Implemented Embedded or Task-Specific GenAI Perceived Fitness for High-Stakes Work Source: B2U Agents enabled by generative AI could function as hyperefficient virtual coworkers Source: McKinsey & Co. Illustration of how an agent system might execute a workflow, from prompt to output Using natural language, the user prompts the generative AI agent system to complet a task. The agent system interprets the prompt and builds a work plan. A manager agent subdivides the project into tasks assigned to specialist agents; they gather and analyze data from multiple sources and collaborate with one another to execute their individual missions. The agent team shares the draft output with the user. The agent team receives user feedback, then iterates and refines output accordingly. External systems: Agents interact with databases and systems– both organizational and external data–to complete the task. Manager agent Specialist agents Start End Analyst agent Checker agent Planner agent 1 2 3 4 The steep drop from pilots to production for task-specific GenAI tools reveals the GenAI divide Source: MIT Project NANDA Why GenAI pilots fall: top barriers to scaling AI in the enterprise Users were asked to rate each issue on a scale of 1-10 0 1 2 3 4 5 6 7 8 9 10 Source: MIT Project NANDA Challenging change management Lack of executive sponsorship Poor user experience Model output quality concerns Unwillingness to adopt new tools How executives select GenAI vendors Derived from interviews and coded by category INNOVATORS 2.5% EARLY ADOPTERS 13.5% EARLY MAJORITY 34% LATE MAJORITY 34% LAGGARDS 16% General-Purpose LLMs 80% 60% 50% 20% 40% 5% Investigated Piloted Successfully Implemented Embedded or Task-Specific GenAI
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Why a Divide If you ask employees directly why their companies get stuck on the wrong side of the divide, you’ll hear some familiar complaints that come with emerging technologies, such as lack of executive buy-in and resistance to change. MIT analysis of implementations, however, suggested organizations aren’t investing in the right places. In terms of functional focus, investment in gen AI tools is heavily concentrated, said MIT researchers, with sales and marketing functions capturing approximately 50 to 70 percent of AI budget allocation across organizations surveyed. In general, front-office tools such as those for sales and marketing get the attention because their outcomes are highly visible, impacts are measured easily and the gains are board-friendly. Metrics such as demo volume or email response time, for instances, align directly with board-level KPIs, said MIT. While bias reflects easier metric attribution, “some of the most dramatic cost savings we documented came from back-office automation,” said MIT researchers. “While front-office gains are visible and board-friendly, the back-office deployments often delivered faster payback periods and clearer cost reductions.” Organizations that focused AI investment on back-office functions such as legal, procurement, operations and finance experienced more substantial, although more subtle, efficiencies such as fewer compliance violations, streamlined workflows or accelerated month-end processes. Real cost savings came when organizations were able to replace BPOs and external agencies with AI-powered internal capabilities – and not from cutting internal staff. “This investment bias perpetuates the GenAI Divide by directing resources toward visible but often less transformative use cases, while the highest-ROI opportunities in backoffice functions remain underfunded.” A similar scenario emerges surrounding investment in general versus specific tools. Consumer-grade tools such as ChatGPT and Copilot are widely used and widely praised among respondents for their flexibility, familiarity and immediate utility. At the same time, generic tools such as LLM chatbots appear to show high pilot-to-implementation rates of more than 83 percent. But again, these tools were used to make changes that were more visible than transformative, said MIT researchers, largely applied to quick tasks while leaving complex projects requiring customization or sustained attention to humans. Yet users of consumer-grade and generic gen AI tools were “overwhelmingly skeptical of custom or vendor-pitched AI tools,” said the MIT study, describing them as brittle, overengineered or misaligned with actual workflows. “Users prefer ChatGPT for simple tasks but abandon it for mission-critical work due to its lack of memory,” said the study. Herein lies what MIT researchers called the “learning gap” that is the primary factor keeping organizations on the wrong side of the gen AI divide: static tools that don’t learn and can’t evolve, integrate poorly into workflows and fail to deliver context. “The core barrier to scaling is not infrastructure, regulation or talent. It is learning,” stated MIT researchers. “Most GenAI systems do not retain feedback, adapt to context or improve over time.” A corporate lawyer at a mid-sized firm exemplified this dynamic. Her organization invested $50,000 in a specialized contract analysis tool, yet she consistently defaulted to a $20-per-month general-purpose tool for drafting work. “Our purchased AI tool provided rigid summaries with limited customization options. With ChatGPT, I can guide the conversation and iterate until I get exactly what I need” this lawyer told MIT researchers. “The fundamen20 CHANNELVISION | FALL 2025 0 1 2 3 4 5 6 7 8 9 10 Source: MIT Project NANDA How executives select GenAI vendors Derived from interviews and coded by category “Would you assign this task to AI or a junior colleague?” 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Source: MIT Project NANDA Flexibility when things change The ability to improve over time Clear data boundaries Minimal disruption to current toots Deep understanding of our workflow A vendor we trust Perceived Fitness for High-Stakes Work Source: MIT Project NANDA AI Preferred Human Preferred Complex projects (multi-week work, client management) Quick tasks (emails, summaries, basic analysis) In your organization’s view, how soon will quantum computers achieve the capability to break current encryption methods? Source: Cap Gemini survey of early adopters Within 1-2 years (i.e., the threat is imminent) Within 2-5 years (i.e., threat is not imminent but in the medium term) Within 5-10 years (i.e., the threat is in the longer term) Uncertain More than 10 years (i.e., quantum computing breakthroughs are not on the horizon) 16% 24% 44% 14% 3% 90% 10% 70% 30% “This investment bias perpetuates the GenAI Divide by directing resources toward visible but often less transformative use cases, while the highest-ROI opportunities in back-office functions remain underfunded.”
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tal quality difference is noticeable, ChatGPT consistently produces better outputs, even though our vendor claims to use the same underlying technology.” Yet the same lawyer who favored ChatGPT for initial drafts drew a clear line at sensitive contracts: “It’s excellent for brainstorming and first drafts, but it doesn’t retain knowledge of client preferences or learn from previous edits. It repeats the same mistakes and requires extensive context input for each session. For high-stakes work, I need a system that accumulates knowledge and improves over time.” In other words, users appreciate the flexibility and responsiveness of consumer LLM interfaces but require the persistence and contextual awareness that current tools cannot provide, said MIT. “Our data reveals a clear pattern: the organizations and vendors succeeding are those aggressively solving for learning, memory and workflow adaptation, while those failing are either building generic tools or trying to develop capabilities internally,” argued MIT. Agentic AI systems, which specifically maintain persistent memory, learn from interactions and can autonomously orchestrate complex workflows, directly address the learning gap that defines this gen AI divide. This can be seen in customer service agents that handle complete inquiries end-to-end, financial processing agents that monitor and approve routine transactions, and sales pipeline agents that track engagement across channels demonstrate, said MIT. In the meantime, an analysis of buyers and organizations that successfully crossed the gen AI divide provides advisors with information to guide customers as they enter the realm of AI or shift to the next wave of AI technologies. Getting Across For starters, organizations that successfully crossed the divide approached AI procurement differently. Top buyers acted less like SaaS customers and more like clients for business process outsourcing (BPO), holding vendors to benchmarks as they would a consulting firm or BPO. These organizations, said MIT researchers, demanded deep customization aligned to internal processes and data; benchmarked tools on operational outcomes, not model benchmarks; and partnered through early-stage failures, treating deployment as a co-evolution. “The most successful buyers understand that crossing the divide requires partnership, not just purchase,” said the report. Likewise, strategic partnerships achieved a significantly higher share of successful deployments than internal development efforts. Although researchers observed far more “build” initiatives than “buy” initiatives in their sample, success rates favored external partnerships. Pilots built via strategic partnerships were twice as likely to reach full deployment as those built internally, while employee usage rates were nearly double for externally built tools. “[P]artnerships often provided faster time-to-value, lower total cost and better alignment with operational workflows,” argued the report. “Companies avoided the overhead of building from scratch, while still achieving tailored solutions. Organizations that understand this pattern position themselves to cross the GenAI Divide more effectively.” Successful organizations also tended to decentralize the sourcing of AI initiatives, relying on a type of bottom-up sourcing versus a central lab. Rather than relying on a centralized AI function to identify use cases, winners allowed individual contributors, budget holders and team managers to surface problems, vet tools and lead rollouts. “Many of the strongest enterprise deployments began with power users, employees who had already experimented with tools like ChatGPT or Claude for personal productivity,” said the report. “These ‘prosumers’ intuitively understood GenAI’s capabilities and limits and became early champions of internally sanctioned solutions.” The most effective AI-buying businesses also did not wait for perfect use cases or central approval. “Instead, they drive adoption through distributed experimentation, vendor partnerships and clear accountability,” continued the report. “These buyers are not just more eager; they are more strategically adaptive.” Concerns among your customers of ending up with “more pilots than Lufthansa” are certainly justified, as are feelings of “seeing AI everywhere but in our P&L statement.” But customers can take heed; organizations that successfully cross the gen AI divide do three things differently, MIT researched advised. They buy rather than build, empower individuals and line managers rather than central labs, and they select tools that integrate deeply while adapting over time. “For organizations currently trapped on the wrong side,” they concluded, “the path forward is clear: stop investing in static tools that require constant prompting, start partnering with vendors who offer custom systems and focus on workflow integration over flashy demos.” o 22 CHANNELVISION | FALL 2025 “Would you assign this task to AI or a junior colleague?” Source: MIT Project NANDA Perceived Fitness for High-Stakes Work Source: MIT Project NANDA AI Preferred Human Preferred Complex projects (multi-week work, client management) Quick tasks (emails, summaries, basic analysis) In your organization’s view, how soon will quantum computers achieve the capability to break current encryption methods? Source: Cap Gemini survey of early adopters Within 1-2 years (i.e., the threat is imminent) Within 2-5 years (i.e., threat is not imminent but in the medium term) Within 5-10 years (i.e., the threat is in the longer term) Uncertain More than 10 years (i.e., quantum computing breakthroughs are not on the horizon) 16% 24% 44% 14% 3% 90% 10% 70% 30%
24 CHANNELVISION | FALL 2025 Demand is strong for both AI and unified communications, especially in the mid-market where companies are heavily focused on driving growth and boosting productivity. According to the latest Telarus Tech Trends report, UCaaS is the top-selling category among technology advisors, while AI remains the leading driver of IT investment. Intermedia, a leading provider of intelligent business communications solutions, helps partners solve two challenges for customers at once, by upgrading communications while introducing safe, practical AI into customers’ daily workflows. “Many organizations, especially smaller ones, are struggling to implement AI and drive meaningful ROI,” explained Brian Gregory, Intermedia senior director of product marketing. “Intermedia helps businesses unlock their full potential, with practical AI-powered features that simplify workflows and elevate customer interactions.” Intermedia delivers everything businesses expect in a UCaaS platform and provides the tools that partners need to be successful. This includes vertical integrations, embedded Teams functionality, omnichannel contact center, archiving and more. But its true differentiator is its embedded AI enhancements which streamline workflows and reduce administrative tasks. • AI Meeting Recap uses AI to deliver powerful post-meeting summaries, action items and transcripts. It automatically transcribes meetings and delivers clear, AI-generated summaries, action items and key topics. And since the tool is easy to use, it eliminates the need for workers to use third-party tools – reducing shadow IT and boosting security. • AI Call Insights delivers AI-generated call summaries, key topics and discussion points, call sentiment and topics discussed, as well as a full transcription. AI Call Insights helps sales, support and service teams act on real conversations and enables managers to monitor call quality and performance at scale. • AI Receptionist (coming soon) uses natural language processing to greet visitors, answer questions, schedule appointments, route calls and manage messages. AI receptionist integrates with business systems such as calendars, knowledge bases and CRM systems to enhance productivity and improve customer experience by automating routine tasks while allowing human staff to focus on higher-value work. • AI Topics & Trends (coming soon) gives managers deep visibility into customer calls, conversational themes and sentiment trends across different teams and time periods. Intermedia also ensures security and compliance, which is a growing concern among IT leaders – and increasingly, a dealbreaker when adopting AI solutions. According to Gartner, AI regulatory violations will result in a 30 percent increase in legal disputes by 2028. And yet, only 23 percent of IT leaders are very confident in their organization’s ability to manage security and governance components when rolling out gen AI tools. Seize the UCaaS Opportunity With pandemic-era contracts coming up for renewal, now is the ideal time for partners to deliver modern, communications solutions that are equipped with the latest AI-driven features. Intermedia makes it simple, with a flexible partner program that supports service providers, resellers, VARs, MSPs and advisors – along with expert support, extensive sales and marketing tools and growth-aligned incentives. o Intermedia is a Bronze sponsor of CVxEXPO25 in Glendale. Meet the team in booth 5010, and catch senior carrier manager Mitch Adams in the AI Demo Café on Tuesday, Nov. 4. Raising the Bar for UCaaS By Gerald Baldino Brian Gregory Intermedia senior director of product marketing Intermedia delivers AI-powered communications for the next era of business collaboration CORE COMMUNICATIONS
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In just a few short years, quantum computing has evolved from a distant and nebulous concept into a rapidly advancing field that’s reshaping cybersecurity and data protection. Quantum computing may sound intimidating, but fortunately you don’t need a PhD in physics to start preparing customers for the quantum era. All you need is a working knowledge and a sense of urgency. According to Capgemini, nearly two-thirds (65 percent) of organizations see quantum computing as the most critical cybersecurity threat in the next three to five years. Organizations that fail to modernize their environments today risk being left vulnerable to nextgeneration attacks, and many are unaware of how quickly the threat landscape is changing. Here we’ll explore how quantum computing is transforming cybersecurity and provide a blue blueprint to help your customers prepare for the next generation of threats. What is Quantum Computing? Quantum computing is a new approach to computing that leverages the laws of quantum mechanics. Unlike traditional computers, which process binary digits – or zeros and ones – quantum computers manipulate quantum bits (qubits) which can exist as both zero and one at the same time through principles such as superpositioning and entanglement. Translation: quantum computers can perform multiple high compute tasks at once, making them exponentially faster and more powerful than classical computers. The nascent quantum industry is now attracting significant capital and investor confidence. McKinsey predicts that by 2035, quantum computing alone could be worth $72 billion, with the broader quantum technology (QT) space – such as quantum sensing and communication – on pace to reach almost $200 billion by 2040. “In 2024, the QT industry saw a shift from growing quantum bits (qubits) to stabilizing qubits – and that marks a turning point,” said McKinsey in its report. “It signals to mission-critical industries that QT could soon become a safe and reliable component of their technology infrastructure.” Looking beyond cybersecurity, companies will need guidance around a variety of areas in the coming years, from identifying and prioritizing quantum-relevant business problems to building pilot programs and developing quantum roadmaps. The Rising Cybersecurity Threat As the quantum industry builds momentum, concerns are mounting over its impact on cybersecurity. The main issue is that quantum computing poses a direct threat to leading encryption frameworks such as Rivest-Shamir-Adleman (RSA) and Elliptic Curve Cryptography (ECC), which organizations currently rely on to safeguard sensitive data. By Gerald Baldino A tech advisor’s guide to quantum computing and cybersecurity CYBER PATROL Quantum Leap 26 CHANNELVISION | FALL 2025
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